WO2025167443A1 - Communication method and related device - Google Patents

Abstract

A communication method and a related device. In the method, a first communication apparatus can deploy a first model, and, on the basis of indication by first information, perform measurement and/or provide feedback on task performance achieved by an output of the first model. Hence, the computing power of a communication node can process a model and a downstream task (and/or a downstream model) of the model, and can also perform measurement on performance and/or provide feedback on performance. In some embodiments, the first communication apparatus can, on the basis of the indication by the first information, provide feedback on the performance of M tasks corresponding to the first model, thus improving flexibility in solution implementation, and also indicating, in a scenario in which the first model is provided with a downstream task (and/or a downstream model), measurement and/or feedback on the performance of the downstream task (and/or the downstream model).

Description

A communication method and related equipment

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on February 7, 2024, with application number 202410175499.3 and application name “A communication method and related equipment”, the entire contents of which are incorporated by reference into this application.

Technical Field

The present application relates to the field of communications, and in particular to a communication method and related equipment.

Background Art

Wireless communication can be the transmission communication between two or more communication nodes without propagating through conductors or cables. The communication nodes generally include network devices and terminal devices.

Currently, in wireless communication systems, communication nodes generally possess both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., processing both sending and receiving signals), enabling communication between the network device and other communication nodes.

However, in communication networks, communication nodes may have excess computing power beyond just supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

Summary of the Invention

The present application provides a communication method and related equipment, which are used to enable communication nodes to process models and downstream tasks (and/or downstream models) of the models while also realizing performance measurement and/or performance feedback of downstream tasks (and/or downstream models) of the models.

In a first aspect, the present application provides a communication method, which is performed by a first communication device. The first communication device may be a communication device (such as a terminal device or a network device), or the first communication device may be a partial component in the communication device (such as a processor, a chip, a baseband chip, a modem chip, a system-on-chip (SoC) chip containing a modem core, a system-in-package (SIP) chip, a communication module, or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device receives first information, which is used to indicate M tasks, where M is a positive integer; wherein the input of any task in the M tasks is determined based on the output of a first model; and the first communication device sends second information, which is used to indicate the performance of one or more tasks in the M tasks.

Based on the above technical solution, after receiving the first information indicating M tasks, the first communication device can send second information and indicate the performance of one or more tasks in the M tasks through the second information. In other words, the first communication device can deploy the first model and, based on the indication of the first information, measure and/or provide feedback on the task performance achieved by the output of the first model. This allows the computing power of the communication node to process the model and its downstream tasks (and/or downstream models) while also achieving performance measurement and/or performance feedback.

In addition, among the M tasks indicated by the first information, the input of any task is determined based on the output of the first model, that is, the output of the first model can be used to execute one or more tasks. In the above technical solution, the first communication device can measure and/or provide feedback on the performance of the M tasks corresponding to the first model based on the indication of the first information. This can improve the flexibility of the solution implementation while also enabling the first model to provide an indication of performance measurement and/or feedback of the downstream task in scenarios where the first model has downstream tasks.

In this application, model may be used interchangeably with other terms, such as neural network model, neural network, artificial intelligence (AI) model, machine learning model, etc.

Optionally, the first information may be used to instruct to perform one or more of the following operations on the performance of the M tasks: measurement, testing, monitoring, evaluation, measurement, feedback, or reporting.

It should be understood that the input of any one of the M tasks is determined based on the output of the first model. It can be understood that the input of any one task at least includes the result obtained by the output of the first model through 0, 1 or more tasks.

Optionally, any of the M tasks may be implemented in a variety of ways, including signal processing, model processing, processing in other applications (APP), or other ways.

As an example, the output of the first model can be multiple path component information (MPC), and any of the M tasks can be model processing for channel-state information (CSI) acquisition, resource management or user scheduling applications, model processing for path loss prediction, network optimization applications, model processing for beam prediction, or beam management applications.

As another example, the output of the first model can be a channel frequency response (CFR), and any of the M tasks can be model processing for CSI acquisition, resource management or user scheduling applications, model processing for interference prediction, interference management applications, model processing for modulation and coding scheme (MCS) prediction, or adaptive modulation and coding applications.

Optionally, the performance of the task may include one or more of the accuracy, precision, and processing speed of the task (or the output of the task).

It should be noted that the second information may indicate the performance of one or more tasks in various ways.

For example, after the first communication device executes the one or more tasks locally and obtains the output of the one or more tasks, it can determine the performance of the one or more tasks based on the output of the one or more tasks, and the second information sent by the first communication device can include information for indicating or characterizing the performance of the one or more tasks.

For another example, after the first communication device can execute the one or more tasks locally and obtain the output of the one or more tasks, the second information sent by the first communication device can include the output of the one or more tasks; subsequently, the recipient of the second information can determine the performance of the one or more tasks based on the output of the one or more tasks.

In a possible implementation manner of the first aspect, the first communication device sends the second information, including: when the performance of one or more tasks in the M tasks is lower than or equal to a threshold, the first communication device sends the second information.

Based on the above technical solution, after receiving the first information indicating M tasks, the first communication device can obtain the performance corresponding to the M tasks, and when the performance of one or more tasks among the M tasks is lower than or equal to a threshold, send second information indicating the performance of the one or more tasks, so that the recipient of the second information can determine the task with degraded performance through the second information.

The second aspect of the present application provides a communication method, which is performed by a second communication device. The second communication device can be a communication device (such as a terminal device or a network device), or the second communication device can be a partial component in the communication device (such as a processor, chip, baseband chip, modem chip, system on chip (SoC) chip containing a modem core, system in package (SIP) chip, communication module, or chip system, etc.), or the second communication device can also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the second communication device sends first information, which is used to indicate M tasks, where M is a positive integer; wherein the input of any task in the M tasks is determined based on the output of the first model; and the second communication device receives second information, which is used to indicate the performance of one or more of the M tasks.

Based on the above technical solution, after the second communication device sends the first information indicating M tasks, the second communication device can receive the second information and determine the performance of one or more tasks in the M tasks through the second information. In other words, the first communication device can deploy the first model and, based on the indication of the first information, measure and/or provide feedback on the task performance achieved by the output of the first model. This allows the computing power of the communication node to process the model and its downstream tasks (and/or downstream models) while also achieving performance measurement and/or performance feedback.

In addition, among the M tasks indicated by the first information, the input of any task is determined based on the output of the first model, that is, the output of the first model can be used to execute one or more tasks. In the above technical solution, the first communication device can measure and/or provide feedback on the performance of the M tasks corresponding to the first model based on the indication of the first information. This can improve the flexibility of the solution implementation while also enabling the first model to provide an indication of performance measurement and/or feedback of the downstream task in scenarios where the first model has downstream tasks.

It should be understood that the input of any one of the M tasks is determined based on the output of the first model. It can be understood that the input of any one task at least includes the result obtained by the output of the first model through 0, 1 or more tasks.

In a possible implementation of the first aspect or the second aspect, the M tasks are included in N tasks, where N is an integer greater than or equal to M; the N tasks correspond to P task sets, each task set in the P task sets includes one or more tasks in the N tasks, and different task sets in the P task sets include different tasks; wherein, the input of any task in the i+1th task set in the P task sets includes the output of one or more tasks in the i-th task set in the P task sets, P is a positive integer, and i is 1 to P-1.

For example, taking P as greater than 2, among the P task sets, the input of one or more tasks included in the first task set includes at least the output of the first model, the input of one or more tasks included in the second task set includes at least the result obtained by processing the output of the first model through one or more tasks in the first task set... and so on, the input of one or more tasks included in the P-th task set includes at least the result obtained by processing the output of the first model through one or more tasks in the P-1-th task set. In other words, the P task sets can also be expressed as P-level task sets. For example, among the P-level task sets, the input of one or more tasks included in the first-level task set includes at least the output of the first model, the input of one or more tasks included in the second-level task set includes at least the result obtained by processing the output of the first model through one or more tasks in the first-level task set... and so on, the input of one or more tasks included in the P-th task set includes at least the result obtained by processing the output of the first model through one or more tasks in the P-1-th task set.

Optionally, different task sets can be understood as task sets of different levels. For example, P task sets can be expressed as P-level task sets, the i-th task set can be expressed as the i-th level task set, the i+1-th task set can be expressed as the i+1-th level task set, and so on.

Based on the above technical solution, among N tasks including M tasks, the input of any task is determined based on the output of the first model. The input of any task at least includes the result obtained by passing the output of the first model through 0, 1, or more tasks, that is, the N tasks may include a set of one or more downstream tasks of the first model. This enables the first information to provide an indication of performance measurement and/or feedback of the downstream tasks of the first model in a scenario where the first model has a set of one or more downstream tasks.

Optionally, the input of any task in the i+1th task set among the P task sets includes the output of one or more tasks in the i-th task set among the P task sets; it can be understood that the input of any task in the j-th task set among the P task sets includes the result obtained after the output of the first model has undergone j-1 processing processes, and the j-1 processing processes respectively include the processing of one or more tasks in each task set in the j-1 task sets before the j-th task set, and the value of j is 1 to P.

In a possible implementation of the first aspect or the second aspect, the first information includes M identifiers, which are respectively used to indicate the M tasks; any one of the M identifiers includes K indexes, wherein the kth index among the K indexes is used to indicate one or more tasks in the kth task set among the first K task sets in P task sets, and the value of k is 1 to K, and K is a positive integer less than or equal to P.

It should be understood that the M identifiers are used to indicate M tasks respectively. It can be understood that the M identifiers correspond one-to-one to the M tasks, and/or the mth identifier among the M identifiers is used to indicate the mth task among the M tasks, and m takes a value of 1 to M.

Based on the above technical solution, the first information received by the first communication device may include M identifiers respectively used to indicate the M tasks. Among the P task sets, the task indicated by any one of the identifiers may be represented as a task in the Kth (K is an integer less than or equal to P) task set among the P task sets. Moreover, any one of the M identifiers may include K indexes, so as to indicate one or more tasks contained in each of the K task sets through the K indexes.

Optionally, different task sets may be understood as task sets of different levels. For example, K task sets may be expressed as K-level task sets, and the k-th task set may be expressed as the k-th-level task set.

Optionally, among the K task sets, the indexes between different task sets (ie, the K indexes) may be continuous (eg, continuously increasing or continuously decreasing).

In a possible implementation manner of the first aspect or the second aspect, any identifier further includes an identifier of the first model.

Based on the above technical solution, the first communication device can deploy one or more first models. Accordingly, each first model may have downstream tasks. To this end, among the M identifiers used to indicate M tasks, any identifier can also include an identifier of the first model. In this way, the first information can indicate the downstream tasks corresponding to one or more first models.

In a possible implementation of the first aspect or the second aspect, the first information includes T indexes, the tasks indicated by the T indexes are the M tasks, the T indexes respectively indicate T task sets in the P task sets, and T is a positive integer less than or equal to P; the tth index in the T indexes is used to indicate 0 or one or more tasks contained in the tth task set in the T task sets, and t is a positive integer less than T.

Based on the above technical solution, the N tasks downstream of the first model can be included in P task sets, and correspondingly, M tasks in the N tasks can be included in T task sets in the P task sets. The first information for indicating the M tasks may include T indexes, and the T indexes are respectively used to indicate 0 or one or more tasks of each task set in the T task sets. In this way, the first information can indicate the M tasks through the tasks included in each task set in the T task sets.

Optionally, different task sets may be understood as task sets of different levels. For example, T task sets may be expressed as a T-level task set, and the t-th task set may be expressed as a t-th level task set.

