WO2025103178A1 - Data transmission method and communication apparatus - Google Patents

Abstract

Provided in the embodiments of the present application are a data transmission method and a communication apparatus. The method comprises: acquiring first data; determining dimension configuration information corresponding to the first data, wherein the dimension configuration information is used for indicating the dimension magnitude of each dimension among N dimensions of second data, the number of elements included in the second data is the same as the number of elements included in the first data, the N dimensions are M dimensions of the first data, and dimension magnitudes corresponding to at least some dimensions between the N dimensions and the M dimensions are different, or N is different from M; determining the second data on the basis of the first data and the dimension configuration information; and sending third data, wherein the third data is data obtained after the second data is subjected to compression processing. In this way, before compressing first data, a first apparatus can orchestrate the dimensions of the first data by means of dimension configuration information to obtain second data, so as to compress the second data, thereby reducing the computational load of data compression and improving the efficiency of data compression.

Description

Data transmission method and communication device

This application claims the priority of the Chinese patent application filed with the State Intellectual Property Office on November 13, 2023, with application number 202311509942.8 and application name “Data Transmission Method and Communication Device”, all contents of which are incorporated by reference in this application.

Technical Field

The present application relates to the field of communications, and in particular to a data transmission method and a communication device.

Background Art

Sensing, imaging, artificial intelligence (AI), or machine learning (ML) are potential applications of future communication systems (e.g., 6th generation communication systems). These applications involve large-scale high-dimensional data transmission due to the wideband multi-frequency, larger-scale multiple-input multiple-output (MIMO) antenna arrays, and the acquisition of signals from different directions.

In order to reduce the wireless resource overhead of data transmission, high-dimensional data can be compressed. However, how to achieve high-dimensional data compression to improve the efficiency of data compression remains to be studied.

Summary of the invention

The data transmission method and communication device provided in the embodiments of the present application provide an implementation scheme for high-dimensional data compression, which can improve the efficiency of data compression.

To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

In a first aspect, a data transmission method is provided, which can be executed by a first device, and the first device can be a terminal device itself, or a processor, module, chip, or chip system that implements the method in the terminal device; or, the first device can be an access network device itself, or a processor, module, chip, or chip system that implements the method in the access network device. The following is an example of the method being executed by the first device. The method includes: obtaining first data, the dimension of the first data is M dimensions, and M is an integer greater than 1; determining dimension configuration information corresponding to the first data, the dimension configuration information is used to indicate the dimension size of each dimension in the N dimensions of the second data, the number of elements contained in the second data is the same as the first data, the N dimensions are M dimensions, and the dimension sizes corresponding to at least some dimensions between the N dimensions and the M dimensions are different, or N is different from M, and N is a positive integer; determining the second data according to the first data and the dimension configuration information; sending the third data, the third data is the data after the second data is compressed.

Due to the embodiment of the present application, the first device can arrange the dimensions of the first data to obtain second data through the dimension configuration information of the first data before compressing the first data, so that the number of dimensions or the size of at least some dimensions of the second data and the first data are different, and then the second data is compressed, which can reduce the amount of data compression calculations and improve the efficiency of data compression.

In a second aspect, a data transmission method is provided, which can be executed by a second device, the second device can be a terminal device itself, or a processor, module, chip, or chip system that implements the method in the terminal device; or, the second device can be an access network device itself, or a processor, module, chip, or chip system that implements the method in the access network device. The following is an example of the method being executed by the second device. The method includes: receiving third data, the third data is associated with the first data, the dimension of the first data is M dimensions, and M is a positive integer greater than 1; obtaining the dimension size of each dimension in the M dimensions and the dimension configuration information corresponding to the first data, the dimension configuration information is used to indicate the dimension size of each dimension in the N dimensions of the second data, the number of elements contained in the second data is the same as the first data, the N dimensions are M dimensions, and the dimension sizes corresponding to at least some dimensions between the N dimensions and the M dimensions are different, or N is different from M, and N is a positive integer; according to the third data, the dimension configuration information, and the dimension size of each dimension in the M dimensions, determine the fourth data.

Among them, the technical effect of the second aspect can refer to the first aspect and will not be repeated here.

In combination with the first aspect or the second aspect, in a possible implementation, the dimension configuration information is associated with at least one of the following: information of compression processing, and/or sub-dimension information corresponding to the second dimension of the M dimensions. That is, the dimension size of each of the N dimensions indicated by the dimension configuration information is associated with the information of compression processing and/or sub-dimension information corresponding to the second dimension of the M dimensions, thereby making the dimension size of the N dimensions and each of the N dimensions match the compression processing type of subsequent compression processing and/or the dimension size of the sub-dimension corresponding to the second dimension, thereby improving the effect of compression processing and the efficiency of compression processing.

In combination with the first aspect or the second aspect, in a possible implementation manner, a difference or ratio of the dimensional sizes between any two dimensions in the N dimensions is less than a first threshold. That is, the difference or ratio of the maximum dimensional size to the minimum dimensional size among the N dimensional sizes corresponding to the N dimensions is less than a first threshold. The difference or ratio between them is less than the first threshold, so that the sizes of the N dimensions tend to be the same, which facilitates subsequent compression processing and improves the efficiency of the compression processing.

In combination with the first aspect or the second aspect, in a possible implementation, the difference or ratio between the dimension size of each dimension in the N dimensions and the geometric mean dimension size corresponding to the M dimensions is less than or equal to the second threshold; or, the sum of the deviations corresponding to each dimension in the N dimensions is less than or equal to the second threshold, and the deviation corresponding to each dimension in the N dimensions is determined based on the ratio between the dimension size of each dimension in the N dimensions and the geometric mean dimension size. In other words, the dimension size of each dimension in the N dimensions is similar to the geometric mean dimension size corresponding to the M dimensions, or the dimension size corresponding to some dimensions in the N dimensions may be different from the geometric mean dimension, but the deviation between the N dimension sizes corresponding to the N dimensions as a whole and the geometric mean dimension size is small, thereby improving the flexibility of determining the size of each dimension in the N dimensions to adapt to different types of compression processing.

In combination with the first aspect or the second aspect, in a possible implementation, the dimension configuration information is determined according to the dimension size of each dimension in the M dimensions and the dimension arrangement rule. That is, the first device or the second device can determine the dimension configuration information according to the dimension arrangement rule and the size of each dimension in the M dimensions.

Optionally, the dimension arrangement rule includes at least one of the following: a corresponding method of dimension arrangement, and/or a corresponding relationship between N dimension sizes and N dimensions. Among them, the corresponding method of dimension arrangement may include dimension arrangement based on prime number decomposition, and dimension arrangement based on reference dimension size. The corresponding relationship between N dimension sizes and N dimensions can be used to: indicate the corresponding relationship between the N dimension sizes of the dimension arrangement and the N dimensions of the second data, so that the first device or the second device can determine the dimension size of each dimension in the N dimensions according to the corresponding relationship.

In combination with the first aspect, in a possible implementation, determining the second data according to the first data and the dimension configuration information includes: determining the second data according to the first data and the dimension configuration when the evaluation parameters corresponding to the M dimensions are greater than or equal to the third threshold. That is, when the M dimensions of the first data satisfy the evaluation parameter greater than or equal to the third threshold, the first device can determine that the M dimensions of the first data do not match the subsequent compression processing, and then arrange the dimensions of the first data to facilitate the subsequent compression processing and improve the compression efficiency.

In combination with the first aspect or the second aspect, in a possible implementation, the evaluation parameter includes a first type parameter, and/or a second type parameter, and the third threshold includes a third threshold corresponding to the first type parameter, and/or a third threshold corresponding to the second type parameter; wherein the first type parameter is the sum of the deviations corresponding to each dimension in the M dimensions, the deviation corresponding to each dimension in the M dimensions is determined based on the dimensional size of each dimension in the M dimensions and the geometric mean dimensional size corresponding to the M dimensions, and the second type parameter is the difference or ratio between the maximum dimensional size and the minimum dimensional size among the M dimensional sizes corresponding to the M dimensions. In other words, the first device can determine whether to dimensionally arrange the first data based on at least two types of parameters, thereby improving the flexibility of the first device in determining whether to dimensionally arrange the first data.

In combination with the first aspect, in a possible implementation, the method provided by the first aspect further includes: receiving first indication information, the first indication information being used to indicate the type of the evaluation parameter and the third threshold value corresponding to the evaluation parameter. That is, the first device can determine the evaluation parameter and the third threshold value corresponding to the evaluation parameter according to the first indication information, thereby increasing the flexibility of the first device in determining whether to perform dimension arrangement on the first data, so as to be applicable to different scenarios.

In conjunction with the second aspect, in a possible implementation, the method provided by the second aspect further includes: sending first indication information, where the first indication is used to indicate the type of the evaluation parameter and the third threshold value corresponding to the evaluation parameter. That is, the second device can indicate the type of the evaluation parameter and the third threshold value corresponding to the evaluation parameter to the first device, so that the first device can determine the evaluation parameter and the third threshold value corresponding to the evaluation parameter according to the first indication information, thereby increasing the flexibility of the first device in determining whether to perform dimension arrangement on the first data, so as to be applicable to different scenarios.

In combination with the first aspect or the second aspect, in a possible implementation, the first indication information includes index information, and the index information is used to determine the third threshold corresponding to the evaluation parameter from the candidate third threshold set, and the candidate third threshold set includes at least two third thresholds corresponding to the evaluation parameters. That is, the first device can determine the third threshold according to the index information, thereby reducing the overhead of the first indication information and increasing the reliability of the first indication information.

In combination with the first aspect or the second aspect, in a possible implementation, the first indication information is further used to indicate a candidate third threshold value set. That is, the second device may configure the candidate dimension set to the first device to indicate to the first device the type of valence parameter and the third threshold value corresponding to the evaluation parameter that the second device expects to use in the next period of time.

In combination with the first aspect, in a possible implementation manner, the method provided by the first aspect further includes: sending second indication information, where the second indication information is used to indicate the dimension size of each dimension in the M dimensions.

In combination with the second aspect, in a possible implementation manner, the method provided by the second aspect also includes: receiving second indication information, where the second indication information is used to indicate the dimension size of each dimension in the M dimensions.

That is, for a scenario in which the dimension configuration information is indicated by the second device, the first device may send each of the M dimensions to the second device. The second device determines the dimension size of each dimension in the M dimensions, so that the second device can determine the dimension configuration information, and send the dimension configuration information to the first device, which can improve the flexibility of the second device in obtaining the dimension size of each dimension in the M dimensions. When the first device determines the dimension configuration information, the second device can determine the dimension configuration information (that is, the dimension size of each dimension in the N dimensions) according to the second indication information and the dimension arrangement rule, and then determine the fourth data according to the dimension size of each dimension in the M dimensions, the dimension configuration information, and the third data.

