WO2025195407A1 - Communication method and apparatus - Google Patents

Abstract

Provided in the present application are a communication method and apparatus, which can reduce the waste of transmission resources. The method comprises: a second network element sending M channel reports to a first network element, wherein an mth channel report among the M channel reports is determined by the second network element on the basis of a first measurement result that is obtained by means of measuring an mth reference signal among M reference signals, M is a positive integer, and m is a positive integer which is taken from 1 to M in sequence; when the first measurement result meets a first condition corresponding to the mth reference signal, the mth channel report comprises one or more of the following: the first measurement result obtained by means of measuring the mth reference signal, a second measurement result obtained by means of measuring the mth reference signal, and channel information obtained on the basis of the mth reference signal; or when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report comprises the first measurement result or the second measurement result.

Description

Communication method and device

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of the People's Republic of China on March 22, 2024, with application number 202410345974.7 and application name "A Communication Method and Device", the entire contents of which are incorporated by reference into this application.

Technical Field

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

Background Art

Artificial intelligence technology is increasingly being used in wireless communication networks. For example, artificial intelligence technology is used to measure and analyze channels to achieve wireless perception.

Currently, the training or inference process of artificial intelligence models involves information exchange between multiple network elements. For example, one network element measures a channel and sends the channel estimation results to another network element, which then uses the channel estimation results as input for model training or inference. However, the loss of one or more channel estimation results may result in unnecessary waste of transmission resources.

Summary of the Invention

The present application provides a communication method and apparatus that can reduce waste of transmission resources.

In a first aspect, an embodiment of the present application provides a communication method, including: a second network element sends M channel reports to a first network element; wherein, the mth channel report among the M channel reports is determined by the second network element based on a first measurement result obtained by measuring an mth reference signal among M reference signals, where M is a positive integer, and m is a positive integer ranging from 1 to M in sequence; when the first measurement result satisfies a first condition corresponding to the mth reference signal, the mth channel report includes one or more of the following: the first measurement result obtained by measuring the mth reference signal; the second measurement result obtained by measuring the mth reference signal; channel information obtained based on the mth reference signal; or, when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result.

In the above method, channel information estimated based on the reference signal is transmitted between network elements only when the reference signal measurement result meets the set conditions, which can reduce the waste of transmission resources.

In one possible design, the first condition corresponding to the m-th channel report may be preconfigured. In another possible design, the method further includes: a second network element receiving first information from the first network element, where the first information is used to indicate the first condition corresponding to the m-th reference signal. Such a design helps the second network element quickly decide on the content carried in the first channel report based on the first condition, thereby improving communication efficiency.

In a second aspect, embodiments of the present application provide a communication method, comprising: a third network element receiving second information from a first network element, the second information indicating a second condition; and the third network element transmitting the image information to the first network element when the image information collected by the third network element meets the second condition. In this design, image transmission between network elements is performed only when the image information meets the set condition, thereby reducing waste of transmission resources.

In a third aspect, an embodiment of the present application provides a communication method, comprising: a first network element receiving M channel reports from a second network element; wherein, the mth channel report among the M channel reports is determined by the second network element based on a first measurement result obtained by measuring the mth reference signal among the M reference signals, and M is a positive integer, and m is a positive integer ranging from 1 to M in sequence; when the first measurement result satisfies a first condition corresponding to the mth reference signal, the mth channel report includes one or more of the following: the first measurement result obtained by measuring the mth reference signal; the second measurement result obtained by measuring the mth reference signal; channel information obtained based on the mth reference signal; or, when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result.

In the above method, channel information estimated based on the reference signal is transmitted between network elements only when the reference signal measurement result meets the set conditions, which can reduce the waste of transmission resources.

In one possible design, the method further includes: the first network element sending first information to the second network element, where the first information is used to indicate a first condition corresponding to the mth reference signal. Such a design helps the second network element quickly determine the content of the first channel report based on the first condition, thereby improving communication efficiency.

In one possible design, the method further includes: a first network element determining a first perception result based on the M channel reports and a first model; wherein an input to the first model is determined based on the M channel reports, and an output of the first model includes the first perception result. Such a design utilizes artificial intelligence to analyze and process one or more channel reports, thereby enabling wireless perception.