Optionally, T is less than or equal to P, that is, the T task sets are part or all of the P task sets. Accordingly, among the T task sets, the indexes between different task sets (i.e., the T indexes) can be continuous (e.g., continuously increasing or continuously decreasing) or discontinuous, which is not limited here.

In a possible implementation manner of the first aspect or the second aspect, the first information further includes an identifier of the first model.

Based on the above technical solution, in addition to T indexes, the first information may also include an identifier of the first model. The first communication device may deploy one or more first models, and accordingly, each first model may have downstream tasks. To this end, in the first information indicating M tasks, the first information may also include an identifier of the first model. In this way, the first information can indicate downstream tasks corresponding to one or more first models.

In a possible implementation of the first aspect or the second aspect, the T indexes satisfy at least one of the following: in the tth index among the T indexes, the value of the first bit is used to indicate whether the first information includes the t+xth index, and the value of x is 1 to T-t; or, in the tth index among the T indexes, when the value of the tth index is a preset value, the tth index is used to indicate the tasks (or all tasks) included in the tth task set.

Exemplarily, the value of the t-th index is a preset value, which can be understood as, among the multiple bits contained in the t-th index, the values of the other bits except the first bit are preset values (for example, all 0s or all 1s, etc.); or, among the multiple bits contained in the t-th index, the values of the multiple bits are preset values.

Based on the above technical solution, in the tth index among the T indexes, the value of the first bit is used to indicate whether the first information includes the t+xth index, so that the first communication device can determine whether it is necessary to parse the t+xth index based on the value of the first bit in the tth index, which can reduce the implementation complexity and avoid unnecessary overhead.

In addition, in the t-th index of the T indexes, when the value of the t-th index is a preset value, the t-th index is used to indicate the tasks included in the t-th task set. In this way, the special value of an identifier can be used to indicate one or more tasks corresponding to the identifier, thereby reducing overhead.

In a possible implementation manner of the first aspect or the second aspect, the first information includes M identifiers, and the M identifiers are respectively used to indicate the M tasks; among the M identifiers, lengths of different identifiers are the same.

Based on the above technical solution, the first information can indicate M tasks respectively through M equal-length identifiers, that is, different tasks can be indicated by sequences of equal length. In this way, the implementation complexity can be reduced.

In a possible implementation manner of the first aspect or the second aspect, the second information is further used to indicate one or more tasks among the M tasks.

It should be understood that the second information may indicate one or more tasks in the M tasks in a manner similar to the manner in which the first information indicates one or more tasks in the M tasks. For example, the second information may include M identifiers, T indexes, and the like.

Based on the above technical solution, the second information can be used to indicate the performance of one or more tasks in the M tasks. Correspondingly, the second information can also be used to indicate the one or more tasks. In this way, the recipient of the second information can determine the one or more tasks in the M tasks based on the second information and clearly understand that the performance indicated by the second information is the performance of the one or more tasks in the M tasks.

A third aspect of the present application provides a communication method, which is performed by a first communication device. The first communication device can be a communication device (such as a terminal device or a network device), or the first communication device can be a partial component in the communication device (such as a processor, chip, baseband chip, modem chip, system on chip (SoC) chip containing a modem core, system in package (SIP) chip, communication module, or chip system, etc.), or the first communication device can also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device receives third information, and the third information is used to indicate M models, where M is a positive integer; wherein any model of the M models is determined based on the second model; and the first communication device sends fourth information, and the fourth information is used to indicate the performance of one or more models of the M models.

Based on the above technical solution, after the first communication device receives the first information indicating M models, the first communication device can send fourth information and indicate the performance of one or more models in the M models through the fourth information. In other words, the first communication device can deploy the second model and M models obtained based on the second model, and measure and/or feedback the performance of the M models based on the indication of the first information. Thereby, the computing power of the communication node can process the model and the downstream tasks (and/or downstream models) of the model while also achieving performance measurement and/or performance feedback.

In addition, among the M models indicated by the first information, any one of the models is determined based on the second model, that is, the second model can be processed once or multiple times to generate any one of the M models. In the above technical solution, the first communication device can measure and/or provide feedback on the performance of the M models corresponding to the second model based on the indication of the first information, which can improve the flexibility of the solution implementation and also enable the performance measurement and/or feedback of the downstream model to be indicated in the scenario where the second model has a downstream model.

It should be understood that any one of the M models is determined based on the second model, which can be understood as any one of the M models being obtained based on the second model through one or more model processings. Any of the one or more model processings can be model fine-tuning, model distillation, model pruning, model compression, model fusion, or other model processing.

Optionally, the performance of the model may include one or more of the accuracy, precision, and processing speed of the model (or the output of the model).

It should be noted that the fourth information may indicate the performance of one or more models in various ways.

For example, the first communication device may determine the performance of the one or more models based on the output of the one or more models after locally executing the one or more models and obtaining the output of the one or more models, and the fourth information sent by the first communication device may include information for indicating or characterizing the performance of the one or more models.

For another example, after the first communication device can locally execute the one or more models and obtain the output of the one or more models, the fourth information sent by the first communication device can include the output of the one or more models; subsequently, the recipient of the second information can determine the performance of the one or more models based on the output of the one or more models.

In a possible implementation manner of the third aspect, the first communication device sends the fourth information, including: when the performance of one or more models in the M models is lower than or equal to a threshold, the first communication device sends the fourth information.

Based on the above technical solution, after receiving the third information indicating M models, the first communication device can obtain the performance corresponding to the M models, and when the performance of one or more models among the M models is lower than or equal to a threshold, send fourth information indicating the performance of the one or more models, so that the recipient of the fourth information can determine the model with degraded performance through the fourth information.

In a fourth aspect, the present application provides a communication method, which is performed by a second communication device. The second communication device may be a communication device (such as a terminal device or a network device), or the second communication device may be a partial component in the communication device (such as a processor, a chip, a baseband chip, a modem chip, a system-on-chip (SoC) chip containing a modem core, a system-in-package (SIP) chip, a communication module, or a chip system, etc.), or the second communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the second communication device sends third information, which is used to indicate M models, where M is a positive integer; wherein any model of the M models is determined based on the second model; and the second communication device receives fourth information, which is used to indicate the performance of one or more models of the M models.

Based on the above technical solution, after the second communication device sends the first information for indicating M models, the second communication device can receive fourth information and determine the performance of one or more models in the M models through the fourth information. In other words, the first communication device can deploy the second model and M models obtained based on the second model, and measure and/or feedback the performance of the M models based on the indication of the first information. Thereby, the computing power of the communication node can process the model and the downstream tasks (and/or downstream models) of the model while also being able to achieve performance measurement and/or performance feedback, and achieve performance feedback.

In addition, among the M models indicated by the first information, any one of the models is determined based on the second model, that is, the second model can be processed once or multiple times to generate any one of the M models. In the above technical solution, the first communication device can measure and/or provide feedback on the performance of the M models corresponding to the second model based on the indication of the first information, which can improve the flexibility of the solution implementation and also enable the performance measurement and/or feedback of the downstream model to be indicated in the scenario where the second model has a downstream model.

In a possible implementation of the third aspect or the fourth aspect, the M models are included in N models, where N is an integer greater than or equal to M; the N models correspond to P model sets, each model set in the P model sets includes one or more models in the N models, and different model sets in the P model sets contain different models; wherein, any model in the i+1th model set in the P model sets is determined based on one or more models in the i-th model set in the P model sets, P is a positive integer, and i is 1 to P-1.

Exemplarily, taking P greater than 2 as an example, among the P model sets, any one of the one or more models included in the first model set is obtained by model processing based on the second model, and any one of the one or more models included in the second model set is obtained by model processing based on one or more models in the first model set... and so on, any one of the one or more models included in the P-th model set is obtained by model processing based on one or more models in the P-1-th model set. In other words, the P model sets can also be expressed as P-level model sets. For example, among the P-level model sets, any one of the one or more models included in the first-level model set is obtained by model processing based on the second model, and any one of the one or more models included in the second-level model set is obtained by model processing based on one or more models in the first-level model set... and so on, any one of the one or more models included in the P-th level model set is obtained by model processing based on one or more models in the P-1-th level model set.

Optionally, different model sets can be understood as model sets of different levels. For example, P model sets can be expressed as P-level model sets, the i-th model set can be expressed as the i-th level model set, the i+1-th model set can be expressed as the i+1-th level model set, and so on.

Based on the above technical solution, among N models including M models, any one model is determined based on the second model. The any one model can be obtained by processing the second model zero times, one time, or multiple times. That is, the N models can include a set of one or more downstream models of the second model. This allows the first information to provide an indication of performance measurement and/or feedback for downstream models of the second model in a scenario where the second model has a set of one or more downstream models.

Optionally, any model in the i+1th model set among the P model sets is determined based on one or more models in the i-th model set among the P model sets; it can be understood that any model in the p-th model set among the P model sets is obtained based on the second model after p-1 processing processes, and the p-1 processing processes include model processing corresponding to one or more models in each model set in the p-1 model sets before the p-th model set, and the value of p is 1 to P.

In a possible implementation of the third aspect or the fourth aspect, the third information includes M identifiers, which are respectively used to indicate the M models; any one of the M identifiers includes K indexes; wherein the kth index among the K indexes is used to indicate the model processing corresponding to one or more models in the kth model set in the first K model sets in the P model sets, and k ranges from 1 to K, where K is a positive integer less than or equal to P.

It should be understood that the M identifiers are used to indicate M models respectively. It can be understood that the M identifiers correspond one-to-one to the M models, and/or the mth identifier among the M identifiers is used to indicate the mth model among the M models, and m takes a value of 1 to M.

Based on the above technical solution, the first information received by the first communication device may include M identifiers respectively used to indicate the M models. Among the P model sets, the model indicated by any one of the identifiers may be represented as a model in the Kth (K is an integer less than or equal to P) model set among the P model sets. Furthermore, any one of the M identifiers may include K indexes, so as to indicate the model processing corresponding to the model through the K indexes.

Optionally, different model sets can be understood as model sets of different levels. For example, K model sets can be expressed as a K-level model set, and the k-th model set can be expressed as a k-th level model set.

Optionally, among the K model sets, the indexes between different model sets (ie, the K indexes) may be continuous (eg, continuously increasing or continuously decreasing).

In a possible implementation manner of the third aspect or the fourth aspect, the any identifier further includes an identifier of the processing of the second model.

Based on the above technical solution, the first communication device can deploy one or more second models. Accordingly, each second model may have a downstream model. To this end, among the M identifiers used to indicate the M models, any identifier can also include the identifier of the second model. In this way, the first information can indicate the downstream models corresponding to one or more second models.

In a possible implementation of the third aspect or the fourth aspect, the third information includes T indexes, the models indicated by the T indexes are the M models, and the T indexes are respectively used to indicate the T model sets in the P model sets, where T is a positive integer less than or equal to P; the tth index in the T indexes is used to indicate 0 or one or more models contained in the tth model set in the T model sets, where t is a positive integer less than T.

Based on the above technical solution, the N models downstream of the second model can be included in P model sets, and accordingly, M models in the N models can be included in T model sets in the P model sets. The first information used to indicate the M models can include T indexes, and the T indexes are respectively used to indicate 0 or one or more models of each model set in the T model sets. In this way, the first information can indicate the M models through the models included in each model set in the T model sets.

Optionally, different model sets can be understood as model sets of different levels. For example, T model sets can be expressed as a T-level model set, and the t-th model set can be expressed as a t-th level model set.