In combination with the first aspect or the second aspect, in a possible implementation, the second indication information is also used to indicate dimension configuration information. That is, for the second device, the second device can directly determine the dimension configuration information based on the second indication information, which can improve the flexibility of the second device in obtaining the dimension configuration information and the size of each dimension in the M dimensions to adapt to different scenarios. For example, in the case where the dimension arrangement rule is not pre-configured and the dimension arrangement rule is not agreed upon between the first device and the second device, the second indication information sent by the first device to the second device also indicates the dimension configuration information, so that the second device can determine the dimension size and dimension configuration information of each dimension in the M dimensions based on the second indication information, so as to facilitate the subsequent decompression of the third data to determine the fourth data.

In combination with the second aspect, in a possible implementation manner, the method provided by the second aspect also includes: sending second indication information, where the second indication information is used to indicate the dimension size of each dimension in the M dimensions.

In combination with the first aspect, in a possible implementation manner, the method provided by the first aspect further includes: receiving second indication information, where the second indication information is used to indicate the dimension size of each dimension in the M dimensions.

That is to say, the first device can determine the dimension configuration information according to the second indication information, which can improve the flexibility of the first device in determining the dimension configuration information to adapt to different application scenarios.

In combination with the first aspect or the second aspect, in a possible implementation, the second indication information includes index information, and the index information is used to determine the dimension size of each dimension in the N dimensions from a candidate dimension size set, and the candidate dimension size set includes at least two groups of dimension sizes, and each group of dimension sizes in the at least two groups of dimension sizes includes the dimension size of each dimension in the N dimensions. That is, the second device can indicate the dimension size of each dimension in the N dimensions to the second device by indicating the index of the dimension configuration information in the candidate set, thereby saving the indication overhead of the second indication information and improving the reliability of the second indication information.

In combination with the first aspect or the second aspect, in a possible implementation, the second indication information is further used to indicate a candidate dimension size set. That is, the second device may configure the candidate dimension size set to the first device to indicate to the first device the candidate dimension size of the second data expected by the second device in the next period of time, that is, the dimension size of each dimension in the candidate N dimensions.

In a third aspect, a communication device is provided for implementing the above-mentioned various methods. The communication device may be the first device in the above-mentioned first aspect or any implementation thereof, or a device including the above-mentioned first device, or a device included in the above-mentioned first device, such as a chip; or, the communication device may be the second device in the above-mentioned second aspect or any implementation thereof, or a device including the above-mentioned second device, or a device included in the above-mentioned second device, such as a chip. The communication device includes a module, unit, or means corresponding to the implementation of the above-mentioned method, and the module, unit, or means may be implemented by hardware, software, or by executing the corresponding software implementation by hardware. The hardware or software includes one or more modules or units corresponding to the above-mentioned functions.

In some possible designs, the communication device may include a processing module and a transceiver module. The transceiver module, which may also be referred to as a transceiver unit, is used to implement the sending and/or receiving functions in any of the above aspects and any possible implementations thereof. The transceiver module may be composed of a transceiver circuit, a transceiver, a transceiver or a communication interface. The processing module may be used to implement the processing functions in any of the above aspects and any possible implementations thereof.

In some possible designs, the transceiver module includes a sending module and a receiving module, which are respectively used to implement the sending and receiving functions in any of the above aspects and any possible implementation methods thereof.

In a fourth aspect, a communication device is provided, comprising: at least one processor; the processor is used to execute a computer program or instruction so that the communication device executes the method described in any one of the above aspects.

In a possible implementation, the communication device further includes the memory. Optionally, the memory is coupled to the processor, the memory may be integrated with the processor, or the memory may be independent of the processor. Optionally, the processor is used to execute a computer program or instruction stored in the memory.

In a possible implementation, the memory is independent of the communication device.

In a possible implementation, the communication device further includes a communication interface, and the communication interface is used to communicate with a module outside the communication device.

The communication device may be the first device in the above-mentioned first aspect or any implementation manner thereof, or a device including the above-mentioned first device, or a device included in the above-mentioned first device, such as a chip; or, the communication device may be the second device in the above-mentioned second aspect or any implementation manner thereof, or a device including the above-mentioned second device, or a device included in the above-mentioned second device, such as a chip.

In a fifth aspect, a computer-readable storage medium is provided, in which a computer program or instruction is stored. When the computer-readable storage medium is run on a communication device, the communication device can execute the method described in any one of the above aspects or any one of its implementation methods.

In a sixth aspect, there is provided a computer program product comprising instructions, which, when executed on a communication device, enables the communication device to Execute the method described in any of the above aspects or any of its implementations.

In a seventh aspect, a communication device (for example, the communication device may be a chip or a chip system) is provided, wherein the communication device includes a processor for implementing the functions involved in any of the above aspects or any of its implementation methods.

In some possible designs, the communication device includes a memory for storing necessary program instructions and data.

In some possible designs, when the device is a chip system, it can be composed of a chip or include a chip and other discrete devices.

It can be understood that when the communication device provided in any one of the third aspect to the seventh aspect is a chip, the above-mentioned sending action/function can be understood as output, and the above-mentioned receiving action/function can be understood as input.

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

In an eighth aspect, a communication system is provided, comprising: the first device in the above-mentioned first aspect or any implementation thereof, and the second device in the above-mentioned second aspect or any implementation thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a channel state information CSI data compression method provided by an embodiment of the present application;

FIG2 is a schematic diagram of the structure of a communication system provided in an embodiment of the present application;

FIG3 is a flow chart of a data transmission method provided in an embodiment of the present application;

FIG4 is a schematic diagram of obtaining second data according to dimension configuration information provided by an embodiment of the present application;

FIG5 is a schematic diagram of a communication device structure provided in an embodiment of the present application;

FIG. 6 is a second schematic diagram of the structure of a communication device provided in an embodiment of the present application.

DETAILED DESCRIPTION

To facilitate understanding of the technical solutions provided by the embodiments of the present application, a brief introduction to the related technologies of the present application is first given. The brief introduction is as follows:

First, the dimension size of the data:

In the embodiment of the present application, dimension may refer to the number of dimensions of data, or the number of dimensions. Dimension size (size/dimension size) refers to the dimension size of the dimension. For example, the dimension size of data refers to the product between the dimension sizes of each dimension in multiple dimensions of data. For example, for two-dimensional data (or data matrix), the dimension of the data is two dimensions: the first dimension and the second dimension, and the dimension size of the data is the product between the dimension size L1 of the first dimension and the dimension size _L2 of the second dimension, i.e., _L1 × _L2 . For another example, for higher-dimensional data (or data tensors), the dimension of the data is M dimensions, and the dimension size of each dimension in the M dimensions is _L1 , _L2 , ..., Li, ..., _LM , _Li is the dimension size of the i-th dimension, and then the dimension size of the data is _L1 × _L2 × ... × _Li × ... × _LM , 1≤i≤M, _and M is an integer greater than 2.

It can be understood that different dimensions can correspond to different meanings. For example, for channel state information (CSI) data, it can correspond to three dimensions, namely: receiving antenna (RX) dimension, transmitting antenna (TX) dimension, and frequency domain dimension. Among them, the dimension size of the receiving antenna dimension can represent the number of antennas at the receiving end (or the number of receiving antenna ports), the dimension size of the transmitting antenna dimension can represent the number of transmitting antennas at the transmitting end (or the number of transmitting antenna ports), and the dimension size of the frequency domain dimension can represent the number of frequency domain units. The frequency domain unit can be, for example, a resource block (resource block, RB), a resource element (resource element, RE), or a subcarrier (subcarrier), etc., and the embodiments of the present application do not make specific limitations on this.

It should be understood that for a radio access network (RAN) device, the number of antennas may reach hundreds or more, such as 512 or 1024, and thus CSI data is usually high-dimensional data. Direct transmission of CSI data results in a large transmission overhead.

To this end, the 3rd generation partnership project (3GPP) provides a compression method to compress CSI data. The data compression process is introduced below.

Second, data compression process:

FIG1 is a CSI data compression flow chart provided in an embodiment of the present application. As shown in FIG1 , the compression process mainly includes:

S101. The transmitter performs singular value decomposition (SVD) on the two-dimensional data corresponding to each frequency domain unit in the three-dimensional CSI data H to obtain two-dimensional data corresponding to each rank in K ranks. The K ranks are obtained by reducing the RX dimension through two-dimensional SVD.

S102, the transmitting end performs discrete Fourier transform (DFT) codebook projection and dimensionality reduction operations on the two-dimensional data W corresponding to each rank to obtain a coefficient matrix W ₂ . The selected codebooks are matrix W ₁ and matrix W _f , respectively. Matrix W ₁ corresponds to the transmit antenna dimension, and matrix W _f corresponds to the frequency domain dimension. It can be understood that the two-dimensional data corresponding to each rank can be decomposed into matrix W ₁ , matrix W _f , and coefficient matrix W ₂ . For example, W = W ₁ W ₂ W _f ^H .

S103, sending the index of the basis vector in the matrix _W1 , the index of the basis vector in the matrix _Wf , and the indication information of the elements in the coefficient matrix _W2 . The indication information of the elements in the coefficient matrix _W2 can be used to indicate the value and position of the elements in the coefficient matrix _W2 .

It can be understood that the sending end can quantize the information sent in the above step S103, convert it into a compressed bit stream, and send the compressed bit stream.

It should be understood that the compression process shown in FIG. 1 above is to realize high-dimensional CSI data compression (or dimensionality reduction) by splitting the three-dimensional CSI data into multiple matrices and performing DFT codebook projection on each of the multiple matrices. However, in the case where the dimensional size difference between the RX dimension, TX dimension, and frequency domain dimension of the three-dimensional CSI data is large, the matrix of the three-dimensional CSI data split is a long and narrow matrix (that is, the dimensional size difference between the row dimension and the column dimension is large), resulting in a large amount of DFT projection operation and low data compression efficiency.

Based on this, an embodiment of the present application provides a data transmission method for providing an implementation scheme for high-dimensional data compression, which can improve the efficiency of data compression.

The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

In order to facilitate understanding of the embodiments of the present application, the following points are explained before introducing the embodiments of the present application.

1. In the embodiments of the present application, when a "set" or "combination" is involved, the objects included in the set or combination may be one or at least two. For example, a vector combination may include one vector or at least two vectors. For example, a dimension set may include one dimension or at least two dimensions.

2. In the embodiments of the present application, for the convenience of description, when numbering or indexing is involved, the numbering can be started from 1 or from 0, or from any parameter.

3. "Predefined", "pre-defined", "pre-configured (or pre-configured)", and "protocol agreement" can be used interchangeably, and pre-definition can be achieved by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in a device (for example, the first device or the second device). The embodiments of the present application do not limit the specific implementation method. Among them, "saved" can mean saved in one or more memories.

4. The “protocol” involved in the embodiments of the present application may refer to a standard protocol in the field of communications, such as the long term evolution (LTE) protocol, the new radio (NR) protocol, the wireless fidelity (Wi-Fi), and related protocols used in future communication systems (such as the sixth generation (6G) communication system), which is not limited in the embodiments of the present application.