In one possible design, taking the example of the M channel reports including a first channel report and a second channel report, the method further includes: the first network element determining a second perception result based on the first channel report and the second model; and determining a third perception result based on the second channel report and the third model; and fusing the second perception result and the third perception result to obtain a fourth perception result. The input of the second model is determined based on the first channel report, and the output of the second model includes the second perception result; the input of the third model is determined based on the second channel report, and the output of the third model includes the third perception result. This design uses artificial intelligence to jointly analyze multiple channel reports, enabling wireless perception.

In one possible design, the method further includes: the first network element sending second information to a third network element, the second information indicating a second condition; and the first network element receiving image information from the third network element, the image information satisfying the second condition. In this design, image transmission between network elements is performed only when the image information satisfies the set condition, thereby reducing waste of transmission resources.

In one possible design, the method further includes determining a fifth perception result based on the M channel reports, the image information, and a fourth model; wherein an input to the fourth model is determined based on the M channel reports and the image information, and an output of the fourth model includes the fifth perception result. This design utilizes artificial intelligence to jointly analyze at least one channel report and image information, thereby enabling wireless perception.

In a fourth aspect, an embodiment of the present application provides a communication device, which may be a second network element, or a device, module, or chip in the second network element, or a device that can be used in combination with the second network element. In one design, the communication device may include a module that executes the method/operation/step/action described in the first aspect, and the module may be a hardware circuit, or software, or a combination of a hardware circuit and software. In one design, the communication device may include a processing module and a communication module, and the communication module includes a sending unit and a receiving unit. Optionally, the processing module may also be replaced by the description of the processing unit.

A processing module, configured to send M channel reports to a first network element through a communication module; wherein the mth channel report among the M channel reports is determined by the processing module based on a first measurement result obtained by measuring an mth reference signal among M reference signals, where M is a positive integer, and m is a positive integer ranging from 1 to M in sequence; when the first measurement result satisfies a first condition corresponding to the mth reference signal, the mth channel report includes one or more of the following: the first measurement result obtained by measuring the mth reference signal; the second measurement result obtained by measuring the mth reference signal; channel information obtained based on the mth reference signal; or, when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result.

In one possible design, the first condition corresponding to the mth channel report may be preconfigured. In another possible design, the method further includes: the second network element receiving first information from the first network element, the first information being used to indicate the first condition corresponding to the mth reference signal.

In a fifth aspect, an embodiment of the present application provides a communication device, which may be a third network element, or a device, module or chip in a third network element, or a device that can be used in combination with a third network element. In one design, the communication device may include a module that corresponds one-to-one to the execution of the method/operation/step/action described in the second aspect, and the module may be a hardware circuit, or software, or a combination of a hardware circuit and software. In one design, the communication device may include a processing module and a communication module, and the communication module includes a sending unit and a receiving unit. Optionally, the processing module may also be replaced by the description of the processing unit.

a communication module, configured to receive second information from the first network element, where the second information indicates a second condition;

A processing module is configured to send the image information to the first network element through the communication module when the image information collected by the third network element meets the second condition.

In a sixth aspect, an embodiment of the present application provides a communication device, which may be a first network element, or a device, module or chip in the first network element, or a device that can be used in combination with the first network element. In one design, the communication device may include a module that corresponds one-to-one to the execution of the method/operation/step/action described in the third aspect, and the module may be a hardware circuit, or software, or a combination of a hardware circuit and software. In one design, the communication device may include a processing module and a communication module, and the communication module includes a sending unit and a receiving unit. Optionally, the processing module may also be replaced by the description of the processing unit.

A communication module is configured to receive M channel reports from a second network element; wherein the mth channel report among the M channel reports is determined by the second network element based on a first measurement result obtained by measuring an mth reference signal among M reference signals, where M is a positive integer and m is a positive integer ranging from 1 to M in sequence; when the first measurement result satisfies a first condition corresponding to the mth reference signal, the mth channel report includes one or more of the following: the first measurement result obtained by measuring the mth reference signal; the second measurement result obtained by measuring the mth reference signal; channel information obtained based on the mth reference signal; or, when the first measurement result does not satisfy the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result.