Optionally, T is less than or equal to P, that is, the T model sets are part or all of the P model sets. Accordingly, among the T model sets, the indexes between different model sets (i.e., the T indexes) can be continuous (e.g., continuously increasing or continuously decreasing) or discontinuous, which is not limited here.

In a possible implementation manner of the third aspect or the fourth aspect, the third information further includes an identifier of the processing of the second model.

Based on the above technical solution, in addition to including T indexes, the third information may also include an identifier of the second model. The first communication device may deploy one or more second models, and accordingly, each second model may have a downstream model. To this end, in the third information indicating M models, the third information may also include an identifier of the second model. In this way, the first information can indicate the downstream models corresponding to one or more second models.

In a possible implementation of the third aspect or the fourth aspect, the T indexes satisfy at least one of the following: in the tth index among the T indexes, the value of the first bit is used to indicate whether the third information includes the t+xth index, and the value of x is 1 to T-t; or, in the tth index among the T indexes, when the value of the tth index is a preset value, the tth index is used to indicate the model (or all models) included in the tth model set.

Exemplarily, the value of the t-th index is a preset value, which can be understood as, among the multiple bits contained in the t-th index, the values of the other bits except the first bit are preset values (for example, all 0s or all 1s, etc.); or, among the multiple bits contained in the t-th index, the values of the multiple bits are preset values.

Based on the above technical solution, in the tth index among the T indexes, the value of the first bit is used to indicate whether the first information includes the t+xth index, so that the first communication device can determine whether it is necessary to parse the t+xth index based on the value of the first bit in the tth index, which can reduce the implementation complexity and avoid unnecessary overhead.

In addition, in the t-th index of the T indexes, when the value of the t-th index is a preset value, the t-th index is used to indicate the models included in the t-th model set. In this way, one or more models corresponding to an identifier can be indicated by a special value of the identifier, thereby reducing overhead.

In a possible implementation manner of the third aspect or the fourth aspect, the third information includes M identifiers, and the M identifiers are respectively used to indicate the M models; among the M identifiers, the lengths of different identifiers are the same.

Based on the above technical solution, the third information can indicate M models respectively through M equal-length identifiers, that is, different models can be indicated by sequences of equal length. In this way, the implementation complexity can be reduced.

In a possible implementation manner of the third aspect or the fourth aspect, the fourth information is further used to indicate an identifier of one or more models among the M models.

Based on the above technical solution, the fourth information can be used to indicate the performance of one or more models in the M models. Correspondingly, the fourth information can also be used to indicate the one or more models. In this way, the recipient of the fourth information can determine the one or more models in the M models based on the fourth information and clearly understand that the performance indicated by the fourth information is the performance of the one or more models in the M models.

It should be understood that the manner in which the fourth information indicates one or more models among the M models may refer to the manner in which the third information indicates one or more models among the M models. For example, the fourth information may include M identifiers, T indexes, and the like.

In a fifth aspect, the present application provides a communication device, which is a first communication device and includes a transceiver unit and a processing unit; the transceiver unit is used to receive first information, and the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any one of the M tasks is determined based on the output of the first model; the processing unit is used to determine second information; the transceiver unit is also used to send second information, and the second information is used to indicate the performance of one or more tasks among the M tasks.

In the fifth aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

In a sixth aspect, the present application provides a communication device, which is a second communication device and includes a transceiver unit and a processing unit; the processing unit is used to determine first information; the transceiver unit is used to send first information, and the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any one of the M tasks is determined based on the output of the first model; the transceiver unit is also used to receive second information, and the second information is used to indicate the performance of one or more of the M tasks.

In the sixth aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

In the seventh aspect of the present application, a communication device is provided, which is a first communication device and includes a transceiver unit and a processing unit; the transceiver unit is used to receive third information, and the third information is used to indicate M models, where M is a positive integer; wherein any one of the M models is determined based on the second model; the processing unit is used to determine fourth information; the transceiver unit is also used to send fourth information, and the fourth information is used to indicate the performance of one or more models among the M models.

In the seventh aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the third aspect and achieve corresponding technical effects. For details, please refer to the third aspect and will not be repeated here.

In an eighth aspect of the present application, a communication device is provided, which is a second communication device, and includes a transceiver unit and a processing unit; the processing unit is used to determine third information, and the transceiver unit is used to send third information, and the third information is used to indicate M models, where M is a positive integer; wherein any one of the M models is determined based on the second model; the transceiver unit is also used to receive fourth information, and the fourth information is used to indicate the performance of one or more models of the M models.

In the eighth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the fourth aspect and achieve corresponding technical effects. For details, please refer to the fourth aspect and will not be repeated here.

In a ninth aspect, the present application provides a communication device, comprising at least one processor coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method described in any possible implementation method of any one of the first to fourth aspects.

In a tenth aspect, the present application provides a communication device comprising at least one logic circuit and an input/output interface; the logic circuit is used to execute the method described in any possible implementation of any one of the first to fourth aspects.

In an eleventh aspect, the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

A twelfth aspect of the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of any aspect of the first to fourth aspects above.

The thirteenth aspect of the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to fourth aspects above.

A fourteenth aspect of the present application provides a chip or chip system, which includes at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to fourth aspects above.

In one possible design, the chip or chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip or may include a chip and other discrete components. Optionally, the chip system also includes an interface circuit that provides program instructions and/or data to the at least one processor.

Among them, the technical effects brought about by any design method in the fifth to fourteenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to fourth aspects, and will not be repeated here.

BRIEF DESCRIPTION OF THE DRAWINGS

Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

Figures 2a to 2e are schematic diagrams of the AI processing process involved in this application;

FIG3 is an interactive schematic diagram of the communication method provided by this application;

Figures 4a and 4b are schematic diagrams of the relationship between the model and tasks provided in this application;

FIG5 is an interactive diagram of the communication method provided by this application;

Figures 6a and 6b are schematic diagrams of the relationship between different models provided in this application;

7 to 11 are schematic diagrams of the communication device provided in this application.

DETAILED DESCRIPTION

First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

(1) Terminal device: It can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and/or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (also known as "cellular" phones, mobile phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile devices that exchange voice and/or data with the radio access network. For example, personal communication service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers, computers with wireless transceiver capabilities, and other devices. Wireless terminal equipment can also be called system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal equipment (remote terminal), access terminal equipment (access terminal), user terminal equipment (user terminal), user agent (user agent), subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

As an example and not a limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

The terminal can also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.

Furthermore, the terminal device may also be a terminal device in a future communication system after the fifth-generation (5G) communication system (e.g., a sixth-generation (6G) communication system) or a terminal device in a future public land mobile network (PLMN). For example, a 6G network may further extend the form and functionality of a 5G communication terminal. 6G terminals include, but are not limited to, vehicles, cellular network terminals (with integrated satellite terminal functionality), drones, and Internet of Things (IoT) devices.

In an embodiment of the present application, the terminal device may also obtain AI services provided by the network device. Optionally, the terminal device may also have AI processing capabilities.

(2) Network equipment: It can be a device in a wireless network. For example, a network device can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmit/receive point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), baseband unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc. In addition, in a network structure, the network device may include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

Alternatively, a RAN node can be a macro base station, micro base station, indoor base station, relay node, donor node, or wireless controller in a cloud radio access network (CRAN) scenario. A RAN node can also be a server, wearable device, vehicle, or onboard device. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

In another possible scenario, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes implementing portions of the base station's functionality. For example, a RAN node can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be separate or included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

In different systems, CU (or CU-CP and CU-UP), DU or RU may have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, this application uses CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit among the CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

Communication between access network equipment and terminal devices follows a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer.

For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

Table 1

The network device may be any other device that provides wireless communication functionality to the terminal device. The embodiments of this application do not limit the specific technology and device form used by the network device. For ease of description, the embodiments of this application do not limit this.

The network equipment may also include core network equipment, such as the mobility management entity (MME), home subscriber server (HSS), serving gateway (S-GW), policy and charging rules function (PCRF), and public data network gateway (PDN gateway, P-GW) in the fourth generation (4G) network; and the access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in the 5G network. In addition, the core network equipment may also include other core network equipment in the 5G network and the next generation network of the 5G network.

In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it can be an AI node on the network side (access network or core network), a computing power node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

In the embodiments of the present application, the apparatus for implementing the function of the network device may be the network device, or may be a device capable of supporting the network device in implementing the function, such as a chip system, which may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application are described by taking the network device as an example.

(3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used simultaneously. Configuration refers to the network device/server sending some parameter configuration information or parameter values to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values pre-negotiated between the network device/server and the terminal device, or parameter information or parameter values used by the base station/network device or terminal device as specified in the standard protocol, or parameter information or parameter values pre-stored in the base station/server or terminal device. This application does not limit this.

Furthermore, these values and parameters can be changed or updated.

(4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" refers to two or more. "And/or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and/or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character "/" generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. In addition, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

(5) “Sending” and “receiving” in the embodiments of the present application indicate the direction of signal transmission. For example, “sending information to XX” can be understood as the destination of the information being XX, which can include direct sending through the air interface, as well as indirect sending through the air interface by other units or modules. “Receiving information from YY” can be understood as the source of the information being YY, which can include direct receiving from YY through the air interface, as well as indirect receiving from YY through the air interface from other units or modules. “Sending” can also be understood as the “output” of the chip interface, and “receiving” can also be understood as the “input” of the chip interface.

In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

(6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be achieved by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, and the various methods/designs/implementations in each embodiment, if there is no special explanation and logical conflict, the terms and/or descriptions between different embodiments and the various methods/designs/implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods/designs/implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The following description of the implementation methods of this application does not constitute a limitation on the scope of protection of this application.

This application can be applied to a long-term evolution (LTE) system, a new radio (NR) system, or a future communication system after 5G (e.g., 6G). The communication system includes at least one network device and/or at least one terminal device.

Please refer to Figure 1a, which is a schematic diagram of a communication system in this application. Figure 1a exemplarily illustrates a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

As shown in Figure 1a, the AI configuration information sending entity can be a network device. The AI configuration information receiving entity can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 serves as a network device, i.e., the AI configuration information sending entity; terminal devices 4 and 6 serve as terminal devices, i.e., the AI configuration information receiving entities. For example, in a connected vehicle system, terminal device 5 sends AI configuration information to terminal devices 4 and 6, respectively, and receives data from them. Correspondingly, terminal devices 4 and 6 receive AI configuration information from terminal device 5 and send data to terminal device 5.

Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices, between network devices and terminal devices, and/or between terminal devices) may also execute AI-related services.

As shown in Figure 1b, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

As shown in Figure 1c, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

The technical solution provided in this application can be applied to wireless communication systems (such as the systems shown in Figures 1a, 1b, or 1c). For example, an AI network element can be introduced into the communication system provided in this application to implement some or all AI-related operations. The AI network element can also be called an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. The AI network element can be a network element built into the communication system. For example, the AI network element can be an AI module built into: an access network device, a core network device, a cloud server, or a network management (OAM) to implement AI-related functions. The OAM can be a network management for a core network device and/or a network management for an access network device. Alternatively, the AI network element can also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal can also include an AI entity to implement AI-related functions.

The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

Artificial intelligence (AI) can imbue machines with human intelligence. For example, it can enable machines to simulate certain intelligent human behaviors using computer hardware and software. Machine learning methods can be used to implement AI. In machine learning, a machine uses training data to learn (or train) a model. This model represents the mapping from input to output. The learned model can be used for inference (or prediction), meaning that the model can be used to predict the output corresponding to a given input. This output can also be called an inference result (or prediction result).

Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

Supervised learning uses machine learning algorithms to learn the mapping relationship between sample values and sample labels based on collected sample values and sample labels, and then expresses this learned mapping relationship using an AI model. The process of training a machine learning model is the process of learning this mapping relationship. During training, sample values are input into the model to obtain the model's predicted values. The model parameters are optimized by calculating the error between the model's predicted values and the sample labels (ideal values). Once the mapping relationship is learned, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mappings or nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

Unsupervised learning uses algorithms to discover inherent patterns in collected sample values. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal (e.g., optimal) decision-making actions. However, because the labels for "correct actions" cannot be obtained in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

A neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, NNs can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, deep learning communication systems based on neural networks can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

The idea of a neural network is derived from the neuronal structure of the brain. For example, each neuron performs a weighted sum operation on its input values and outputs the result through an activation function.

As shown in Figure 2a, it is a schematic diagram of a neuron structure. Assume that the input of the neuron is x = [x ₀ , x ₁ ,…, x _n ], and the weights corresponding to each input are w = [w ₀ , w ₁ ,…, w _n ], where n is a positive integer, and w _i and _xi can be various possible types such as real numbers, integers (such as 0, positive integers or negative integers, etc.), or complex numbers. w _i is used as the weight of _xi to weight _xi . The bias for weighted summation of input values according to the weights is, for example, b. There can be many forms of activation functions. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), the output of the neuron is: For another example, if the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a real number, an integer (eg, 0, a positive integer, or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

Furthermore, neural networks generally include multiple layers, each of which may include one or more neurons. Increasing the depth and/or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it comprises, and the number of neurons in each layer can be referred to as the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, a hidden layer, and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to an intermediate hidden layer. The hidden layer performs calculations on the received processing results to obtain a calculation result, which is then passed to the output layer or the next adjacent hidden layer, which ultimately obtains the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected in sequence, without limitation.

An example of a neural network is a deep neural network (DNN). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Figure 2b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are fully connected. This characteristic typically requires a large amount of storage space and results in high computational complexity.

CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

During the machine learning model training process, a loss function can be defined. This function describes the gap or discrepancy between the model's output and the ideal target value. Loss functions can be expressed in various forms, and there are no restrictions on their specific form. The model training process can be viewed as adjusting some or all of the model's parameters to keep the loss function below a threshold or meet the target.

A model may also be referred to as an AI model, rule, or other name. An AI model can be considered a specific method for implementing an AI function. An AI model represents a mapping relationship or function between the input and output of a model. AI functions may include one or more of the following: data collection, model training (or model learning), model information release, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or inference result release, etc. AI functions may also be referred to as AI (related) operations, or AI-related functions.

The following is an illustrative description of the implementation process of a fully connected neural network, also known as a multilayer perceptron (MLP), with reference to the accompanying figures.

As shown in Figure 2c, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (center). Each layer of the MLP contains several nodes, called neurons. Neurons in adjacent layers are connected to each other.

Alternatively, considering neurons in two adjacent layers, the output h of the neurons in the next layer is the weighted sum of all neurons x in the previous layer connected to it and passes through the activation function, which can be expressed as:
h＝f(wx+b).

Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

Alternatively, the output of the neural network can be recursively expressed as:
y=f _n (w _n f _n-1 (…)+b _n ).

Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly, and the process of obtaining this mapping from random w and b using existing data is called neural network training.

Optionally, the specific training method is to use a loss function to evaluate the output results of the neural network.

As shown in Figure 2d, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches a minimum, which is the "better point (e.g., optimal point)" in Figure 2d. It is understood that the neural network parameters corresponding to the "better point (e.g., optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.

Alternatively, the gradient descent process can be expressed as:

Among them, θ is the parameter to be optimized (including w and b), L is the loss function, and η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, represents the derivative of θ with respect to L.

Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

As shown in Figure 2e, the gradient of the previous layer parameters can be recursively calculated from the gradient of the next layer parameters, which can be expressed as:

Among them, _wij is the weight of node j connecting to node i, and _si is the weighted sum of the inputs on node i.

The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a, 1b, or 1c). In wireless communication systems, communication nodes generally have both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., performing signal transmission and reception processing) to enable communication between the network device and other communication nodes.

However, in communication networks, communication nodes may have excess computing power beyond just supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

In order to solve the above problems, the present application provides a communication method and related equipment, which will be described in detail below with reference to the accompanying drawings.

Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

It should be noted that, in the following, FIG3 and FIG5 take the first communication device and the second communication device as the execution subject of the interaction diagram as an example to illustrate the method, but the present application does not limit the execution subject of the interaction diagram. For example, the first communication device can be a communication device (such as a terminal device or a network device), or a chip, a baseband chip, a modem chip, a system on chip (SoC) chip containing a modem core, a system in package (SIP) chip, a communication module, a chip system, a processor, a logic module or software, etc. in the communication device. Similarly, the second communication device can be a communication device (such as a terminal device or a network device), or a chip, a chip system, a processor, a logic module or software, etc. in the communication device.

S301. A second communication device sends first information, and the first communication device receives the first information. The first information indicates M tasks, where M is a positive integer. The input of each of the M tasks is determined based on the output of the first model.

S302: The first communication device sends second information, and correspondingly, the second communication device receives the second information, wherein the second information is used to indicate the performance of one or more tasks in the M tasks.

In this application, model may be used interchangeably with other terms, such as neural network model, neural network, artificial intelligence (AI) model, machine learning model, etc.

Optionally, the first information received by the first communication device in step S301 may be used to instruct one or more of the following operations to be performed on the performance of the M tasks: measurement, testing, monitoring, evaluation, measurement, feedback, or reporting.

It should be understood that the input of any of the M tasks is determined based on the output of the first model. This means that the input of any task at least includes the result of passing the output of the first model through zero, one, or more tasks. The input of any of the M tasks can be derived based on the output of the first model. Therefore, the M tasks can be referred to as downstream tasks of the first model.

Optionally, the output of the first model can be used to determine the input of one or more downstream tasks.

For ease of understanding, the relationship between the first model and the M tasks will be exemplarily explained below with reference to the example shown in FIG4 a and taking the value of M as 8.

In the example shown in FIG4a , the input of any task among Task 1, Task 2, Task 3 and Task 4 may include the output of the first model, that is, the input of any task among these four tasks may include the result obtained by the output of the first model after 0 tasks.

In the example shown in FIG4 a , the input of task 5 may include the output of task 1 , that is, the input of task 5 may include the result obtained by the output of the first model through a task in a task set (ie, task 1 ).

In the example shown in FIG4a , the input of task 6 or task 7 may include the output of task 2, that is, the input of task 6 or task 7 may include the result obtained by the output of the first model through a task in a task set (that is, task 2).

In the example shown in FIG4a , the input of task 8 may include the output of task 5, that is, the input of task 8 may include the result obtained by the output of the first model through the tasks in the two task sets (ie, task 1 and task 5).

It should be noted that the value of M can be a positive integer. When M is other than a positive integer, the relationship between the M tasks and the first model can refer to the example shown in Figure 4a. That is, among the M tasks, the input of any task at least includes the output of the first model obtained by passing it through zero, one, or more tasks.

Exemplarily, the first model may be a wireless pre-trained model, a pre-trained model, or a wireless large model, etc.

Optionally, the input of the first model can be implemented in a variety of ways, such as environmental parameters collected by the first communication device, communication signals received by the first communication device from other communication devices (such as the second communication device), and one or more of the information pre-configured locally by the first communication device.

Optionally, any of the M tasks may be implemented in a variety of ways, including signal processing, model processing, processing in other applications (APP), or other ways.

As an example, the output of the first model can be multipath component information (MPC), and any of the M tasks can be model processing for channel state information CSI acquisition, resource management or user scheduling applications, model processing for path loss prediction, network optimization applications, model processing for beam prediction, or beam management applications.

As another example, the output of the first model can be a channel frequency response (CFR), and any of the M tasks can be model processing for CSI acquisition, resource management or user scheduling applications, model processing for interference prediction, interference management applications, model processing for modulation and coding scheme (MCS) prediction, or adaptive modulation and coding applications.

Optionally, the performance of the task may include one or more of the accuracy, precision, and processing speed of the task (or the output of the task).

It should be noted that the second information may indicate the performance of one or more tasks in various ways.

For example, after the first communication device executes the one or more tasks locally and obtains the output of the one or more tasks, it can determine the performance of the one or more tasks based on the output of the one or more tasks, and the second information sent by the first communication device can include information for indicating or characterizing the performance of the one or more tasks.

For another example, after the first communication device can execute the one or more tasks locally and obtain the output of the one or more tasks, the second information sent by the first communication device can include the output of the one or more tasks; subsequently, the recipient of the second information can determine the performance of the one or more tasks based on the output of the one or more tasks.

In one possible implementation, the M tasks indicated by the first information sent by the second communication device in step S301 are included in N tasks, where N is an integer greater than or equal to M; the N tasks correspond to P task sets, each task set in the P task sets includes one or more tasks in the N tasks, and different task sets in the P task sets contain different tasks; wherein, the input of any task in the i+1th task set in the P task sets includes the output of one or more tasks in the i-th task set in the P task sets, P is a positive integer, and i is 1 to P-1.

Specifically, among N tasks comprising M tasks, the input of any task is determined based on the output of the first model. The input of any task includes at least the result obtained by passing the output of the first model through zero, one, or more tasks, that is, the N tasks may include a set of one or more downstream tasks of the first model. This enables the first information to provide an indication of performance measurement and/or feedback for the downstream tasks of the first model in a scenario where the first model has a set of one or more downstream tasks.

For example, taking P as greater than 2, among the P task sets, the input of one or more tasks included in the first task set includes at least the output of the first model, the input of one or more tasks included in the second task set includes at least the result obtained by processing the output of the first model through one or more tasks in the first task set... and so on, the input of one or more tasks included in the P-th task set includes at least the result obtained by processing the output of the first model through one or more tasks in the P-1-th task set. In other words, the P task sets can also be expressed as P-level task sets. For example, among the P-level task sets, the input of one or more tasks included in the first-level task set includes at least the output of the first model, the input of one or more tasks included in the second-level task set includes at least the result obtained by processing the output of the first model through one or more tasks in the first-level task set... and so on, the input of one or more tasks included in the P-th task set includes at least the result obtained by processing the output of the first model through one or more tasks in the P-1-th task set.

For ease of understanding, the relationship between the first model and the M tasks will be exemplarily explained below with reference to the example shown in FIG. 4 b , taking the value of N as 11 and the value of M as 8 as an example.

In the example shown in FIG4 b , the implementation of tasks 1 to 8 may refer to FIG4 a and related descriptions above.

In the example shown in Figure 4b, the input of task 9 or task 10 may include the output of task 8, that is, the input of task 9 or task 10 may include the output of the first model obtained through one or more of the outputs contained in each of the first three task sets (that is, task 1, task 5, and task 8).

In the example shown in Figure 4b, the input of task 11 may include the output of tasks 9 and 10, that is, the input of task 11 may include the output of the first model after 5 tasks (i.e., task 1, task 5, task 8, task 9 and task 10).

Furthermore, in the example shown in FIG. 4 b , 10 (N=11) tasks may correspond to 5 (P=5) task sets.

In the first task set, the input of any task includes the output of the first model (i.e., the output of the first model after passing it through task 0). For example, in the first task set, the input of any task among Task 1, Task 2, Task 3, and Task 4 includes the output of the first model.