5. In the embodiments of the present application, descriptions such as “when…”, “in the case of…”, “if” and “if” all mean that under certain objective circumstances, the device (such as the first device or the second device) will perform corresponding processing. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

6. In the embodiments of the present application, “sending information to...(first device)” can be understood as the destination of the information being the first device, and can include directly or indirectly sending information to the first device. “Receiving information from...(second device)” or “receiving information from...(second device)” can be understood as the source of the information being the second device, and can include directly or indirectly receiving information from the second device. The information may be processed as necessary between the source and destination of the information transmission, such as format changes, etc., but the destination can understand the valid information from the source. Similar expressions in the present application can be understood similarly and will not be repeated here.

7. In the description of the embodiments of the present application, unless otherwise specified, the "and/or" in the embodiments of the present application indicates that there may be three relationships, for example, A and/or B, which may indicate: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B may be singular or plural. Moreover, "at least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and the like are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art may understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not limit the certain difference. At the same time, in the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions.

The embodiments of the present application may be applicable to LTE systems or NR systems (also referred to as fifth generation (5th generation, 5G) systems), systems with hybrid networking of LTE and NR, vehicle to everything (V2X) systems, device-to-device (D2D) systems, machine to machine (M2M) communication systems, Internet of Things (IoT) systems (such as narrowband Internet of Things (NB-IoT) systems), Wi-Fi systems, non-terrestrial networks (NTN) systems, 6G systems, and other next generation communication systems. Alternatively, the communication system may also be an open radio access network (O-RAN or ORAN) or a cloud radio access network (CRAN), without limitation.

It can be understood that the embodiments of the present application can be applicable to a variety of different business scenarios, such as enhanced mobile broadband (eMBB), ultra-high reliability and ultra-low latency communication (URLLC), massive machine type communication (mMTC), immersive communication, massive communication, ubiquitous connections, integrated artificial intelligence and communication, or integrated sensing and communication, etc. In order to meet the further requirements of the above-mentioned different business application scenarios for latency, reliability, and coverage, more flexible resource allocation is needed.

In addition, the communication architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the evolution of the communication architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

As shown in FIG2 , a schematic diagram of the structure of a communication system 200 provided in an embodiment of the present application is provided. FIG2 takes the communication system 200 as an example, including at least one access network device (such as 210a or 210b in FIG2 ), and at least one terminal device (such as 220a to 220j in FIG2 ) connected to the access network device. It should be understood that the access network device can be connected to the core network (CN) wirelessly or wired, and the CN equipment and the access network device in the CN can be different physical devices, or can be the same physical device that integrates the CN logical function and the wireless access network logical function. It can be understood that the number of access network devices and terminal devices in FIG2 is only an example, and can be more or less, and the embodiment of the present application does not specifically limit this.

In a possible implementation, the access network device in the embodiment of the present application may be a device that communicates with the terminal device. The access network device may also be referred to as a RAN device, an access node, a RAN entity, or a RAN node, etc. As shown in FIG. 2 , multiple access network devices in the communication system 200 may be nodes of the same type or nodes of different types. In some scenarios, the roles of the access network device and the terminal device are relative. For example, the network element 220i in FIG. 2 may be a helicopter or a drone, which may be configured as a mobile base station. For those terminal devices 220j that access the communication system 200 through the network element 220i, the network element 220i may be a base station 210a; but for the base station 210a, the network element 220i is a terminal device. The access network device and the terminal device are sometimes referred to as communication devices. For example, the network elements 210a and 210b in FIG. 2 may be understood as communication devices with base station functions, and the network elements 220a-220j may be understood as communication devices with terminal functions.

In one possible scenario, the access network device may be a transmission and reception point (TRP), a base station, a remote radio unit (RRU) or a baseband unit (BBU) (also referred to as a digital unit (DU)) of a separate base station, a broadband network gateway (BNG), a convergence switch, a non-2GPP access device, a relay station or an access point, etc. The access network device may be a macro base station (e.g., the network element 210a in FIG. 2 ), a micro base station or an indoor station (e.g., the network element 210b in FIG. 2 ), a relay node or a donor node, or a wireless controller in a CRAN scenario. Optionally, the access network device may also be a server, a wearable device, a vehicle or an onboard device, etc. For example, the access network device in a V2X system may be a road side unit (RSU). In addition, the access network device in the embodiments of the present application can be an eNB or eNodeB (evolutional NodeB) in LTE, a wireless controller in a CRAN scenario, a base station in a 5G communication system (such as a next-generation Node B (gNodeB, gNB)), or a base station in a future evolution system (such as a 6G communication system), etc., without specific limitation herein.

In one possible implementation, in some deployments, the gNB may include a centralized unit (CU), a DU, a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The gNB may also include an active antenna unit (AAU). The CU implements some functions of the gNB, and the DU implements some functions of the gNB, for example, the CU is responsible for processing non-real-time protocols and services, and implementing the functions of the radio resource control (RRC) and/or packet data convergence protocol (PDCP) layers. The DU is responsible for processing physical (PHY) layer protocols and real-time services, and implementing the functions of the radio link control (RLC) layer, the media access control (MAC) layer, and the PHY layer. The AAU implements some physical layer processing functions, RF processing, and related functions of active antennas. Since the information of the RRC layer will eventually become the information of the PHY layer, or be converted from the information of the PHY layer, therefore, under this architecture, high-level signaling, such as RRC layer signaling, can also be considered to be sent by DU, or, sent by DU+AAU. It can be understood that the access network device can be a device including one or more of a CU node, a DU node, and an AAU node. In addition, the CU can be divided into an access network device in the RAN, and the CU can also be divided into an access network device in the CN, and the embodiments of the present application are not limited to this.

In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in the 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, the embodiments of the present application are described by taking CU, CU-CP, CU-UP, DU and RU as examples. Any unit of CU (or CU-CP, CU-UP), DU and RU in the embodiments of the present application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

In a possible implementation, the terminal device in the embodiment of the present application may be a device for implementing a wireless communication function, such as a terminal or a chip that can be used in a terminal, etc. Among them, the terminal may be a user equipment (UE), an access terminal, a terminal unit, a terminal station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, or a terminal agent, etc. in a 5G network or a future evolved public land mobile network (PLMN). The access terminal may be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with a wireless communication function, a computing device, or a device connected to a wireless modem. Other processing devices of the device, vehicle-mounted devices, wearable devices, VR terminal devices, AR terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical, wireless terminals in smart grid, wireless terminals in transportation safety, wireless terminals in smart city, wireless terminals in smart home, etc. In a possible implementation, the terminal device can be mobile or fixed, which is not limited.

It can be understood that the above-mentioned communication system 200 can support a variety of different business application scenarios, such as enhanced mobile broadband (eMBB), ultra-high reliability and ultra-low latency communication (URLLC), massive machine type communication (mMTC), immersive communication, massive communication, ubiquitous connections, integrated artificial intelligence and communication, or integrated sensing and communication, etc., and the embodiments of the present application do not make specific limitations on this.

The embodiment of the present application provides an information transmission method, and the execution subject of the method may be a first device. The first device may be the terminal device in FIG2, or a module or unit of the terminal device (such as a chip, a chip system, a chip circuit, or a circuit, etc. of the terminal device), or an access network device, or a module or unit of the access network device (such as a chip, a chip system, a chip circuit, or a circuit, etc. of the access network device).

In a possible implementation, a first device obtains first data, the dimension of the first data is M dimensions, M is an integer greater than 1; the first device determines the dimension configuration information corresponding to the first data, the dimension configuration information is used to indicate the dimension size of each dimension in the N dimensions of the second data, the number of elements contained in the second data and the first data is the same, the N dimensions are the M dimensions, and the dimension sizes corresponding to at least some dimensions between the N dimensions and the M dimensions are different, or N is different from M, N is a positive integer; the first device determines the second data according to the first data and the dimension configuration information; the first device sends the third data, the third data is the data after the second data is compressed. In this way, the first device can arrange the dimensions of the first data to obtain the second data before compressing the first data through the dimension configuration information of the first data, so that the number of dimensions or the dimension size of at least some dimensions between the second data and the first data is different, and then compress the second data, which can reduce the amount of data compression calculations and improve the efficiency of data compression.

The above method provided in the embodiment of the present application will be described in detail below with reference to FIG. 3 to FIG. 4 .

It should be understood that the signals between various devices or apparatuses, the names of various parameters in the signals, or the names of the information carried by the signals in the following embodiments of the present application are merely examples, and other names may also be used in specific implementations, and the embodiments of the present application do not specifically limit this.

In addition, the method provided in the embodiment of the present application can be applicable to the interaction between the first device and the second device. Among them, the first device can be the terminal device in Figure 2 above, or a module or unit of the terminal device (such as a chip, a chip system, a chip circuit, or a circuit of the terminal device, etc.), and the second device can be the access network device in Figure 2 above, or a module or unit of the access network device (such as a chip, a chip system, a chip circuit, or a circuit of the access network device, etc.). Alternatively, the first device can be the access network device in Figure 2 above, or a module or unit of the access network device, and the second device can be the terminal device in Figure 2 above, or a module or unit of the terminal device.

The first device and the second device may operate in a high frequency band, such as a millimeter wave band or a terahertz band, or in a low frequency band, such as a 700 MHz, 900 MHz, 2.1 GHz, 2.6 GHz, or 3.5 GHz band, etc. It is understood that the first device and the second device may also operate in other frequency bands supported by the 6G system, and the embodiments of the present application do not specifically limit this.

It can be understood that the first device can operate in an RRC activated state, an RRC inactivated state, an RRC idle state, or other RRC states or RRC modes defined in the 6G communication system, and the embodiments of the present application do not specifically limit this.

For ease of understanding, the following takes the interaction between the first device and the second device as an example to explain in detail the data transmission method process shown in FIG. 3 .

FIG3 is a flow chart of a data transmission method provided by an embodiment of the present application. As shown in FIG3 , the method includes the following steps:

S301: A first device obtains first data, wherein the first data has M dimensions, and M is an integer greater than 1.

S302: The first device determines dimension configuration information corresponding to the first data, wherein the dimension configuration information is used to indicate the dimension size of each dimension of N dimensions of the second data, the second data and the first data contain the same number of elements, and N is a positive integer.

S303: The first device determines the second data according to the first data and the dimension configuration information.

S304: The first device sends third data to the second device. Correspondingly, the second device receives the third data from the first device. The third data is the data after the second data is compressed.

S305. The second device obtains the dimension size and dimension configuration information of each dimension in the M dimensions.

S306. The second device determines fourth data according to the third data, the dimension configuration information, and the dimension size of each dimension in the M dimensions.

It can be understood that the third data includes data compressed from the first data, and the fourth data includes data decompressed from the third data, that is, the fourth data may correspond to the first data.

The above steps S301 to S306 are described in detail below.

Corresponding to step S301:

It can be understood that the first data may be two-dimensional data, three-dimensional data, or data with more dimensions. The dimension size of the first data is the product of the size of each dimension in the M dimensions, that is, L ₁ ×…×L _i ×…×L _M , 1≤i≤M, and M is an integer greater than 1.