In one possible design, the communication module is also used to send first information to the second network element, where the first information is used to indicate a first condition corresponding to the mth reference signal.

In one possible design, a processing module is used to determine a first perception result based on the M channel reports and a first model; wherein the input of the first model is determined based on the M channel reports, and the output of the first model includes the first perception result.

In one possible design, taking the M channel reports including a first channel report and a second channel report as an example, the processing module is further configured to: determine a second perception result based on the first channel report and the second model; determine a third perception result based on the second channel report and the third model; and fuse the second perception result and the third perception result to obtain a fourth perception result. The input of the second model is determined based on the first channel report, and the output of the second model includes the second perception result; the input of the third model is determined based on the second channel report, and the output of the third model includes the third perception result.

In one possible design, the communication module is further used to: send second information to a third network element, where the second information indicates a second condition; and receive image information from the third network element, where the image information meets the second condition.

In one possible design, the processing module is further used to determine a fifth perception result based on the M channel reports, the image information and the fourth model; wherein the input of the fourth model is determined based on the M channel reports and the image information, and the output of the fourth model includes the fifth perception result.

In a seventh aspect, an embodiment of the present application provides a communication system, comprising the communication apparatus as described in the fourth and sixth aspects. Optionally, the communication system may further comprise the communication apparatus as described in the fifth aspect.

In an eighth aspect, an embodiment of the present application provides a communication system, comprising an apparatus (such as a second network element) for implementing the method described in the first aspect and an apparatus (such as a first network element) for implementing the method described in the third aspect. Optionally, the communication system further comprises an apparatus (such as a third network element) for implementing the method described in the second aspect.

In a ninth aspect, an embodiment of the present application provides a communication device, comprising a processor for implementing the method described in any one of the first to third aspects above. The processor is coupled to a memory, and the memory is used to store instructions and data. When the processor executes the instructions stored in the memory, the method described in any one of the first to third aspects can be implemented. Optionally, the communication device may further include a memory; the communication device may further include a communication interface, and the communication interface is used for the communication device to communicate with other devices. Exemplarily, the communication interface may be a transceiver, a circuit, a bus, a module, a pin, or other types of communication interfaces.

In the tenth aspect, an embodiment of the present application provides a communication device, comprising a logic circuit and an interface circuit; the interface circuit is used to communicate with a module outside the communication device; the logic circuit is used to execute a computer program so that the communication device executes the method provided in any one of the first to third aspects above.

In the eleventh aspect, an embodiment of the present application further provides a computer program, which, when executed on a computer, enables the computer to execute the method provided in any one of the first to third aspects above.

In the twelfth aspect, an embodiment of the present application further provides a computer program product, comprising instructions, which, when executed on a computer, enable the computer to execute the method provided in any one of the first to third aspects above.

In the thirteenth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is run on a computer, the computer executes the method provided in any one of the first to third aspects above.

In the fourteenth aspect, an embodiment of the present application further provides a chip, which is used to read a computer program stored in a memory and execute the method provided in any one of the first to third aspects above.

In a fifteenth aspect, an embodiment of the present application further provides a chip system, which includes a processor for supporting a computer device to implement the method provided in any one of the first to third aspects above. In one possible design, the chip system also includes a memory for storing the necessary programs and data for the computer device. The chip system can be composed of a chip, or it can include a chip and other discrete devices.

For the effects of the solutions provided in any of the fourth to fifteenth aspects above, reference can be made to the corresponding descriptions in the first to third aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic structural diagram of a communication system;

FIG2A is a schematic diagram of a neuron structure;

FIG2B is a schematic diagram of the layer relationship of a neural network;

Figure 2C is a schematic diagram of an AI application framework;

FIG3 is a schematic diagram of the working mode of wireless sensing;

FIG4 is a schematic diagram of the structure of another communication system;

Figures 5A and 5B are schematic diagrams of downlink positioning scenarios;

Figures 6A and 6B are schematic diagrams of uplink positioning scenarios;

FIG7 is a flow chart of a communication method according to an embodiment of the present application;

FIG8 is a flow chart of a communication method according to an embodiment of the present application;

FIG9 is a flow chart of a communication method according to an embodiment of the present application;

10A to 10C are schematic diagrams of a perception process based on an AI model in an embodiment of the present application;

FIG11 is a flow chart of a communication method according to an embodiment of the present application;

FIG12 is a flow chart of a communication method according to an embodiment of the present application;

FIG13 is a flow chart of a communication method according to an embodiment of the present application;

FIG14 is a schematic diagram of a structure of a communication device according to an embodiment of the present application;

FIG15 is one of the structural diagrams of the communication device in the embodiment of the present application.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of this application clearer, this application will be further described in detail below with reference to the accompanying drawings.