When i=1 and i+1=2, in the second task set, the input of any task includes the result obtained by processing the output of the first model through one or more tasks in the previous task set (i.e., the output of one or more tasks in the first task set). For example, in the second task set, the input of task 5 includes the result obtained by processing the output of the first model through task 1, i.e., the input of task 5 may include the result obtained by processing the output of the first model through a task in the task set (i.e., task 1); the input of task 6 or task 7 may include the output of task 2, i.e., the input of task 6 or task 7 may include the result obtained by processing the output of the first model through a task (i.e., task 2).

When i=2 and i+1=3, in the third task set, the input of any task includes the result obtained by processing the output of the first model through one or more tasks in the first two task sets (i.e., the output of one or more tasks in the second task set). For example, in the third task set, the input of Task 8 includes the result obtained by processing the output of the first model through Task 1 and Task 5. That is, the input of Task 8 can include the result obtained by processing the output of the first model through tasks in two task sets (i.e., Task 1 and Task 5).

When i=3 and i+1=4, in the fourth task set, the input of any task includes the result obtained by passing the output of the first model through the first three tasks (i.e., the output of one or more tasks in the third task set). For example, in the fourth task set, the input of task 9 or task 10 includes the result obtained by passing the output of the first model through tasks 1, 5, and 8. That is, the input of task 9 or task 10 can include the result obtained by passing the output of the first model through the tasks in the three task sets (i.e., task 1, task 5, and task 8).

When i=4 and i+1=5, in the fifth task set, the input of any task includes the result obtained by passing the output of the first model through the first four tasks (i.e., the output of one or more tasks in the fourth task set). For example, in the fifth task set, the input of task 11 includes the result obtained by passing the output of the first model through tasks 1, 5, 8, 9, and 10. That is, the input of task 11 may include the result obtained by passing the output of the first model through the tasks in the four task sets (i.e., task 1, 5, 8, 9, and 10).

Optionally, the input of any task in the i+1th task set among the P task sets includes the output of one or more tasks in the i-th task set among the P task sets; it can be understood that the input of any task in the j-th task set among the P task sets includes the result obtained after the output of the first model has undergone j-1 processing processes, and the j-1 processing processes respectively include the processing of one or more tasks in each task set in the j-1 task sets before the j-th task set, and the value of j is 1 to P.

For example, in the example shown in FIG4b, for task 8 in the 3rd (j=3) task set, the output of the first model is processed 2 (j-1=2) times, and the two processing processes are respectively included in the processing of one or more tasks in each of the 2 (j-1=2) task sets before the 3rd (j=3) task set. That is, the two processing processes are the processing of task 1 in the 1st (j-2=1) task set and the processing of task 1 in the 2nd (j-1=2) task set.

For another example, in the example shown in FIG4b, for task 9 in the 4th (j=4) task set, the output of the first model is processed 3 (j-1=3) times, and the 3 processing processes are respectively included in the processing of one or more tasks in each of the 3 (j-1=3) task sets before the 4th (j=4) task set. That is, the 3 processing processes are the processing of task 1 in the 1st (j-3=1) task set, the processing of task 1 in the 2nd (j-2=2) task set, and the processing of task 8 in the 3rd (j-1=3) task set.

In one possible implementation, the process of the first communication device sending the second information in step S302 includes: when the performance of one or more tasks among the M tasks is lower than or equal to a threshold, the first communication device sending the second information. Specifically, after receiving the first information indicating the M tasks, the first communication device may obtain the performance corresponding to the M tasks, and when the performance of one or more tasks among the M tasks is lower than or equal to the threshold, the first communication device may send the second information indicating the performance of the one or more tasks, so that a recipient of the second information can identify the task with degraded performance through the second information.

In one possible implementation, the second information sent by the first communication device in step S302 is also used to identify one or more tasks in the M tasks. Specifically, the second information can be used to indicate the performance of one or more tasks in the M tasks, and accordingly, the second information can also be used to indicate the one or more tasks. In this way, the recipient of the second information can determine one or more tasks in the M tasks based on the second information, and clearly understand that the performance indicated by the second information is the performance of one or more tasks in the M tasks.

It should be understood that the second information may indicate one or more tasks in the M tasks in a manner similar to the manner in which the first information indicates one or more tasks in the M tasks. For example, the second information may include M identifiers, T indexes, and the like.

Based on the technical solution shown in Figure 3, after the first communication device receives the first information indicating M tasks in step S301, the first communication device can send the second information in step S302, and indicate the performance of one or more tasks in the M tasks through the second information. In other words, the first communication device can deploy the first model and measure and/or feedback the task performance achieved by the output of the first model based on the indication of the first information. Thereby, the computing power of the communication node can process the model and the downstream tasks (and/or downstream models) of the model while also being able to achieve performance measurement and/or performance feedback, and achieve performance feedback.

In addition, among the M tasks indicated by the first information, the input of any task is determined based on the output of the first model, that is, the output of the first model can be used to execute one or more tasks. In the above technical solution, the first communication device can measure and/or provide feedback on the performance of the M tasks corresponding to the first model based on the indication of the first information. This can improve the flexibility of the solution implementation while also enabling the first model to provide an indication of performance measurement and/or feedback of the downstream task in scenarios where the first model has downstream tasks.

In the method shown in FIG3 , the first information for indicating the M tasks sent by the second communication device in step S301 can be implemented in a variety of ways, which will be described below with reference to some implementation examples.

Implementation example one, the first information includes M identifiers, which are respectively used to indicate the M tasks; any identifier among the M identifiers includes K indexes, wherein the kth index among the K indexes is used to indicate one or more tasks in the kth task set among the first K task sets in P task sets, and the value of k is 1 to K, and K is a positive integer less than or equal to P.

It should be understood that the M identifiers are used to indicate M tasks respectively. It can be understood that the M identifiers correspond one-to-one to the M tasks, and/or the mth identifier among the M identifiers is used to indicate the mth task among the M tasks, and m takes a value of 1 to M.

In implementation example 1, the first information received by the first communication device may include M identifiers respectively used to indicate the M tasks. Among the P task sets, the task indicated by any one of the identifiers may be represented as a task in the Kth (K is an integer less than or equal to P) task set among the P task sets. Furthermore, any one of the M identifiers may include K indexes, so as to indicate one or more tasks contained in each task set in the K task sets through the K indexes.

Optionally, in implementation example 1, any identifier also includes the identifier of the first model. Specifically, the first communication device can deploy one or more first models, and accordingly, each first model may have downstream tasks. To this end, among the M identifiers used to indicate M tasks, any identifier can also include the identifier of the first model. In this way, the first information can indicate the downstream tasks corresponding to one or more first models.

As an application example of implementation example 1, taking the output of the first model as MPC, the value of M being 7 and the M tasks being tasks 1 to 7 as shown in FIG4 a , the implementation of the above-mentioned first information can be achieved by the method shown in the following Table 2.

Table 2

In Table 1, the identifiers corresponding to tasks 1 through 7 are "100," "101," "110," "111," "1000," "1010," and "1011," respectively. The first bit of each identifier indicates the "first model." For example, if the first bit of an identifier is 0, the identifier can be understood as indicating the first model; if the first bit of an identifier is 1, the identifier can be understood as indicating the downstream task of the first model.

For the identifier "0" of the first model, if the first information received by the first communication device in step S301 contains the identifier "0", the first communication device can determine that the first information indicates the first model, and the second information sent by the first communication device in step S302 can include the performance of the first model. For example, the second information can include multipath error, that is, the performance of task 1 is characterized by the value of the multipath error.

For the identifier "100" of task 1, the value of the K indexes is "00" after the first bit, that is, K = 1, and the 1 index occupies 2 bits. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "100", the first communication device can determine that the M tasks indicated by the first information include task 1 (i.e., channel prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of task 1, the second information can include the CSI normalized mean square error (NMSE), that is, the performance of task 1 is characterized by the value of the CSI NMSE.

For the identifier "101" of task 2, the values of the K indexes are "01" after the first bit, that is, K=1, and the K (K=1) indexes occupy 2 bits. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "101", the first communication device can determine that the M tasks indicated by the first information include task 2 (i.e., PMI prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of task 2, the second information can include PMI squared generalized cosine similarity (SGCS), that is, the performance of task 2 is characterized by the value of PMI SGCS.

For the identifier "110" of Task 3, the value of the K indexes is "10" after the first bit, that is, K = 1, and the K (K = 1) indexes occupy 2 bits. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "110", the first communication device can determine that the M tasks indicated by the first information include Task 3 (i.e., loss prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of Task 3, the second information may include a path loss error, that is, the performance of Task 3 is characterized by the value of the path loss error.

For the identifier "111" of task 4, the value of the K indexes is "11" after the first bit, that is, K = 1, and the K (K = 1) indexes occupy 2 bits. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "111", the first communication device can determine that the M tasks indicated by the first information include task 4 (i.e., line-of-sight path LoS prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of task 4, the second information can include the LOS detection accuracy rate, that is, the performance of task 4 is characterized by the value of the LOS detection accuracy rate.

For the identifier "1000" of Task 5, the value of the K indexes is "1000" after the first bit, that is, K = 2, and the first index of the K (K = 2)K indexes occupies 2 bits, and the second index occupies 1 bit. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "1000", the first communication device can determine that the M tasks indicated by the first information include Task 5 (i.e., MCS prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of Task 5, the second information can include the MCS accuracy rate, that is, the performance of Task 5 is characterized by the value of the MCS accuracy rate.

For the identifier "1010" of Task 6, the value of the K indexes is "1010" after the first bit, that is, K = 2, and the first index of the K (K = 2)K indexes occupies 2 bits, and the second index occupies 1 bit. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "1010", the first communication device can determine that the M tasks indicated by the first information include Task 6 (i.e., beam prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of Task 6, the second information can include the beam accuracy rate, that is, the performance of Task 6 is characterized by the value of the beam accuracy rate.

For the identifier "1011" of task 7, the value of the K indexes is "1011" after the first bit, that is, K=2, and the first index of the K(K=2)K indexes occupies 2 bits, and the second index occupies 1 bit. Accordingly, if the first information received by the first communication device in step S301 includes the identifier "1011", the first communication device can determine that the M tasks indicated by the first information include task 7 (i.e., beam prediction). If the second information subsequently sent by the first communication device in step S302 includes the performance of task 7, the second information can include a beam reference signal received power (RSRP) error, that is, the performance of task 7 is characterized by the value of the beam RSRP error.

Implementation example two, the first information includes T indexes, the tasks indicated by the T indexes are the M tasks, the T indexes respectively indicate T task sets in the P task sets, and T is a positive integer less than or equal to P; the tth index in the T indexes is used to indicate 0 or one or more tasks contained in the tth task set in the T task sets, and t is a positive integer less than T.

In implementation example 2, the N tasks downstream of the first model may be included in P task sets, and correspondingly, M tasks out of the N tasks may be included in T task sets out of the P task sets. The first information for indicating the M tasks may include T indexes, where the T indexes are respectively used to indicate 0 or one or more tasks of each task set in the T task sets. In this way, the first information can indicate the M tasks through the tasks included in each task set in the T task sets.

Optionally, in implementation example two, the first information also includes an identifier of the first model. Specifically, in addition to T indexes, the first information may also include an identifier of the first model. The first communication device may deploy one or more first models, and accordingly, each first model may have downstream tasks. To this end, in the first information for indicating M tasks, the first information may also include an identifier of the first model. In this way, the first information can indicate downstream tasks corresponding to one or more first models.