In an embodiment of the present application, the first data may be channel data (such as CSI data), perception data, imaging data, artificial intelligence AI data, or model data, etc., without specific limitation.

Exemplarily, for channel data, the dimensions of the channel data may include: RX dimension, TX dimension, frequency domain dimension, or time domain dimension, etc. Among them, the dimension size of the time domain dimension may refer to the number of time domain units, and the time domain unit may be a time domain unit of different granularities, such as a time slot or a symbol, etc. It can be understood that the dimension of the channel data may also include a Doppler dimension, which may be used to indicate the change of the channel over time, such as the Doppler frequency corresponding to the multipath component.

The perception data is similar to the channel data, except that the perception data can be pre-processed data. For example, for lidar data, the dimensions of the perception data can include the three-dimensional coordinate dimension of the lidar, the laser reflection intensity dimension, or the laser wavelength dimension. It can be understood that the perception data can be called point cloud data after processing, and the point cloud data can also be used for positioning, so the dimensions of the perception data can also include the geographic location dimension.

The imaging data is usually two-dimensional data, which may include a target echo delay dimension (corresponding to the target distance) and a frequency deviation dimension (corresponding to the target speed). Of course, the imaging data may also be three-dimensional data, which is a higher dimensional data, and the embodiments of the present application do not specifically limit this.

The dimensions of AI data or model data are related to features (or channels). For example, with respect to AI data of images, the dimensions of AI data may include: the number of images, the number of pixels in the length direction of the image, the number of pixels in the width direction of the image, and the number of channels of the image (e.g., RGB channels).

In a possible implementation, the first device may generate the first data. The first device may obtain the first data by collecting signals. For example, the first device may obtain the perception data by receiving an echo signal. It is understood that the first device may also obtain multiple image data through a sensor.

It is understandable that the first data may be processed data. For example, the first device may collect the sensing signal and perform preprocessing to generate the first data in the form of a point cloud.

In another possible implementation, the first device may obtain the first data from other devices, where the other devices may be, for example, other terminal devices, the second device, or network elements in a core network element, etc., which is not specifically limited in the embodiment of the present application.

For example, the first device may send a request message to other devices (or network elements) to request to obtain the first data, and then receive the first data from other devices. It is understandable that other devices may also send the first data directly to the first device, and the embodiments of the present application do not specifically limit this.

For step S302:

It can be understood that the second data is the data after the first data is dimensionally arranged. The dimension arrangement can refer to changing the number of dimensions of the first data, or changing the dimensional size of at least some of the M dimensions of the first data, and the number of elements contained in the first data and the second data are the same, that is, the dimensional size (or data size) between the two is the same. Among them, the number of dimensions of the first data is changed, so that N is different from M. The number of dimensions of the first data is not changed (that is, N=M), and the dimensional size of at least some of the M dimensions is changed, so that the dimensional size of at least some of the M dimensions is different from that of the N dimensions. For example, at least some of the dimensions can be 2 dimensions, 3 dimensions, or all dimensions of the M dimensions.

Further, optionally, the elements included in the first data and the second data may be the same, that is, the values of the elements included in the first data may not be changed when the first data is dimensionally arranged, so that the amount of data arrangement calculation can be reduced.

Alternatively, optionally, the elements contained between the first data and the second data are in a linear relationship. That is to say, the first device can perform an overall linear transformation on the first data to obtain the second data, so as to achieve preprocessing of the first data, facilitate subsequent compression processing, and improve the efficiency of compression processing. It can be understood that the linear relationship may refer to the linear transformation of the element a in the first data, which is transformed into the element b=xa+y of the second data. Of course, the above-mentioned linear relationship includes normalizing the elements in the first data to obtain the elements in the second data, or other linear processing, which is not specifically limited in the embodiments of the present application.

It can be understood that the above-mentioned dimensional arrangement, by changing the number of dimensions of the first data, or changing the size of at least some of the M dimensions of the first data, obtains the second data, which can improve the efficiency of the candidate first device in compressing the second data.

For ease of understanding, the following first introduces the compression process and then introduces the dimension size of each dimension in the N dimensions.

A. Compression processing:

It should be understood that in the embodiments of the present application, compression processing (or data compression) refers to compressing data to reduce the dimensional size of the data (i.e., the amount of data, or the number of elements). Reducing the dimensional size of the data may include: the number of dimensions of the data remains unchanged, but Reduce the size of at least some dimensions; or, reduce the number of dimensions of the data.

It can be understood that the compression process may include: transformation, dimensionality reduction, or decomposition. Among them, transformation may refer to projection (such as the DFT projection shown in Figure 1); or, transformation may refer to feature extraction (such as convolution), which is not specifically limited in the embodiments of the present application. In addition, the dimensional arrangement in the embodiments of the present application is different from the transformation. The difference is that the value of the element will change nonlinearly after the data is projected, that is, the element after projection is actually the result of the interaction between multiple elements in the column vector or row vector where the element is located, which reflects the correlation between multiple elements, rather than the value of the element itself.

Dimensionality reduction may refer to: for data that has been transformed, reducing the size of one or more dimensions of the data, such as pooling after convolution, or reducing the dimension of the transformed data through a dimensionality reduction matrix.

Decomposition may include two-dimensional decomposition or high-dimensional decomposition. Among them, two-dimensional decomposition may refer to expanding high-dimensional data into multiple two-dimensional matrices, and then decomposing the two-dimensional matrices, similar to the above-mentioned transformation followed by dimensionality reduction, and specifically refer to the method shown in Figure 1. High-dimensional decomposition may refer to directly performing a high-order decomposition on high-dimensional data to obtain a core tensor and multiple orthogonal matrices, the number of dimensions of the core tensor is the same as the number of dimensions of the dimensional data, but the dimension size of one or more dimensions of the core tensor is smaller than the dimension size of the corresponding dimension in the high-dimensional data (i.e., the original dimension size).

Alternatively, decomposition may refer to reducing the dimension of high-dimensional data through a dimension reduction matrix, and then performing tensor expansion to obtain a feature matrix corresponding to a specified dimension, and performing orthogonal decomposition on the feature matrix to obtain an orthogonal matrix corresponding to the specified dimension, so that the first device can obtain a compressed core tensor through the product of one or more orthogonal matrices corresponding to the specified dimensions and the high-dimensional data. For example, tensor multiplication is performed on the high-dimensional data and the dimension reduction matrix to reduce the dimension size of one or more dimensions in the high-dimensional data. For example, the first data includes M=3 dimensions, the dimension size of the first dimension is L ₁ , the dimension size of the second dimension is L ₂ , and the dimension size of the third dimension is L ₃ , and the row dimension×column dimension of the dimension reduction matrix is L ₁ ×L ₁ '. In this way, the first data is subjected to tensor mode j multiplication with the conjugate transposed matrix of the dimension reduction matrix to obtain a dimension size of the reduced feature tensor of L ₁ '×L ₂ ×L ₃ , and the feature tensor is further expanded for the second dimension to obtain a feature matrix corresponding to the second dimension of L ₂ ×(L ₁ '×L ₃ ), and/or the feature tensor is expanded for the third dimension to obtain a dimension size of L ₃ ×(L ₁ '×L ₂ ), so the orthogonal matrix corresponding to the second dimension can be obtained according to the feature matrix corresponding to the second dimension, and the orthogonal matrix corresponding to the third dimension can be obtained according to the feature matrix corresponding to the third dimension, and based on the high-dimensional data, the orthogonal matrix corresponding to the second dimension, and the orthogonal matrix corresponding to the third dimension, the core tensor can be obtained.

Alternatively, decomposition may also mean first performing tensor expansion on the high-dimensional data to obtain two-dimensional data corresponding to each dimension in one or more dimensions, and then obtaining the feature matrix corresponding to each dimension based on the two-dimensional data corresponding to each dimension and the dimensionality reduction matrix corresponding to each dimension. In this way, the first device can obtain the core tensor by multiplying the orthogonal matrix of the feature matrix corresponding to each dimension with the high-dimensional data.

It should be understood that the multiplication operation between the above-mentioned high-dimensional data (ie, tensor) and the dimension reduction matrix can also be a Kronecker product or a Khatri–Rao product, etc., which is not specifically limited in the embodiments of the present application.

It should also be understood that the name of the tensor expansion in the embodiments of the present application is only an example, and the tensor expansion can also be replaced by matrix expansion, tensor expansion by dimension, tensor mode-n expansion, or matricization, etc. The embodiments of the present application do not make specific limitations on this.

It can be understood that the above compression processing may also include quantization, that is, quantizing the compressed data into a compressed bit stream for easy transmission.

B. The dimension size of each dimension in N dimensions:

It should be understood that N≤M, the N dimensions are N dimensions among the M dimensions; or, N＜M, the first dimension among the N dimensions corresponds to multiple dimensions among the M dimensions; or, N＞M, the N dimensions include M dimensions.

It can be understood that, in the case of N=M, N dimensions are M dimensions, that is, the N dimensions in the second data still use the dimensions in the M dimensions, for example, the M dimensions are RX dimensions, TX dimensions, and frequency domain dimensions, and the N dimensions of the second data include the above-mentioned RX dimensions, TX dimensions, and frequency domain dimensions, but the dimension size of each dimension in at least one of the dimensions has changed. For example, the dimension size of the RX dimension of the first data is 16, the dimension size of the TX dimension is 256, and the dimension size of the frequency domain dimension is 64. The dimension size of the first dimension of the second data is 64, including the information of the RX dimension and part of the TX dimension of the first data, the dimension size of the second dimension of the second data is 64, including the information of part of the TX data of the first data, and the dimension size of the third dimension of the second data is 64, corresponding to the frequency domain dimension of the first data, that is, the dimension arrangement changes the dimension size of the RX dimension and the dimension size of the TX dimension in the M dimensions of the first data.

It should be understood that by changing the size of each of the M dimensions, the efficiency of the compression process can be improved. For example, for the DFT projection in the above transformation, by arranging the size of each of the M dimensions in the first data to be similar, the first device can use a fast DFT projection algorithm to make changes, which can improve the efficiency of the compression process. For another example, for the above convolution, convolution is an operation that extracts features through a sliding window. If the size of each dimension of the convolution input data is similar, the amount of convolution operations can be reduced, thereby improving the efficiency of the compression process.

For another example, in the above compression process, by reducing the dimension through the dimension reduction matrix, the dimension size of each dimension in the M dimensions is changed so that the dimension of the dimensionally arranged data matches the dimension of the dimension reduction matrix (for example, the row dimension or the column dimension), so that the compression can be performed without loss. Based on the effect (changing the dimension of the dimensionality reduction matrix (for example, by adding zero elements) will affect the correlation between the features corresponding to the dimension), the computational complexity of the multiplication (for example, the tensor multiplication or matrix multiplication mentioned above) between the two is reduced, thereby improving the compression efficiency.