The following at least one (item) involved in this application indicates one (item) or more (items). More than one (item) refers to two (items) or more than two (items). "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the previous and next associated objects are in an "or" relationship. In addition, it should be understood that although the terms first, second, etc. may be used to describe each object in this application, these objects should not be limited to these terms. These terms are only used to distinguish each object from each other.

The terms "including" and "having" and any variations thereof mentioned in the following description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any method or design described in this application as "exemplary" or "for example" should not be interpreted as being more preferred or more advantageous than other methods or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

The technical solutions provided in this application can be applied to various communication systems, such as fifth-generation (5G) or new radio (NR) systems, long-term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area network (WLAN) systems, satellite communication systems, future communication systems such as sixth-generation (6G) mobile communication systems, or a fusion system of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

A network element in a communication system can send a signal to another network element or receive a signal from another network element. The signal may include information, signaling, or data, etc. The network element can also be replaced by an entity, a network entity, a device, a communication device, a communication module, a node, a communication node, etc. The present application takes the network element as an example for description. For example, the communication system may include at least one terminal device and at least one access network device. The access network device can send a downlink signal to the terminal device, and/or the terminal device can send an uplink signal to the access network device. In addition, it can be understood that if the communication system includes multiple terminal devices, the multiple terminal devices can also send signals to each other, that is, the signal sending network element and the signal receiving network element can both be terminal devices.

The communication method provided in this application can be applied to wireless communication systems such as 5G, 6G, and satellite communications. Referring to Figure 1, Figure 1 is a simplified schematic diagram of the wireless communication system provided in this application. As shown in Figure 1, the wireless communication system includes a wireless access network 100. The wireless access network 100 can be a next-generation (e.g., 6G or higher) wireless access network, or a traditional (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more terminal devices (120a-120j, collectively referred to as 120) can be connected to each other or to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Optionally, Figure 1 is only a schematic diagram, and the wireless communication system may also include other devices, such as core network devices, wireless relay devices and/or wireless backhaul devices, sensor devices, etc., which are not drawn in Figure 1.

Optionally, in actual applications, the wireless communication system may include multiple network devices (also called access network devices) at the same time, and may also include multiple terminal devices at the same time. A network device can serve one or more terminal devices at the same time. A terminal device can also access one or more network devices at the same time. This application does not limit the number of terminal devices and network devices included in the wireless communication system.

The network device may be an entity on the network side for transmitting or receiving signals. The network device may be an access device for a communication device to access the wireless communication system in a wireless manner, such as a base station. Base station can broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), access network equipment in open radio access network (O-RAN), relay station, access point, transmitting point (TRP), transmitting point (TP), master station MeNB, secondary station SeNB, multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), centralized unit control plane (CU-CP) node, centralized unit user plane (CU-UP) node, positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The network device can also refer to a communication module, a modem or a chip for being arranged in the aforementioned device or apparatus. The network device can also be a mobile switching center and a device to device (Device-to-Device, D2D), vehicle outreach (vehicle-to-everything, V2X), a device that performs the base station function in machine to machine (machine-to-machine, M2M) communications, a network side device in a 6G network, a device that performs the base station function in a future communication system, etc. The network device can support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.

Network devices can be fixed or mobile. For example, base stations 110a and 110b are stationary and are responsible for wireless transmission and reception in one or more cells from communication device 120. The helicopter or drone 120i shown in Figure 1 can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) can be configured to act as a communication device communicating with base station 110b.