Optionally, in implementation example 2, the T indexes satisfy at least one of the following: in the t-th index among the T indexes, the value of the first bit is used to indicate whether the first information includes the t+x-th index, and the value of x is 1 to T-t; or, in the t-th index among the T indexes, when the value of the t-th index is a preset value, the t-th index is used to indicate the tasks (or all tasks) included in the t-th task set. Thus, in the case where the value of the first bit in the t-th index among the T indexes is used to indicate whether the first information includes the t+x-th index, the first communication device can determine whether it is necessary to parse the t+x-th index based on the value of the first bit in the t-th index, which can reduce implementation complexity and avoid unnecessary overhead.

In addition, when the value of the t-th index in the T indexes is a preset value, the t-th index is used to indicate the tasks included in the p-th task set. In this way, one or more tasks corresponding to an identifier can be indicated by a special value of the identifier, thereby reducing overhead.

Exemplarily, the value of the t-th index is a preset value, which can be understood as, among the multiple bits contained in the t-th index, the values of the other bits except the first bit are preset values (for example, all 0s or all 1s, etc.); or, among the multiple bits contained in the t-th index, the values of the multiple bits are preset values.

The following describes various implementations of T indexes by taking the scenario shown in FIG4b as an example, where the M tasks indicated by the first information are 4 tasks (task 1, task 2, task 5, and task 9).

As an application example of implementation example 2, the value of T is equal to the value of P. That is, the first information may include 4 (T=P=4) indexes, and the 4 indexes may be implemented in the manner shown in Table 3 below.

Table 3

As shown in Table 3, the first information may include T indexes, which are as follows:

The first index corresponds to the first task set in FIG4 b , and the value of the first index is used to indicate task 1 and task 2 .

The second index corresponds to the second task set in FIG4 b , and the value of the second index is used to indicate task 5 .

The third index corresponds to the third task set in FIG4 b , and the value of the third index is used to indicate that the M tasks do not include any task in the third task set.

The fourth index corresponds to the fourth task set in FIG4 b , and the value of the fourth index is used to indicate task 9 .

The following will take the first index as an example for illustration.

In a first implementation, the first index needs to indicate one or more tasks from Task 1 to Task 4. Accordingly, the first index may include four bits, indicating Task 1 to Task 4 via a bitmap. For example, when the value of the i-th bit in the four bits is 1, it may indicate that the M tasks include the i-th task corresponding to the i-th bit; conversely, when the value of the i-th bit in the four bits is 0, it may indicate that the M tasks do not include the i-th task corresponding to the i-th bit. In the above example, the value of the four bits in the first index may be "1100."

Optionally, in the first index, the value of the first bit can also be used to indicate whether the first information includes the 1+xth index (x is 1 to 3). Accordingly, the first index can include 5 bits, with a value of "11100". Among them, the value of the first bit is "1" to indicate that the first information includes at least one index among the second index, the third index, and the fourth index. The values of the last four bits are "1100", and their meanings are as described above.

In the second implementation, the first index needs to indicate one or more tasks from task 1 to task 4. There are 15 situations in total. Accordingly, at least four bits can be used to indicate the 15 situations, respectively, as follows:

Case 1: Indicates that M tasks include task 1, for example, the value of the four bits is 0001;

Case 2: Indicates that M tasks include task 2, for example, the values of the four bits are 0010;

Case 3: Indicates that M tasks include task 3, for example, the values of the four bits are 0011;

Case 4: indicating that the M tasks include task 4, for example, the values of the four bits are 0100;

Case 5: Indicates that the M tasks include Task 1 and Task 2. For example, the values of the four bits are 0101.

Case 6: Indicates that the M tasks include task 1 and task 3, for example, the values of the four bits are 0110;

Case 7: indicating that the M tasks include task 1 and task 4, for example, the values of the four bits are 0111;

Case 8: Indicates that the M tasks include Task 2 and Task 3. For example, the value of the four bits is 1000.

Case 9: indicating that the M tasks include task 2 and task 4, for example, the value of the four bits is 1001;

Case 10: indicating that the M tasks include task 3 and task 4, for example, the value of the four bits is 1010;

Case 11: indicating that the M tasks include task 1, task 2, and task 3, for example, the values of the four bits are 1011;

Case 12: indicating that the M tasks include task 1, task 3, and task 4, for example, the value of the four bits is 1100;

Case 13: indicating that the M tasks include task 1, task 2, and task 4, for example, the values of the four bits are 1101;

Case 14: indicating that the M tasks include task 2, task 3, and task 4, for example, the values of the four bits are 1110;

Case 15: Indicates that the M tasks include task 1, task 2, task 3, and task 4. For example, the values of the four bits are 1111.

In the above example, the value of the 4 bits in the first index may be "0101".

Optionally, in the first index, the value of the first bit can also be used to indicate whether the first information includes the 1+xth index (x is 1 to 3). Accordingly, the first index can include 5 bits, with a value of "10101". Among them, the value of the first bit is "1" to indicate that the first information includes at least one index among the second index, the third index, and the fourth index. The values of the last four bits are "0101", and their meanings are as described above.

In implementation mode three, the first index indicates four situations: one of tasks 1 to 4, a situation where none of tasks 1 to 4 are selected, and a situation where all of tasks 1 to 4 are selected. There are a total of six situations. Accordingly, at least three bits can be used to indicate the six situations, respectively, as follows:

Case 1: Indicates that M tasks include task 1, for example, the value of the four bits is 001;

Case 2: Indicates that M tasks include task 2, for example, the values of the four bits are 010;

Case 3: indicating that the M tasks include task 3, for example, the values of the four bits are 011;

Case 4: indicating that the M tasks include task 4, for example, the value of the four bits is 100;

Case 5: Indicates that tasks 1 to 4 are all not selected, for example, the value of the four bits is 101;

Case 6: Indicates that all tasks 1 to 4 are selected. For example, the value of the four bits is 111.

Optionally, 101 and 110 are reserved values and can be used to indicate other information.

In the fourth implementation, the first index indicates one of the tasks 1 to 4. Accordingly, at least two bits may be used to indicate the four situations, respectively, as follows:

Case 1: Indicates that M tasks include task 1, for example, the value of the four bits is 00;

Case 2: Indicates that the M tasks include task 2, for example, the values of the four bits are 01;

Case 3: Indicates that M tasks include task 3, for example, the value of the four bits is 10;

Case 4: Indicates that the M tasks include task 4, for example, the value of the four bits is 11.

In addition, in addition to including several bits to indicate tasks 1 to 4, a certain bit in the first index (e.g., the first bit) can be used to indicate whether the first information includes the second index. For example, when the value of the first bit in the first index is 0, the first communication device can determine that the first information does not include the second index and other identifiers after the second index; when the value of the first bit in the first index is 1, the first communication device can determine that the first information includes the second index and at least one of the other indexes after the second index.

As an application example of implementation example 2, the value of T is less than or equal to the value of P. Since M tasks (i.e., task 1, task 2, task 5, and task 9) are located in the first task set, the second task set, and the fourth task set, respectively, the first information may include three (T=3) indexes, which can be implemented as shown in Table 4 below.

Table 4

As shown in Table 4, the first information may include T indexes, which are as follows:

The first index corresponds to the first task set in FIG4 b , and the value of the first index is used to indicate task 1 and task 2 .

The second index corresponds to the second task set in FIG4 b , and the value of the second index is used to indicate task 5 .

The third index corresponds to the fourth task set in FIG4 b , and the value of the fourth index is used to indicate task 9 .

It should be noted that the values of the various indexes in Table 4 may refer to the implementation methods in the above implementation method 1 and implementation method 2.

In implementation example three, the first information includes M identifiers, and the M identifiers are respectively used to indicate the M tasks; among the M identifiers, lengths of different identifiers are the same.

In implementation example three, the first information can indicate M tasks respectively through M identifiers of equal length, that is, different tasks can be indicated by sequences of equal length. In this way, the implementation complexity can be reduced.

As an application example of implementation example three, taking the scenario shown in FIG4b as an example, the first information may include 10 (N=10) identifiers, and the 10 identifiers may be implemented in the manner shown in Table 5 below.

Table 5

In Table 5, different tasks can be indicated by different values of Q (Q is a positive integer) bits.

As an example, when any value from 1 to 10 is different, Q is greater than or equal to 4, that is, at least 4 bits are used to indicate ten different values.

As another example, if the values 1 to 5 are the same, and the values 6 to 10 are the same, then Q is greater than or equal to 1, meaning that at least one bit indicates two different values. In other words, in this case, the first information can be used to indicate whether the M tasks include tasks 1 to 5, and whether the M tasks include tasks 6 to 10.

Please refer to FIG5 , which is another schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

S501. The second communication device sends third information, and the first communication device receives the third information accordingly. The third information indicates M models, where M is a positive integer. Any of the M models is obtained based on the second model.

Optionally, the third information received by the first communication device in step S501 may be used to instruct one or more of the following operations to be performed on the performance of the M models: measurement, testing, monitoring, evaluation, measurement, feedback, reporting, uploading, or submission.

S502: The first communication device sends fourth information, and correspondingly, the second communication device receives the fourth information, wherein the fourth information is used to indicate the performance of one or more models in the M models.

It should be understood that any one of the M models is determined based on the second model, which can be understood as any one of the M models being obtained based on the second model through one or more model processings. Any of the one or more model processings can be model fine-tuning, model distillation, model pruning, model compression, model fusion, or other model processing.

Optionally, the performance of the model may include one or more of the accuracy, precision, and processing speed of the model (or the output of the model).

It should be noted that the fourth information may indicate the performance of one or more models in various ways.

For example, the first communication device may determine the performance of the one or more models based on the output of the one or more models after locally executing the one or more models and obtaining the output of the one or more models, and the fourth information sent by the first communication device may include information for indicating or characterizing the performance of the one or more models.

For another example, after the first communication device can locally execute the one or more models and obtain the output of the one or more models, the fourth information sent by the first communication device can include the output of the one or more models; subsequently, the recipient of the second information can determine the performance of the one or more models based on the output of the one or more models.

Optionally, the input of the second model can be implemented in a variety of ways, such as environmental parameters collected by the first communication device, communication signals received by the first communication device from other communication devices (such as the second communication device), and one or more of the information pre-configured locally by the first communication device.

For ease of understanding, the relationship between the second model and the M models will be exemplarily explained below with reference to the example shown in FIG6 a and taking the value of M as 8.

In the example shown in FIG6 a , any one of Model 1 , Model 2 , Model 3 and Model 4 can be obtained based on the second model, that is, any one of these four models can be obtained by processing the second model once.

In the example shown in FIG6 a , model 5 may be obtained based on model 1 , that is, model 5 may be obtained by performing two model processes using the second model.

In the example shown in FIG6 a , model 6 or model 7 may be obtained based on model 2 , that is, model 6 or model 7 may be obtained by performing two model processes on the second model.

In the example shown in FIG6 a , model 8 may be obtained based on model 5 , that is, model 8 may be obtained by processing the second model three times.

It should be noted that the value of M can be a positive integer. When M is other than a positive integer, the relationship between the M models and the first model can refer to the example shown in FIG6a. That is, among the M models, any model can be obtained by processing the second model through one or more models.

Exemplarily, the first model may be a wireless pre-trained model, a pre-trained model, or a wireless large model, etc.