For N＜M, the N dimensions may be N dimensions among the M dimensions. For example, when N is equal to 2, the N dimensions may be RX&frequency domain dimension and TX dimension, or RX&TX dimension and frequency domain dimension, or TX&frequency domain dimension and RX dimension. Wherein, & represents the meaning of and.

It can be understood that for the reduction of M dimensions to N dimensions, the elements contained in each dimension of at least one of the N dimensions actually correspond to the elements of other dimensions in the M dimensions except the N dimensions. For example, the first device can merge the elements of other dimensions in the M dimensions except the N dimensions into the elements corresponding to each dimension in at least one of the N dimensions. For example, the N dimensions in the above example are the RX dimension and the TX dimension, and the elements corresponding to the RX dimension and/or the TX dimension actually also contain information corresponding to the frequency domain dimension. In this way, the physical meaning corresponding to each dimension in the N dimensions in the M dimensions can be kept unchanged, so that subsequent compression processing can be performed according to the physical meaning corresponding to the dimension.

When N＜M, the first dimension of the N dimensions corresponds to multiple dimensions of the M dimensions, that is, multiple dimensions of the M dimensions can be merged, thereby reducing the M dimensions of the first data to N dimensions. For example, in the above example, N is equal to 2, the RX dimension can be retained, the TX dimension and the frequency domain dimension can be merged, and a new dimension (i.e., the first dimension) can be obtained, and the first dimension corresponds to the TX dimension and the frequency domain dimension.

It can be understood that the first dimension can be any one of the N dimensions, and the embodiment of the present application does not specifically limit this.

It can also be understood that the N dimensions may include multiple first dimensions. For example, the M dimensions of the first data are the 1st to 4th dimensions, and the N dimensions include two first dimensions, one of which corresponds to the 1st dimension and the 2nd dimension respectively, and the other first dimension corresponds to the 3rd dimension and the 4th dimension respectively.

It should be understood that when N is less than M, the number of dimensions of the first data can be reduced through dimensional arrangement, and then the complexity of decomposition (such as high-order SVD decomposition) can be reduced when the first device performs subsequent compression processing, thereby improving the efficiency of the compression processing.

Furthermore, the dimensional size of each dimension in the above N dimensions may be close to each other, or the dimensional size corresponding to at least some of the dimensions in the N dimensions may correspond to the row dimension or column dimension of the dimensionality reduction matrix, thereby improving the compression efficiency.

For N>M, N dimensions include M dimensions, that is, the dimension arrangement can add a dimension and the dimension size corresponding to the dimension on the basis of the M dimensions, so as to improve the compression effect. For example, the added dimension can be a sub-dimension with high correlation corresponding to one of the M dimensions. By adding the sub-dimension with high correlation to the second data, the correlation of the sub-dimension can be fully utilized during the subsequent compression processing, reducing redundancy and improving the compression effect.

Exemplarily, the first data is downlink CSI data, which corresponds to the RX dimension, TX dimension, and frequency domain dimension, wherein the TX dimension is 1024, and the transmitting antenna of the access network device can be divided into multiple associated sub-arrays, so that the sub-array dimension can be increased for the first data through dimensional arrangement, and then the correlation of the data within each sub-array can be more fully utilized, and when the second data is compressed, redundancy is reduced and the compression effect is improved.

Furthermore, the size of the subarray dimension is determined according to the number of subarrays in the TX dimension. For example, if the TX dimension is equal to 1024, the number of antennas of the access network device is 1024, which can be divided into 4 subarrays, and thus the size of the subarray dimension is 4.

In a possible implementation, the dimension configuration information is associated with at least one of the following: information on compression processing, and/or sub-dimension information corresponding to the second dimension of the M dimensions. That is, the dimension size of each dimension of the N dimensions indicated by the dimension configuration information is associated with the information on compression processing and/or sub-dimension information corresponding to the second dimension of the M dimensions, thereby matching the dimension size of the N dimensions and each dimension of the N dimensions with the compression processing type of subsequent compression processing and/or the dimension size of the sub-dimension corresponding to the second dimension, thereby improving the effect of the compression processing and the efficiency of the compression processing.

It should be understood that the second dimension can be any one of the M dimensions, and the embodiment of the present application does not specifically limit this.

Further, the M dimensions may include multiple second dimensions and sub-dimension information corresponding to the second dimensions, that is, the N dimensions may add sub-dimensions corresponding to multiple dimensions relative to the M dimensions. Among them, the sub-dimension information corresponding to the second dimension may include the physical meaning corresponding to the sub-dimension and the dimension size of the sub-dimension. For details, please refer to the sub-array of the transmitting antenna of the above-mentioned access network device, which will not be repeated here.

For example, the information of the compression processing includes the type of compression processing and the dimensional size of the row dimension of the dimension reduction matrix used for the compression processing, and/or the dimensional size of the column dimension of the dimension reduction matrix. For example, for the compression processing as transformation, the dimensional size between each dimension of the N dimensions indicated by the dimension configuration information can be the same or tend to be the same, so that the amount of transformation calculation can be reduced, thereby improving the efficiency of the compression processing. For another example, for the compression processing as decomposition, the N dimensions can be smaller than the M dimensions, thereby reducing the computational complexity of the decomposition (such as high-order SVD), thereby improving the efficiency of the compression processing. For another example, for the case where the compression processing requires dimensionality reduction through a dimensionality reduction matrix, the dimensional size corresponding to some dimensions in the N dimensions is the same as the row dimension or column dimension of the dimensionality reduction matrix, thereby improving the compression efficiency on the basis of losing the compression effect. For another example, for the M dimensions, there is a sub-dimension corresponding to the second dimension, the N dimensions can be larger than the M dimensions, and the newly added dimension in the N dimensions corresponds to the sub-dimension, and then when the first device performs compression processing on the second data, the correlation of the sub-dimensions can be used, Reduce redundancy and improve compression effect.

It can be understood that the sub-dimension information corresponding to the M dimensions can be pre-configured, or negotiated in advance by the network side (for example, the second device), or the first device and the second device. The embodiments of the present application do not specifically limit this.

It should be understood that the above-mentioned dimensional sizes between each dimension in the N dimensions tend to be the same may mean that the difference or ratio of the dimensional sizes between each dimension is small, which is explained in detail below.

In a possible implementation, the difference or ratio of the dimensional sizes between any two dimensions in the N dimensions is less than the first threshold value. That is, the difference or ratio between the maximum dimensional size and the minimum dimensional size in the N dimensional sizes corresponding to the N dimensions is less than the first threshold value, and then the sizes of the N dimensions tend to be the same, so as to facilitate subsequent compression processing and improve the efficiency of the compression processing.

Among them, the first threshold can be 20% or 40% or a larger percentage of the arithmetic mean dimensional size corresponding to the M dimensions, or the first threshold can be 20% or 40% or a larger percentage of the difference or ratio between the maximum dimensional size and the minimum dimensional size among the M dimensional sizes corresponding to the M dimensions, which is not specifically limited in the embodiments of the present application.

In one possible implementation, the difference or ratio between the dimensional size of each dimension in the N dimensions and the geometric mean dimensional size corresponding to the M dimensions is less than or equal to a second threshold; or, the sum of the deviations corresponding to each dimension in the N dimensions is less than or equal to the second threshold, and the deviations corresponding to each dimension in the N dimensions are determined based on the ratio between the dimensional size of each dimension in the N dimensions and the geometric mean dimensional size.

It can be understood that the geometric mean dimension size corresponding to M dimensions can be expressed as Among them, the dimensional size of each dimension in the N dimensions is close to the geometric mean dimensional size corresponding to the M dimensions, which may mean that the dimensional size of each dimension in the N dimensions is close to the geometric mean dimensional size, so that the dimensional size of each dimension in the N dimensions is close to or the same as each other.

It can also be understood that the sum of the deviations corresponding to each dimension in the N dimensions is less than or equal to the second threshold, which may mean that each dimension in the N dimensions may be different in size from the geometric mean dimension corresponding to the M dimensions, but the sum of the accumulated deviations should be less than the second threshold.

It can also be understood that the second threshold may be 20%, or 40%, or a larger percentage of the geometric mean dimension size corresponding to the M dimensions, and this embodiment of the present application does not specifically limit this.

That is to say, the dimensional size of each dimension in the N dimensions is close to the geometric mean dimensional size corresponding to the M dimensions, or the dimensional sizes corresponding to some dimensions in the N dimensions may be different from the geometric mean dimensional size, but the overall deviation between the N dimensional sizes corresponding to the N dimensions and the geometric mean dimensional size is small, thereby improving the flexibility of determining the size of each dimension in the N dimensions to adapt to different types of compression processing.

It should be understood that in the embodiments of the present application, the dimension configuration information may be determined by the first device; or, the dimension configuration information may be indicated by the network side (e.g., the second device). For example, when the dimension size of the first data changes dynamically and the first device does not send the dimension size of the first data to the second device, the first device may determine the dimension configuration information. For another example, when the dimension size of the first data does not change, the first device may use the dimension configuration information indicated by the network side.

It can be understood that the above examples of dimension configuration information being determined by the first device or indicated by the network side are merely illustrative, and the embodiments of the present application do not specifically limit this.

It should be understood that for the specific implementation of the network side indication dimension configuration, please refer to step S309 below, which will not be repeated here.

The specific implementation of determining dimension configuration information by the first device is introduced below.

In a possible implementation, the dimension configuration information is determined according to the dimension size of each dimension in the M dimensions and the dimension arrangement rule. That is, the first device can determine the dimension configuration information according to the dimension arrangement rule and the size of each dimension in the M dimensions.

Optionally, the dimension arrangement rule includes at least one of the following: a corresponding method of dimension arrangement, and/or a corresponding relationship between N dimension sizes and N dimensions. Among them, the corresponding method of dimension arrangement may include dimension arrangement based on prime number decomposition, and dimension arrangement based on reference dimension size. The corresponding relationship between N dimension sizes and N dimensions can be used to: indicate the corresponding relationship between the N dimension sizes of the dimension arrangement and the N dimensions of the second data, so that the first device can determine the dimension size of each dimension in the N dimensions according to the corresponding relationship.

It can be understood that the dimension arrangement rule can be pre-configured, or negotiated in advance between the first device and the second device, or indicated by the second device, and the embodiments of the present application do not specifically limit this.

The following introduces the dimension arrangement based on prime number decomposition and the dimension arrangement based on the reference dimension size.

For dimensional arrangement based on prime number decomposition:

Dimension arrangement based on prime number decomposition can include the following steps:

Step S1: Prime number decomposition is performed on the product of the sizes of the M dimensions corresponding to the M dimensions (ie, L ₁ ×L ₂ ×…×L _M ) to obtain

Step S2: Calculate the geometric mean dimension size corresponding to M dimensions

Step S3: determine the sizes of N dimensions: L ₁ ', L ₂ ', ... L _N '.

Wherein, step S3 includes step S3-1 and step S3-2.

Step S3-1, calculate L ₁ ': If I ₂ ≥I ₂ ', then Otherwise calculate If I ₃ ≥I ₃ ', then Otherwise keep trying;

Step S3-2, calculate L _N ': exclude the prime numbers used by L ₁ ', ..., L _N-1 ', and try to use the remaining prime numbers in order from small to large to form The number closest to the magnitude.

Exemplarily, the above-mentioned dimensional arrangement based on prime number decomposition is explained by taking N=M as an example.

Assume that the M dimensions of the first data are 3 dimensions, the dimension size of the first dimension is L ₁ =32, the dimension size of the second dimension is L ₂ =1024, and the dimension size of the third dimension is L ₃ =10, and then according to step S1, N=2 ¹⁶ ×5 ¹ is obtained.

According to step S2, determine

According to step S3-1, calculate L ₁ ': determine Satisfies less than I ₂ = 16, and then

Calculate L ₂ ': Exclude the prime numbers used in L ₁ ', and we have (L ₁ ×L ₂ ×L ₃ )/L ₁ '＝2 ¹⁰ ×5 ¹ . Pick 6 2s from the remaining prime number combinations to get L ₂ '＝64;

Calculate L ₃ ': Exclude the prime numbers used by L ₁ ' and L ₂ ', and we have (L ₁ ×L ₂ ×L ₃ )/(L ₁ '×L ₂ ')＝2 ⁴ ×5 ¹ . Pick out 4 2s and 1 5 from the remaining prime number combinations, and we get L ₃ '＝80;

Finally, we get N dimension sizes: 64, 64, and 80.

It can be understood that the first device can randomly allocate the above three dimensional sizes to the three dimensions, or the first device can allocate the above three dimensional sizes to the three dimensions according to the correspondence between the N dimensional sizes and the N dimensions. For example, the correspondence between the N dimensional sizes and the N dimensions can be: allocate the N dimensional sizes after dimensional arrangement according to the original dimensional sizes. For example, if the original dimensional size of the second dimension is larger, the dimension size 80 can be preferentially allocated to the second dimension. In this way, the dimension with a larger original dimension can be preferentially allocated to the larger dimensional size after arrangement, thereby avoiding allocating the larger dimensional size after arrangement to the dimension with a smaller original dimension, thereby avoiding the original dimension with a larger dimension being split into 2 or more dimensions after dimensional arrangement, increasing redundancy and affecting the compression effect. For dimensional arrangement based on reference dimension size:

The dimension arrangement based on the reference dimension size is similar to the dimension arrangement based on prime number decomposition mentioned above, except that when calculating L ₁ ', the closest The prime power of The closest prime power is 2 ⁶ = 64, and thus L ₁ ' = 64. For another example, The closest prime power is 5 ³ = 125.

It can be understood that the calculation of L ₂ ′ and the subsequent L _N ′ is similar to the above-mentioned dimensional arrangement based on prime number diversity, and will not be described in detail.

It should be understood that the above determination method of the dimension configuration is for an example where N=M and the dimension sizes of each dimension in the N dimensions tend to be the same. For N<M, the above Modified to For N＞M, the dimension information corresponding to the newly added dimension can be determined according to the dimension size of the sub-dimension of the second dimension, and the other M dimensions can be determined by the above-mentioned dimension arrangement based on prime number decomposition and/or the dimension arrangement based on the reference dimension size.

It can be understood that the dimension size of each dimension in the N dimensions may not be the same, for example, the N dimensions are the same as the row dimension (or column dimension) of the dimensionality reduction matrix, so the first device can also determine the dimension configuration information based on the information of subsequent compression processing.

It should be understood that the above-mentioned implementation of the first device determining the dimension configuration information is only an example, and other methods can also be used to determine the dimension configuration information, and the embodiments of the present application do not specifically limit this.

For step S303:

FIG4 is a schematic diagram of obtaining second data according to dimension configuration information provided by an embodiment of the present application. As shown in FIG5, the dimension size of each of the M dimensions of the first data is respectively: L ₁ , L ₂ , L ₃ . For N=M, that is, the number of dimensions remains unchanged, and at least part of the M dimensions changes. Further, the arranged data is obtained in the access order of dimension 1, dimension 2, and dimension 3. The specific operation is to split each element of the third dimension into two matrices in dimension 1 and dimension 2, and then splice them in dimension 3 in sequence, so that the dimensions of the arranged data are L ₁ '=L ₁ , L ₂ '=L ₁ /2, L ₃ '=2L ₃ .

It can be understood that the access order of the above-mentioned dimensions can be determined by the first device or indicated by the second device, which can increase the flexibility of the first device in determining the second data according to the dimension configuration information.

For example, as shown in Table 1, the second device may configure the first device with an access order indicating the dimension arrangement, so that the first device may determine which dimension to perform dimension arrangement for first according to the indication information.

Table 1

It can be understood that when N is less than M, the first device can determine which dimensions of the M dimensions correspond to the first dimension in N according to the dimension configuration information, and then obtain the second data by merging these dimensions into the first dimension. For example, for the above example, the dimension configuration can indicate the dimension size of dimension 1 and the dimension size of dimension 4. Among them, the dimension size of dimension 1 remains unchanged, and then the first device can determine to merge dimension 2 and dimension 3 into 1 dimension, and the dimension size of the merged dimension is L ₄ =L ₂ ×L ₃ of dimension 4, and then the above three-dimensional first data is transformed into two-dimensional second data after dimension arrangement, and the dimension size of the second data is: L ₁ ×(L ₂ ×L ₃ ).

For another example, continuing the above example, the dimension size of dimension 1 is L ₁ /2, and the dimension size of dimension 4 is 2×(L ₂ ×L ₃ ), so the dimension size of the second data is:

In a possible implementation, the first device determines the second data (i.e., step S303) based on the first data and the dimension configuration information, including: when the evaluation parameters corresponding to the M dimensions are greater than or equal to the third threshold, determining the second data based on the first data and the dimension configuration. That is, when the M dimensions of the first data satisfy the evaluation parameter greater than or equal to the third threshold, the first device can determine that the M dimensions of the first data do not match the subsequent compression processing, and then arrange the dimensions of the first data to facilitate the subsequent compression processing and improve the compression efficiency.

In a possible implementation, the evaluation parameter includes a first type parameter, and/or a second type parameter, and the third threshold includes a third threshold corresponding to the first type parameter, and/or a third threshold corresponding to the second type parameter. The first type parameter is the sum of the deviations corresponding to each dimension in the M dimensions, and the deviations corresponding to each dimension in the M dimensions are determined based on the dimensional size of each dimension in the M dimensions and the geometric mean dimensional size corresponding to the M dimensions. The second type parameter is the difference or ratio between the maximum dimensional size and the minimum dimensional size among the M dimensional sizes corresponding to the M dimensions. In other words, the first device can determine whether to dimensionally arrange the first data based on at least two types of parameters, thereby improving the flexibility of the first device in determining whether to dimensionally arrange the first data.

For example, for M dimensions, the geometric mean dimension size Then the deviation corresponding to the i-th dimension in M dimensions is the dimension size _Ni of the i-th dimension and The ratio between, or the logarithm of the ratio (e.g. Or difference. Further, the first type parameter _P1 is

For another example, the second type parameter _P2 is the difference or ratio between the maximum dimension size max({L ₁ , L ₂ , …, L _M }) among the M dimension sizes and the minimum dimension size min({L ₁ , L ₂ , …, L _M }) among the M dimension sizes.

It can be understood that the third threshold corresponding to the first type parameter can take a value of 0.5, 1.0, 1.5, or a larger value, and the embodiment of the present application does not specifically limit this.

For the second type parameter which is the difference between the maximum dimension size and the minimum dimension size, the third threshold corresponding to the second type parameter can be 40, 80, 128, or a larger value, etc., which is not specifically limited in the embodiment of the present application.

It should be understood that the above determination by the first device whether to arrange the first data in dimensions based on whether the evaluation parameter is greater than or equal to the third threshold is merely an example. The first device may also determine whether to arrange the first data in dimensions based on other conditions, such as whether the dimensionality reduction matrix used in the compression process matches the dimension of the first data, or whether there is sub-dimensional information in a dimension of the M dimensions, etc. The embodiments of the present application do not specifically limit this.

It can be understood that the type of the above-mentioned evaluation parameters and the third threshold value corresponding to the evaluation parameters can be pre-configured, or negotiated in advance between the first device and the second device, or indicated by the network side (for example, the second device), and the embodiments of the present application do not specifically limit this.

Optionally, the method shown in FIG3 further includes:

S307: The second device sends first indication information to the first device. Correspondingly, the first device receives the first indication information from the second device. The first indication information is used to indicate the type of the evaluation parameter and the third threshold value corresponding to the evaluation parameter.

That is to say, the second device can indicate the type of evaluation parameter and the third threshold corresponding to the evaluation parameter to the first device, so that the first device can determine the evaluation parameter and the third threshold corresponding to the evaluation parameter based on the first indication information, thereby increasing the flexibility of the first device in determining whether to dimensionally arrange the first data to suit different scenarios.

For example, the second device may indicate its desired third threshold to the first device, so that the second device determines whether to perform dimension arrangement on the first data. As shown in Table 2, the third threshold in Table 2 is the third threshold corresponding to the first type parameter. The second device may indicate to the first device that the third threshold is 4.0. Thus, for the third threshold of 0.5, the dimension size of each dimension of the M dimensions of the first data is the same as the M dimensions. The tolerance for deviations between the geometric mean dimension sizes corresponding to the dimensions is high.

Table 2

It should be understood that Table 2 is only an example, and the third threshold value may also be other values, which is not specifically limited in the embodiments of the present application.

In a possible implementation, the first indication information includes index information, and the index information is used to determine the third threshold corresponding to the evaluation parameter from a set of candidate third thresholds, and the set of candidate third thresholds includes third thresholds corresponding to at least two evaluation parameters. That is, the first device can determine the third threshold according to the index information, thereby reducing the overhead of the first indication information and increasing the reliability of the first indication information.

It can be understood that the index information in the embodiment of the present application can be an index of the third threshold or an identifier of the third threshold, and the embodiment of the present application does not specifically limit this.

In a possible implementation, the first indication information is also used to indicate a candidate third threshold set. That is, the second device can configure a candidate dimension set for the first device to indicate to the first device the type of valence parameter and the third threshold corresponding to the evaluation parameter that the second device expects to use in the next period of time.

It can be understood that in the embodiment of the present application, the candidate third threshold set can also be pre-configured, or negotiated in advance between the first device and the second device, and the embodiment of the present application does not specifically limit this.

For step S304:

It can be understood that the specific implementation of the first device compressing the second data to obtain the third data can be described in the above-mentioned step S302 regarding the compression processing, which will not be repeated here.

It can also be understood that when the first device is a terminal device and the second device is an access network device, the resources used to transmit the third data may be resources pre-configured by the second device for the first device, such as resources pre-configured in grant-free transmission, and grant-free transmission may include, for example, transmission based on pre-configured uplink resources (pre-configured uplink resource, PUR) and configured grant (configured grant, CG) transmission, etc.; or, the resources used to transmit the third data may be resources dynamically scheduled by the network side, such as resources dynamically configured by the second device to the first device via downlink control information (downlink control information, DCI).