In one possible implementation, the M models indicated by the third information sent by the second communication device in step S501 are included in N models, where N is an integer greater than or equal to M; the N models correspond to P model sets, each model set in the P model sets includes one or more models in the N models, and different model sets in the P model sets contain different models; wherein, any model in the i+1th model set in the P model sets is determined based on one or more models in the i-th model set in the P model sets, P is a positive integer, and i is 1 to P-1.

Exemplarily, taking P as greater than 2, among the P model sets, any one of the one or more models included in the first model set is obtained by model processing based on the second model, and any one of the one or more models included in the second model set is obtained by model processing based on one or more models in the first model set... and so on, any one of the one or more models included in the P-th model set is obtained by model processing based on one or more models in the P-1-th model set. In other words, the P model sets can also be expressed as P-level model sets. For example, among the P-level model sets, any one of the one or more models included in the first-level model set is obtained by model processing based on the second model, and any one of the one or more models included in the second-level model set is obtained by model processing based on one or more models in the first-level model set... and so on, any one of the one or more models included in the P-th model set is obtained by model processing based on one or more models in the P-1-th model set.

Specifically, among N models including M models, any one model is determined based on the second model. The any one model may be obtained by processing the second model zero times, one time, or multiple times, i.e., the N models may include a set of one or more downstream models of the second model. This enables the first information to provide an indication of performance measurement and/or feedback of downstream models of the second model in a scenario where the second model has a set of one or more downstream models.

Optionally, any model in the i+1th model set among the P model sets is determined based on one or more models in the i-th model set among the P model sets; it can be understood that any model in the p-th model set among the P model sets is obtained based on the second model after p-1 processing processes, and the p-1 processing processes include model processing corresponding to one or more models in each model set in the p-1 model sets before the p-th model set, and the value of p is 1 to P.

For ease of understanding, the relationship between the second model and the M models will be exemplarily described below with reference to the example shown in FIG. 6 b , taking the value of N being 11 and the value of M being 8 as an example.

In the example shown in FIG6 b , the implementation of Models 1 to 8 may refer to FIG6 a and related descriptions above.

In the example shown in FIG6 b , model 9 or model 10 may be obtained based on model 8 , that is, model 9 or model 10 may be obtained by processing model 8 .

In the example shown in FIG6 b , model 11 may be obtained based on model 9 and model 10 , that is, model 11 may be obtained by model processing of model 9 and model 10 .

Furthermore, in the example shown in FIG6 b , 10 (N=11) models may correspond to 4 (P=5) model sets.

In the first model set, any model can be obtained based on the second model. For example, in the first model set, any model among Model 1, Model 2, Model 3 and Model 4 is obtained by a certain model processing of the second model.

When i=1 and i+1=2, in the second model set, any model can be obtained by processing the second model with the model corresponding to one or more models in the previous model set (i.e., by processing the model with one or more models in the first model set). For example, in the second model set, Model 5 is obtained by processing the second model with the model corresponding to Model 1; and Model 6 or Model 7 is obtained by processing the second model with the model corresponding to Model 2.

When i=2 and i+1=3, in the third model set, any model can be obtained by processing the second model with the corresponding models of one or more models in the first two model sets (i.e., by processing the models of one or more models in the second model set). For example, in the third model set, model 8 is obtained by processing the second model with the corresponding models of models 1 and 5.

When i=3 and i+1=4, in the fourth model set, any model can be obtained by processing the second model with the models corresponding to one or more models in the first three model sets (i.e., by processing the models based on one or more models in the third model set). For example, in the fourth model set, Model 9 or Model 10 is obtained by processing the second model with the models corresponding to Model 1, Model 5, and Model 8.

When i=4 and i+1=5, in the fifth model set, any model can be obtained by processing the second model with the models corresponding to one or more models in the first four model sets (i.e., by processing the models based on one or more models in the fourth model set). For example, in the fifth model set, model 11 is obtained by processing the second model with the models corresponding to models 1, 5, 8, 9, and 10.

In one possible implementation, the process of the first communication device sending the fourth information in step S502 includes: when the performance of one or more models among the M models is lower than or equal to a threshold, the first communication device sending the fourth information. Specifically, after receiving the third information indicating the M models, the first communication device may obtain the performance corresponding to the M models, and when the performance of one or more models among the M models is lower than or equal to the threshold, the first communication device may send fourth information indicating the performance of the one or more models, so that the recipient of the fourth information can determine the model with degraded performance through the fourth information.

In one possible implementation, the fourth information sent by the first communication device in step S502 may also be used to indicate one or more models among the M models. Specifically, the fourth information may be used to indicate the performance of one or more models among the M models, and accordingly, the fourth information may also be used to indicate the one or more models. In this way, the recipient of the fourth information can determine one or more models among the M models based on the fourth information, and clearly specify that the performance indicated by the fourth information is the performance of one or more models among the M models.

It should be understood that the manner in which the fourth information indicates one or more models among the M models may refer to the manner in which the third information indicates one or more models among the M models. For example, the fourth information may include M identifiers, T indexes, and the like.

Based on the technical solution shown in Figure 5, after the first communication device receives the first information indicating M models in step S501, the first communication device can send fourth information in step S502, and indicate the performance of one or more models in the M models through the fourth information. In other words, the first communication device can deploy the second model and M models obtained based on the second model, and measure and/or feedback the performance of the M models based on the indication of the first information. Thereby, the computing power of the communication node can process the model and the downstream tasks (and/or downstream models) of the model while also achieving performance measurement and/or performance feedback.

In addition, among the M models indicated by the first information, any one of the models is determined based on the second model, that is, the second model can be processed once or multiple times to generate any one of the M models. In the above technical solution, the first communication device can measure and/or provide feedback on the performance of the M models corresponding to the second model based on the indication of the first information, which can improve the flexibility of the solution implementation and also enable the performance measurement and/or feedback of the downstream model to be indicated in the scenario where the second model has a downstream model.

In the method shown in FIG. 5 , the third information for indicating the M models sent by the second communication device in step S501 can be implemented in a variety of ways, which will be described below with reference to some implementation examples.

Implementation example A, the third information includes M identifiers, which are respectively used to indicate the M models; any identifier among the M identifiers includes K indexes; wherein, the kth index among the K indexes is used to indicate the model processing corresponding to one or more models in the kth model set among the first K model sets in the P model sets, and the value of k is 1 to K, and K is a positive integer less than or equal to P.

It should be understood that the M identifiers are used to indicate M models respectively. It can be understood that the M identifiers correspond one-to-one to the M models, and/or the mth identifier among the M identifiers is used to indicate the mth model among the M models, and m takes a value of 1 to M.

In implementation example A, the third information received by the first communication device may include M identifiers, each used to indicate the M models. Among the P model sets, the model indicated by any one of the identifiers may be represented as a model in the Kth (K is an integer less than or equal to P) model set among the P model sets. Furthermore, any one of the M identifiers may include K indexes, so as to indicate the model processing corresponding to the model through the K indexes.

Optionally, in implementation example A, any identifier also includes an identifier of the processing of the second model. Specifically, the first communication device can deploy one or more second models, and accordingly, each second model may have a downstream model. To this end, among the M identifiers used to indicate M models, any identifier can also include an identifier of the second model. In this way, the third information can indicate the downstream models corresponding to one or more second models.

It should be noted that, for the implementation of the third information in Example A, reference can be made to the implementation process of the first information in Example 1 above.

Implementation example B, the third information includes T indexes, the models indicated by the T indexes are the M models, the T indexes are respectively used to indicate the T model sets in the P model sets, and T is a positive integer less than or equal to P; the t-th index in the T indexes is used to indicate 0 or one or more models contained in the t-th model set in the T model sets, and t is a positive integer less than T.

In implementation example B, the N models downstream of the second model may be included in P model sets, and correspondingly, M models among the N models may be included in T model sets among the P model sets. The third information used to indicate the M models may include T indexes, where the T indexes are respectively used to indicate 0 or one or more models of each model set in the T model sets. In this way, the third information can indicate the M models through the models included in each model set in the T model sets.

Optionally, in implementation example B, the third information also includes an identifier of the processing of the second model. Specifically, in addition to T indexes, the third information may also include an identifier of the second model. The first communication device may deploy one or more second models, and accordingly, each second model may have a downstream model. To this end, in the third information for indicating M models, the third information may also include an identifier of the second model. In this way, the third information can indicate the downstream models corresponding to one or more second models.

Optionally, in implementation example B, the T indexes satisfy at least one of the following: in the tth index among the T indexes, the value of the first bit is used to indicate whether the third information includes the t+xth index, and the value of x is 1 to T-t; or, in the tth index among the T indexes, when the value of the tth index is a preset value, the tth index is used to indicate the model (or all models) included in the tth model set.

Exemplarily, the value of the t-th index is a preset value, which can be understood as: among the multiple bits included in the t-th index, the values of the other bits except the first bit are preset values (for example, all 0s or all 1s); or, among the multiple bits included in the t-th index, the values of the multiple bits are preset values. Specifically, in the case where the value of the first bit in the t-th index of the T indexes is used to indicate whether the third information includes the t+x-th index, the first communication device can determine whether it is necessary to parse the t+x-th index based on the value of the first bit in the t-th index, which can reduce implementation complexity and avoid unnecessary overhead.

In addition, in the t-th index of the T indexes, when the value of the t-th index is a preset value, the t-th index is used to indicate the models included in the t-th model set. In this way, one or more models corresponding to an identifier can be indicated by a special value of the identifier, thereby reducing overhead.

It should be noted that, for the implementation of the third information in Example B, reference can be made to the implementation process of the first information in Example 2 above.

In implementation example C, the third information includes M identifiers, where the M identifiers are respectively used to indicate the M models; among the M identifiers, the lengths of different identifiers are the same.

Optionally, in implementation example C, the third information can indicate M models respectively through M equal-length identifiers, that is, different models can be indicated by sequences of equal length. In this way, the implementation complexity can be reduced.

It should be noted that, for the implementation of the third information in Example C, reference can be made to the implementation process of the first information in Example 3 above.

Referring to Figure 7, an embodiment of the present application provides a communication device 700. This communication device 700 can implement the functions of the second communication device or the first communication device in the above-mentioned method embodiment, thereby also achieving the beneficial effects of the above-mentioned method embodiment. In this embodiment of the present application, the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component, such as a chip, within the first communication device (or the second communication device).

It should be noted that the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in Figure 3 and related embodiments, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive first information, and the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any one of the M tasks is determined based on the output of the first model; the processing unit 701 is used to determine second information; and the transceiver unit 702 is also used to send second information, and the second information is used to indicate the performance of one or more tasks among the M tasks.

In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in Figure 3 and related embodiments, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to determine first information; the transceiver unit 702 is used to send first information, and the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any task among the M tasks is determined based on the output of the first model; the transceiver unit 702 is also used to receive second information, and the second information is used to indicate the performance of one or more of the M tasks.

In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in Figure 5 and related embodiments, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive third information, and the third information is used to indicate M models, where M is a positive integer; wherein any one of the M models is determined based on the second model; the processing unit 701 is used to determine fourth information; and the transceiver unit 702 is also used to send fourth information, and the fourth information is used to indicate the performance of one or more models among the M models.

In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in Figure 5 and related embodiments, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to determine third information, and the transceiver unit 702 is used to send third information, and the third information is used to indicate M models, where M is a positive integer; wherein any model among the M models is determined based on the second model; and the transceiver unit 702 is also used to receive fourth information, and the fourth information is used to indicate the performance of one or more models among the M models.