When the first device is an access network device, the resources used to transmit the third data may be resources estimated by the first device based on the dimension size of the first data and the compression rate of the first data. The compression rate of the first data may refer to: the ratio of the amount of data after dimensionality reduction of the first data (i.e., the third data) to the amount of data before dimensionality reduction of the first data, or the ratio of the dimensional size of the first data after dimensionality reduction to the dimensional size of the first data before dimensionality reduction.

It can be understood that the compression rate of the first data can be pre-configured; or, the compression rate of the first data can be negotiated in advance between the first device and the second device; or, the compression rate of the first data can be indicated by the second device, and the embodiments of the present application do not specifically limit this.

Optionally, the first device sends the third data to the second device (i.e., step S304), including: the first device quantizes the third data to obtain a compressed bit stream; the first device sends the compressed bit stream to the second device. It can be understood that the third data is quantized to facilitate the first device to send. For example, the elements in the third data can be quantized to 6 bits. It can also be understood that the first device can use other numbers of bits for quantization, and the embodiments of the present application do not specifically limit this.

It should be understood that the second device can receive a data signal or data channel carrying the third data (for example, a physical uplink shared channel (physical uplink share channel, PUSCH) or a physical downlink shared channel (physical downlink share channel, PDSCH), etc.) on the time domain resources and/or frequency domain resources corresponding to the third data, so that the second device can determine that the third data is associated with the first data, and then the second device can obtain the dimensional size of each dimension of the M dimensions of the first data and the dimensional configuration information corresponding to the first data based on the association relationship between the third data and the first data.

In addition, the second device obtains the dimension size and dimension configuration information of each dimension of the M dimensions of the first data. For details, please refer to the following step S305, which will not be repeated here.

For step S305:

In a possible implementation, the second device obtains the dimension size of each dimension in the M dimensions (i.e., step S405), including: the second device determines the dimension size of the first data according to the capability information of the first device and/or the resources used to transmit the third data. In other words, the second device can determine the dimension size of the first data according to the capability information of the first device and/or the resources used to transmit the third data, and then the first device may not indicate the dimension size of the first data to the second device, saving network overhead.

For example, taking the first device as a terminal device, the second device as an access network device, and the first data as CSI data (or perception data), the CSI data generally includes an RX dimension and a TX dimension. Among them, the second device can determine the number of antennas of the first device based on the capability information reported by the first device, that is, the second device can determine the dimension size of the RX dimension, the dimension size of the TX dimension, the sub-dimension information corresponding to the TX dimension, etc. Furthermore, in the case where the CSI data also includes a frequency domain dimension, for unlicensed transmission, the frequency domain resources used to transmit the third data are configured in advance by the second device to the first device, so that the second device can determine the dimension size of the frequency domain dimension.

It can be understood that for downlink transmission, the first device is an access network device, the second device is a terminal device, and the second device can determine the dynamically scheduled resources based on the DCI sent by the first device, and then determine the dimensional size of the frequency domain dimension. Further, the second device can estimate the dimensional size of the first data based on the compression rate and the dynamically scheduled resources. Among them, the compression rate can be pre-configured, or negotiated in advance between the first device and the second device, or indicated by the first device, and the embodiments of the present application do not specifically limit this.

It should be understood that the above-mentioned second device determining the dimensional size of the first data based on the capability information of the first device and/or the resources used to transmit the third data is only an example, and the second device may also use other methods to determine the dimensional size of the first data, and the embodiments of the present application do not specifically limit this.

In another possible implementation, the method shown in FIG3 further includes:

S308. The first device sends the second information to the second device. Accordingly, the second device receives the second indication information from the first device. The second indication information is used to indicate the dimension size of each dimension in the M dimensions. That is, for the second device to send dimension configuration information to the first device, the first device can send the dimension size of each dimension in the M dimensions to the second device, so that the second device can determine the dimension configuration information and send the dimension configuration information to the first device, which can improve the flexibility of the second device to obtain the dimension size of each dimension in the M dimensions. For the first device to determine the dimension configuration information, the second device can determine the dimension configuration information (i.e., the dimension size of each dimension in the N dimensions) according to the second indication information and the dimension arrangement rules, and then determine the fourth data according to the dimension size of each dimension in the M dimensions, the dimension configuration information, and the third data.

It can be understood that the second device determines the dimension size of each dimension in the N dimensions based on the second indication information (i.e., the dimension size of each dimension in the M dimensions) and the dimension arrangement rule. For details, please refer to the aforementioned "Dimension arrangement based on prime number decomposition" and "Dimension arrangement based on reference dimension size", which will not be repeated here.

In a possible implementation, the second indication information is also used to indicate dimension configuration information. That is, for the second device, the second device can directly determine the dimension configuration information based on the second indication information, which can improve the flexibility of the second device in obtaining the dimension configuration information and the size of each dimension in the M dimensions to adapt to different scenarios. For example, in the case where the dimension arrangement rule is not pre-configured and the dimension arrangement rule is not agreed upon between the first device and the second device, the second indication information sent by the first device to the second device also indicates the dimension configuration information, so that the second device can determine the dimension size and dimension configuration information of each dimension in the M dimensions based on the second indication information, so as to facilitate the subsequent decompression of the third data to determine the fourth data.

It can be understood that for the scenario where the dimension configuration information is indicated by the second device, step S308 may be before step S301; or, step S308 may be after step S301.

It can also be understood that for the scenario where the dimensional configuration information is determined by the first device, step S308 may be before step S304, or step S308 may be after step S304, or both may be performed simultaneously, and the embodiments of the present application do not specifically limit this.

For step S306:

It can be understood that the second device can determine the fifth data based on the third data and the dimensional configuration information, and the fifth data is the data obtained after decompression of the third data. The fifth data has the same dimensional size as the second data, and then the second device can determine the fourth data based on the fifth data and the dimensional size of each dimension in the M dimensions.

It should be understood that the above is only an example of the second device determining the fourth data. Other methods can also be used to determine the fourth data based on the third data, dimension configuration information, and the dimension size of each dimension in the M dimensions. The embodiment of the present application does not make specific limitations on this.

Optionally, the method shown in FIG3 further includes:

S309, the second device sends the second indication information to the first device. Correspondingly, the first device receives the second indication information from the second device. The second indication information is used to indicate the dimension configuration information. In other words, the first device can determine the dimension configuration information according to the second indication information, so as to improve the flexibility of the first device in determining the dimension configuration information to be applicable to different application scenarios.

In a possible implementation, the second indication information includes index information. The index information is used to determine the dimension size of each dimension in the N dimensions from the candidate dimension size set. The candidate dimension size set includes at least two groups of dimension sizes, and each group of dimension sizes in the at least two groups of dimension sizes includes the dimension size of each dimension in the N dimensions. That is, the second device can indicate the dimension size of each dimension in the N dimensions to the second device by indicating the index of the dimension configuration information in the candidate set, thereby saving the indication overhead of the second indication information and improving the reliability of the second indication information.

For example, Table 3 is a schematic table of a candidate dimension size set provided in an embodiment of the present application. As shown in Table 3, the candidate dimension size set The set may include K groups of dimension sizes, each group of dimension sizes indicating the dimension size corresponding to each dimension in the N dimensions. Among them, the candidate dimension size set in Table 3 also includes the corresponding relationship between the dimension size of each dimension in the M dimensions and the dimension size of each dimension in the N dimensions. That is to say, the first device can send multiple potential dimension sizes of the first data to the second device, that is, multiple groups of different dimension sizes of each dimension in the M dimensions, so that the second device can configure the corresponding relationship between the dimension size of each dimension in the multiple groups of different M dimensions and the dimension size of each dimension in the multiple groups of different N dimensions to the first device, so that the first device can determine the dimension size of each dimension in the N dimensions corresponding to the different dimension sizes of the first data.

Further, _L1 in Table 3 may represent the dimension size of the RX dimension, _L2 may represent the dimension size of the TX dimension, and _LM may represent the dimension size of the frequency domain dimension. The dimension sizes of each dimension in the N dimensions tend to be the same, such as the dimension sizes shown by index 0, index 1, and index K-1. The N dimensions shown in index 2 are smaller than the M dimensions, wherein the dimension corresponding to LM _-1 ' may be associated with the dimension corresponding to LM _-1 and the dimension corresponding to _LM in the M dimensions, that is, the dimension corresponding to LM _-1 ' is the dimension size of the first dimension in the above step S302, and the first dimension may correspond to dimension M-1 and dimension M in the M dimensions.

It can be understood that the index overhead in Table 3 is related to the K value. The larger the K value, the larger the indication overhead required. For example, the indication overhead of the index is ceil(log ₂ (K)), where ceil() means rounding up.

Table 3

It can be understood that Table 3 is only an illustrative example of the candidate dimension size set, and the N dimension sizes corresponding to the N dimensions in the candidate dimension size set can also be specific dimensions, for example, the specific dimension can be the row dimension or column dimension of the dimensionality reduction matrix; or, the N dimensions in the candidate dimension size set can be greater than the M dimensions, and the embodiments of the present application do not specifically limit this.

In a possible implementation, the second indication information is also used to indicate a set of candidate dimension sizes. That is, the second device can configure the set of candidate dimension sizes for the first device to indicate to the first device the candidate dimension sizes of the second data expected by the second device in the next period of time, that is, the dimension size of each dimension in the candidate N dimensions.

It should be understood that in the embodiment of the present application, the candidate dimension set may also be pre-configured by the protocol, or negotiated in advance between the first device and the second device, and the embodiment of the present application does not specifically limit this.

It can be understood that the indication information (e.g., the first indication information and the second indication information) in the above steps S307 to S309 can be carried by at least one of the following: an RRC message (or signaling), a DCI carried, a MAC protocol data unit (PDU), uplink control information (UCI), or a physical uplink control channel (PUCCH). Among them, the RRC message can be, for example, an RRC setup (RRC set up) message, or an RRC resume (RRC resume) message, or an RRC reconfiguration (RRC reconfiguration) message, etc. In other words, the information corresponding to the first indication information and/or the second indication information can be continuously effective during the RRC connection period, and the first indication information and the second indication information do not need to be sent each time the scheduling is performed. It is suitable for the scenario where the same first indication information and the second indication information are continuously sent during the RRC connection period, and the first indication information and the second indication information are changed through RRC signaling due to mobility, energy saving, or changes in business requirements.

It can be understood that for the first indication information and the second indication information carried by the DCI or MAC PDU (for example, the MAC control element (CE) in the MAC PDU, or the MAC service data unit (SDU)), the first device can dynamically indicate to the second device the information corresponding to the first indication information and/or the second indication information.