In one possible design, when the communication device 700 is a terminal device, a terminal, or a communication module within a terminal, the functions of the processing unit 701 may be implemented by one or more processors. Specifically, the processor may include a modem chip, or a system-on-chip (SoC) chip or SIP chip containing a modem core. The functions of the transceiver unit 702 may be implemented by a transceiver circuit.

In one possible design, when the communication device 700 is a circuit or chip responsible for communication functions in a terminal, such as a modem chip or a system-on-chip (SoC) chip or SIP chip containing a modem core, the functions of the processing unit 701 can be implemented by a circuit system including one or more processors or processor cores in the aforementioned chip. The functions of the transceiver unit 702 can be implemented by an interface circuit or data transceiver circuit on the aforementioned chip.

It should be noted that, for details on the information execution process of the units of the above-mentioned communication device 700, please refer to the description in the method embodiment shown above in this application, and no further details will be given here.

Please refer to Fig. 8, which is another schematic structural diagram of a communication device 800 provided in this application. The communication device 800 includes a logic circuit 801 and an input/output interface 802. The communication device 800 may be a chip or an integrated circuit.

The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the input/output interface 802 in FIG8 , which may include an input interface and an output interface. Alternatively, the communication interface may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in Figure 3 and related embodiments, the input-output interface 802 is used to receive first information, and the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any one of the M tasks is determined based on the output of the first model; the logic circuit 801 is used to determine second information; and the input-output interface 802 is also used to send second information, and the second information is used to indicate the performance of one or more tasks among the M tasks.

In one possible implementation, when the device 800 is used to execute the method executed by the second communication device in Figure 3 and related embodiments, the logic circuit 801 is used to determine the first information; the input-output interface 802 is used to send the first information, and the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any one of the M tasks is determined based on the output of the first model; and the input-output interface 802 is also used to receive the second information, and the second information is used to indicate the performance of one or more of the M tasks.

In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in Figure 5 and related embodiments, the input-output interface 802 is used to receive third information, and the third information is used to indicate M models, where M is a positive integer; wherein any one of the M models is determined based on the second model; the logic circuit 801 is used to determine fourth information; and the input-output interface 802 is also used to send fourth information, and the fourth information is used to indicate the performance of one or more models among the M models.

In one possible implementation, when the device 800 is used to execute the method executed by the second communication device in Figure 5 and related embodiments, the logic circuit 801 is used to determine the third information, and the input-output interface 802 is used to send the third information, and the third information is used to indicate M models, where M is a positive integer; wherein any one of the M models is determined based on the second model; and the input-output interface 802 is also used to receive fourth information, and the fourth information is used to indicate the performance of one or more models among the M models.

The logic circuit 801 and the input/output interface 802 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

Optionally, the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or entirely implemented by software. The functions of the processing device may be partially or entirely implemented by software.

Optionally, the processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and/or steps in any one of the method embodiments.

Alternatively, the processing device may include only a processor. A memory for storing the computer program is located outside the processing device, and the processor is connected to the memory via circuits/wires to read and execute the computer program stored in the memory. The memory and processor may be integrated or physically separate.

Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontrollers (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

Please refer to Figure 9, which shows a communication device 900 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 900 can specifically be a communication device serving as a terminal device in the above-mentioned embodiments. The example shown in Figure 9 is that the terminal device is implemented through the terminal device (or a component in the terminal device).

Herein, a possible logical structure diagram of the communication device 900 is shown. The communication device 900 may include but is not limited to at least one processor 901 and a communication port 902 .

The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the communication port 902 in FIG9 , which may include an input interface and an output interface. Alternatively, the communication port 902 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

Further optionally, the device may also include at least one of a memory 903 and a bus 904. In an embodiment of the present application, the at least one processor 901 is used to control and process the actions of the communication device 900.

In addition, the processor 901 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

It should be noted that the communication device 900 shown in Figure 9 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment and achieve the corresponding technical effects of the terminal device. The specific implementation methods of the communication device shown in Figure 9 can refer to the description in the aforementioned method embodiment and will not be repeated here.

Please refer to Figure 10, which is a structural diagram of the communication device 1000 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1000 can specifically be a communication device as a network device in the above-mentioned embodiments. The example shown in Figure 10 is that the network device is implemented through the network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Figure 10.

The communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device also includes at least one memory 1012, at least one transceiver 1013 and one or more antennas 1015. The processor 1011, the memory 1012, the transceiver 1013 and the network interface 1014 are connected, for example, via a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1015 is connected to the transceiver 1013. The network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the network interface 1014 in FIG10 , which may include an input interface and an output interface. Alternatively, the network interface 1014 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

Processor 1011 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process software program data, for example, to support the communication device in performing the actions described in the embodiments. The communication device may include a baseband processor and a central processing unit. The baseband processor is primarily used to process communication protocols and communication data, while the central processing unit is primarily used to control the entire terminal device, execute software programs, and process software program data. Processor 1011 in Figure 10 may integrate the functions of both a baseband processor and a central processing unit. Those skilled in the art will appreciate that the baseband processor and the central processing unit may also be independent processors interconnected via a bus or other technology. Those skilled in the art will appreciate that a terminal device may include multiple baseband processors to accommodate different network standards, multiple central processing units to enhance its processing capabilities, and various components of the terminal device may be connected via various buses. The baseband processor may also be referred to as a baseband processing circuit or a baseband processing chip. The central processing unit may also be referred to as a central processing circuit or a central processing chip. The functionality for processing communication protocols and communication data may be built into the processor or stored in memory as a software program, which is executed by the processor to implement the baseband processing functionality.

The memory is primarily used to store software programs and data. Memory 1012 can exist independently and be connected to processor 1011. Alternatively, memory 1012 and processor 1011 can be integrated together, for example, within a single chip. Memory 1012 can store program code for executing the technical solutions of the embodiments of the present application, and execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

Figure 10 shows only one memory and one processor. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the present embodiment.

The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1013 can be connected to the antenna 1015. The transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive radio frequency signals. The receiver Rx of the transceiver 1013 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or digital intermediate frequency signal to the processor 1011 so that the processor 1011 can further process the digital baseband signal or digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1013 is also used to receive a modulated digital baseband signal or digital intermediate frequency signal from the processor 1011, convert the modulated digital baseband signal or digital intermediate frequency signal into a radio frequency signal, and transmit the radio frequency signal through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal. The order of the down-mixing and analog-to-digital conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal. The order of the up-mixing and digital-to-analog conversion processes is adjustable. The digital baseband signal and the digital intermediate frequency signal may be collectively referred to as digital signals.

The transceiver 1013 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit that implements a receiving function may be referred to as a receiving unit, and a device in the transceiver unit that implements a transmitting function may be referred to as a transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

It should be noted that the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network device. The specific implementation methods of the communication device 1000 shown in Figure 10 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.

Please refer to FIG11 , which is a schematic structural diagram of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.

It can be understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to implement the technical solutions provided in this application. The communication device 110 can be the terminal device or network device described above, or a component (such as a chip) in these devices, used to implement the method described in the following method embodiment. The communication device 110 includes one or more processors 111. The processor 111 can be a general-purpose processor or a dedicated processor. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a RAN node, terminal, or chip, etc.), execute software programs, and process data of software programs.

Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instructions), which may be executed on the processor 111 to cause the communication device 110 to perform the methods described in the following embodiments. In yet another possible design, the communication device 110 includes circuitry (not shown in FIG11 ).

Optionally, the communication device 110 may include one or more memories 112 on which a program 114 (sometimes also referred to as code or instructions) is stored. The program 114 can be run on the processor 111, so that the communication device 110 executes the method described in the above method embodiment.

Optionally, the processor 111 and/or the memory 112 may include AI modules 117 and 118, which are used to implement AI-related functions. The AI module can be implemented through software, hardware, or a combination of software and hardware. For example, the AI module may include a wireless intelligent control (RIC) module. For example, the AI module may be a near-real-time RIC or a non-real-time RIC.

Optionally, data may be stored in the processor 111 and/or the memory 112. The processor and the memory may be provided separately or integrated together.

Optionally, the communication device 110 may further include a transceiver 115 and/or an antenna 116. The processor 111 may also be referred to as a processing unit, and controls the communication device (e.g., a RAN node or terminal). The transceiver 115 may also be referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, and is configured to implement the transceiver functions of the communication device through the antenna 116.

The processing unit 701 shown in FIG7 may be the processor 111. The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the transceiver 115 shown in FIG11 . The transceiver 115 may include an input interface and an output interface. Alternatively, the transceiver 115 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.

An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method that may be implemented by the above-mentioned first communication device or second communication device.

An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and/or data to the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.

An embodiment of the present application further provides a communication system, wherein the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. Whether a function is performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

In addition, the functional units in the various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the contributing part or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

Claims (15)

A communication method, comprising: Receive first information, where the first information is used to indicate M tasks, where M is a positive integer; wherein an input of any one of the M tasks is determined based on an output of the first model; Second information is sent, where the second information is used to indicate performance of one or more tasks among the M tasks. A communication method, comprising: Sending first information, where the first information is used to indicate M tasks, where M is a positive integer; wherein the input of any task in the M tasks is determined based on the output of the first model; Second information is received, where the second information is used to indicate performance of one or more of the M tasks. The method according to claim 1 or 2, characterized in that the M tasks are contained in N tasks, N is an integer greater than or equal to M; the N tasks correspond to P task sets, each task set in the P task sets includes one or more tasks in the N tasks, and different task sets in the P task sets include different tasks; The input of any task in the i+1th task set among the P task sets includes the output of one or more tasks in the i-th task set among the P task sets, where P is a positive integer and i is 1 to P-1. The method according to claim 3, wherein the first information includes M identifiers, and the M identifiers are respectively used to indicate the M tasks; Any one of the M identifiers includes K indexes; wherein, the kth index among the K indexes is used to indicate one or more tasks in the kth task set among the first K task sets among the P task sets, and the value of k is 1 to K, where K is a positive integer less than or equal to P. The method according to claim 4 is characterized in that the any identifier also includes an identifier of the first model. The method according to claim 4, characterized in that The first information includes T indexes, the tasks indicated by the T indexes are the M tasks, and the T indexes respectively indicate T task sets in the P task sets, where T is a positive integer less than or equal to P; the tth index in the T indexes is used to indicate 0 or one or more tasks contained in the tth task set in the T task sets, where t is a positive integer less than T. The method according to claim 6 is characterized in that the first information also includes an identification of the first model. The method according to claim 6 or 7, wherein the T indexes satisfy at least one of the following: In the t-th index of the T indexes, the value of the first bit is used to indicate whether the first information includes the t+x-th index, where the value of x is 1 to T-t; or, In the t-th index among the T indexes, when the value of the t-th index is a preset value, the t-th index is used to indicate the tasks included in the t-th task set. The method according to any one of claims 1 to 3, characterized in that the first information includes M identifiers, and the M identifiers are respectively used to indicate the M tasks; Among the M identifiers, lengths of different identifiers are the same. The method according to any one of claims 1 to 9 is characterized in that the second information is also used to indicate one or more tasks among the M tasks. The method according to any one of claims 1 to 10, characterized in that the performance of one or more tasks among the M tasks is lower than or equal to a threshold. A communication device, characterized by comprising a module for executing the method according to any one of claims 1 to 11. A communication device, characterized by comprising at least one processor, wherein the at least one processor is coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 11. The communication device according to claim 13, wherein the communication device is a chip or a chip system. A readable storage medium, characterized in that a computer program or instruction is stored in the storage medium, and when the computer program or instruction is executed by a communication device, the method according to any one of claims 1 to 11 is implemented.