Due to the embodiment of the present application, the first device can arrange the dimensions of the first data to obtain second data through the dimension configuration information of the first data before compressing the first data, so that the number of dimensions or the size of at least some dimensions of the second data and the first data are different, and then the second data is compressed, which can reduce the amount of data compression calculations and improve the efficiency of data compression.

It can be understood that in each of the above embodiments, the methods and/or steps implemented by the first device can also be implemented by components that can be used for the first device (such as a processor, chip, chip system, circuit, logic module, or software); the methods and/or steps implemented by the second device can also be implemented by components that can be used for the second device (such as a processor, chip, chip system, circuit, logic module, or software).

The above mainly introduces the scheme provided by the present application. Accordingly, the present application also provides a communication device, which is used to implement various methods in the above method embodiments. The communication device can be the first device in the above method embodiments, or a device including the first device, or a component that can be used for the first device, such as a chip or a chip system. Alternatively, the communication device can be the second device in the above method embodiments, or a device including the second device, or a component that can be used to calculate the second device, such as a chip or a chip system.

It is understandable that, in order to realize the above functions, the communication device includes hardware structures and/or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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 embodiment of the present application can divide the functional modules of the communication device according to the above method embodiment. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

Taking the communication device as the first device or the second device in the above method embodiment as an example, FIG5 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application. As shown in FIG5, the communication device 500 includes: a processing module 501 and a transceiver module 502. Among them, the processing module 501 is used to perform the processing function of the first device or the second device in the above method embodiment. The transceiver module 502 is used to perform the transceiver function of the first device or the second device in the above method embodiment.

Among them, all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

Since the communication device 500 provided in this embodiment can execute the above-mentioned data transmission method, the technical effects that can be obtained can refer to the above-mentioned method embodiments and will not be repeated here.

In a possible design scheme, in the embodiment of the present application, the transceiver module 502 may include a receiving module and a sending module (not shown in FIG. 5 ). The transceiver module is used to implement the sending function and the receiving function of the communication device 500 .

In a possible design, the communication device 500 may further include a storage module (not shown in FIG. 5 ), which stores a program or instruction. When the processing module 501 executes the program or instruction, the communication device 500 may perform the function of the first device or the second device in the method shown in FIG. 3 .

It should be understood that the processing module 501 involved in the communication device 500 can be implemented by a processor or a processor-related circuit component, which can be a processor or a processing unit; the transceiver module 502 can be implemented by a transceiver or a transceiver-related circuit component, which can be a transceiver or a transceiver unit.

For example, FIG6 is a schematic diagram of the structure of another communication device provided in an embodiment of the present application. The communication device may be a first device or a second device, or may be a chip (system) or other component or assembly that can be provided in the first device or the second device. As shown in FIG6 , a communication device 600 may include a processor 601.

In a possible design, the communication device 600 may further include a memory 602 and/or a transceiver 603. The processor 601 is coupled to the memory 602 and the transceiver 603, for example, via a communication bus.

The following is a detailed introduction to the various components of the communication device 600 in conjunction with FIG. 6 :

The processor 601 is the control center of the communication device 600, which can be a processor or a general term for multiple processing elements. For example, the processor 601 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

In one possible design, the processor 601 may perform various functions of the communication device 600 by running or executing a software program stored in the memory 602 and calling data stored in the memory 602 .

In a specific implementation, as an embodiment, the processor 601 may include one or more CPUs, such as CPU0 and CPU1 shown in FIG. 6 .

In a specific implementation, as an embodiment, the communication device 600 may also include multiple processors, such as the processor 601 and the processor 604 shown in FIG6 . Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and/or devices for processing data (such as computer program instructions). processing core.

The memory 602 is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor 601. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

In a possible design, the memory 602 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 602 may be integrated with the processor 601, or may exist independently and be coupled to the processor 601 through an interface circuit (not shown in FIG. 6 ) of the communication device 600, which is not specifically limited in the embodiment of the present application.

The transceiver 603 is used for communication with other communication devices. For example, if the communication device 600 is a terminal device, the transceiver 603 can be used to communicate with an NTN device, or with another terminal device. For another example, if the communication device 600 is an NTN device, the transceiver 603 can be used to communicate with a terminal device, or with another NTN device.

In a possible design, transceiver 603 may include a receiver and a transmitter (not shown separately in FIG6 ), wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

In one possible design scheme, the transceiver 603 can be integrated with the processor 601, or it can exist independently and be coupled to the processor 601 through the interface circuit of the communication device 600 (not shown in Figure 6), which is not specifically limited in the embodiment of the present application.

It should be noted that the structure of the communication device 600 shown in FIG. 6 does not constitute a limitation on the communication device, and an actual communication device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

In addition, the technical effects of the communication device 600 can refer to the technical effects of the data transmission method described in the above method embodiment, which will not be repeated here.

In a possible implementation, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program or instructions, and the computer program or instructions implement the functions of the above method embodiment when executed by a computer.

In a possible implementation, the embodiment of the present application further provides a computer program product, which implements the functions of the above method embodiment when executed by a computer.

In a possible implementation, an embodiment of the present application further provides a communication system, which includes the first device described in the above method embodiment and the second device described in the above method embodiment.

In a possible implementation, an embodiment of the present application further provides a communication method, which includes the method described in any of the above method embodiments or any of its implementations.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more media integrated. Available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state drives (SSDs)), etc.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

Those skilled in the art can 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.

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 only illustrative, for example, the division of the units is only a logical function division, and 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 special Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

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

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other changes to the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims (24)

A data transmission method, characterized in that the method comprises: Acquire first data, where the dimension of the first data is M dimensions, where M is an integer greater than 1; Determine dimension configuration information corresponding to the first data, where the dimension configuration information is used to indicate the dimension size of each dimension of N dimensions of the second data, the second data and the first data have the same number of elements, the N dimensions are the M dimensions, and the dimension sizes corresponding to at least some dimensions of the N dimensions and the M dimensions are different, or N is different from M, and N is a positive integer; Determine the second data according to the first data and the dimension configuration information; Send third data, where the third data is data obtained by compressing the second data. A data transmission method, characterized in that the method comprises: Receive third data, where the third data is associated with the first data, where the first data has M dimensions, and M is a positive integer greater than 1; Obtaining a dimension size of each dimension in the M dimensions and dimension configuration information corresponding to the first data, where the dimension configuration information is used to indicate a dimension size of each dimension in N dimensions of the second data, the second data and the first data have the same number of elements, the N dimensions are the M dimensions, and the dimension sizes corresponding to at least some dimensions of the N dimensions and the M dimensions are different, or N is different from M, and N is a positive integer; Fourth data is determined according to the third data, the dimension configuration information, and the dimension size of each dimension in the M dimensions. The method according to claim 1 or 2 is characterized in that the dimension configuration information is associated with at least one of the following: information on the compression processing, and/or sub-dimension information corresponding to the second dimension of the M dimensions. The method according to any one of claims 1 to 3 is characterized in that a difference or ratio between the dimensional sizes of any two dimensions among the N dimensions is less than a first threshold. The method according to any one of claims 1-4 is characterized in that the difference or ratio between the dimensional size of each dimension in the N dimensions and the geometric mean dimensional size corresponding to the M dimensions is less than or equal to a second threshold; or, the sum of the deviations corresponding to each dimension in the N dimensions is less than or equal to a second threshold, and the deviation corresponding to each dimension in the N dimensions is determined based on the ratio between the dimensional size of each dimension in the N dimensions and the geometric mean dimensional size. The method according to any one of claims 1 to 5 is characterized in that the dimension configuration information is determined according to the dimension size and dimension arrangement rule of each dimension in the M dimensions. The method according to any one of claims 1, 3-6, characterized in that determining the second data according to the first data and the dimension configuration information comprises: When the evaluation parameters corresponding to the M dimensions are greater than or equal to a third threshold, the second data is determined according to the first data and the dimension configuration. The method according to claim 7 is characterized in that the evaluation parameters include first type parameters and/or second type parameters, and the third threshold includes a third threshold corresponding to the first type parameters and/or a third threshold corresponding to the second type parameters; wherein the first type parameter is the sum of the deviations corresponding to each dimension of the M dimensions, the deviation corresponding to each dimension of the M dimensions is determined based on the dimensional size of each dimension in the M dimensions and the geometric mean dimensional size corresponding to the M dimensions, and the second type parameter is the difference or ratio between the maximum dimensional size and the minimum dimensional size among the M dimensional sizes corresponding to the M dimensions. The method according to claim 7 or 8, characterized in that the method further comprises: First indication information is received, where the first indication information is used to indicate a type of the evaluation parameter and a third threshold corresponding to the evaluation parameter. The method according to any one of claims 2 to 6, characterized in that the method further comprises: First indication information is sent, where the first indication is used to indicate a type of the evaluation parameter and a third threshold corresponding to the evaluation parameter. The method according to claim 9 or 10 is characterized in that the first indication information includes index information, and the index information is used to determine the third threshold corresponding to the evaluation parameter from a candidate third threshold set, and the candidate third threshold set includes at least two third thresholds corresponding to the evaluation parameters. The method according to claim 11 is characterized in that the first indication information is also used to indicate the candidate third threshold set. The method according to any one of claims 1 to 11, characterized in that the method further comprises: Send second indication information, where the second indication information is used to indicate the dimension size of each dimension in the M dimensions. The method according to claim 13 is characterized in that the second indication information is also used to indicate the dimension configuration information. The method according to any one of claims 1 to 13, characterized in that the method further comprises: Second indication information is received, where the second indication information is used to indicate the dimension configuration information. The method according to claim 15 is characterized in that the second indication information includes index information, and the index information is used to determine the dimension size of each dimension in the N dimensions from a set of candidate dimension sizes, and the set of candidate dimension sizes includes at least two groups of dimension sizes, and each group of dimension sizes in the at least two groups of dimension sizes includes the dimension size of each dimension in the N dimensions. The method according to claim 16 is characterized in that the second indication information is also used to indicate the candidate dimension size set. A communication device, characterized in that the communication device includes a module or unit for executing the method described in any one of claims 1 and 3-17, or includes a module or unit for executing the method described in any one of claims 2-17. A communication device, characterized in that the communication device includes a processor, and the processor is used to enable the communication device to execute the method according to any one of claims 1, 3-17 through logic circuits and/or execution instructions, or enable the communication device to execute the method according to any one of claims 2-17. The communication device according to claim 19 is characterized in that the communication device also includes a memory, and the memory is used to store the instruction. The communication device according to claim 19 or 20 is characterized in that the communication device also includes a communication interface, and the communication interface is used to input and/or output signaling and/or data. A computer-readable storage medium, characterized in that the computer-readable storage medium includes instructions, and when the instructions are executed by a processor, the method according to any one of claims 1, 3-17 is implemented, or the method according to any one of claims 2-17 is implemented. A computer program product, characterized in that the computer program product comprises instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1, 3-17, or cause the computer to execute the method according to any one of claims 2-17. A communication system, characterized in that the communication system comprises a first device and a second device, the first device is used to execute the method according to any one of claims 1 and 3-17, and the second device is used to execute the method according to any one of claims 2-17.