The network device in the embodiments of the present application may be an integrated base station, or may be a base station including a centralized unit (CU) and/or a distributed unit (DU). A base station including both a CU and a DU may also be referred to as a base station with separate CUs and DUs, such as a base station including a gNB-CU and a gNB-DU. The CU may also be separated into a CU control plane (CU-CP) and a CU user plane (CU-UP), such as a base station including a gNB-CU-CP, a gNB-CU-UP, and a gNB-DU. Alternatively, the network device in the embodiments of the present application may be an antenna unit (RU). Furthermore, the network device in the embodiments of the present application may also be an open radio access network (O-RAN) architecture, and the embodiments of the present application do not limit the specific deployment method of the network device. For example, when the network device is an O-RAN architecture, the network device shown in the embodiments of the present application may be an access network device in the O-RAN, such as a combination of one or more of a CU, DU, or RU, or a module in the access network device. In the ORAN system, CU may also be referred to as open (O)-CU, CU-CP may also be referred to as O-CU-CP, CU-UP may also be referred to as O-CU-UP, and RU may also be referred to as O-RU.

In this application, the communication device used to implement the above-mentioned access network function can be a base station, or a network device with partial access network functions, or a device capable of supporting the implementation of the access network function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. The device can be installed in a base station or used in conjunction with a base station. In the method of this application, the communication device used to implement the base station function is described as an example.

A terminal device can be an entity on the user side that receives or transmits signals, such as a mobile phone. A terminal device can be used to connect people, objects, and machines. A terminal device can communicate with one or more core networks via network devices. Terminal devices include handheld devices with wireless connectivity, other processing devices connected to a wireless modem, or in-vehicle devices. A terminal device can be portable, pocket-sized, handheld, built into a computer, or in-vehicle. Terminal device 120 can be widely used in various scenarios, such as cellular communications, device-to-device (D2D), vehicle-to-everything (V2X), end-to-end (P2P), machine-to-machine (M2M), machine-type communications (MTC), the Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, and more. Some examples of terminal devices 120 include: 3GPP standard user equipment (UE), fixed devices, mobile devices, handheld devices, wearable devices, cellular phones, smart phones, Session Initiation Protocol (SIP) phones, laptops, personal computers, smart books, vehicles, satellites, Global Positioning System (GPS) devices, target tracking devices, drones, helicopters, aircraft, ships, remote control devices, smart home devices, industrial devices, personal communication service (PCS) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), wireless network cameras, tablet computers, PDAs, mobile Internet devices (mobile internet devices), etc. The terminal device 120 may be a wireless device in various scenarios described above or a device used to be provided in a wireless device, such as a communication module, modem, or chip in the aforementioned devices. A terminal device may also be referred to as a terminal, terminal device, user equipment (UE), mobile station (MS), or mobile terminal (MT). A terminal device may also be referred to as a terminal, terminal device, user equipment (UE), mobile station (MS), or mobile terminal (MT). A terminal device may also be a terminal device in a future wireless communication system. The terminal device can be used in a dedicated network device or a general-purpose device. The embodiments of the present application do not limit the specific technology and specific device form used by the terminal device.

Optionally, a UE can act as a scheduling entity, providing sidelink (SL) signals between UEs in V2X, D2D, or P2P scenarios. As shown in Figure 1, a cell phone 120a and a car 120b communicate with each other using sidelink signals. Cell phone 120a and smart home device 120e communicate without relaying the communication signals through base station 110b.

In this application, the communication device for realizing the functions of the terminal device can be a terminal device, or a device having some of the functions of the above terminal devices, or a device capable of supporting the realization of the functions of the above terminal devices, such as a chip system, which can be installed in the terminal device or used in combination with the terminal device. In this application, the chip system can be composed of chips, or it can include chips and other discrete devices. In the technical solution provided in this application, the communication device for realizing the functions of the terminal device is described as a terminal device or UE as an example.

It should be understood that the number and type of each device in the communication system shown in Figure 1 are for illustration only, and the present application is not limited thereto. In actual applications, the communication system may further include more terminal devices and more base stations; the communication system may further include other network elements, for example, Figure 1 also illustrates the inclusion of core network equipment 130. In addition, the communication system may further include sensing equipment (such as radars, cameras), network management, network elements for implementing artificial intelligence functions, etc. Among them, the network management may also be referred to as an operation and maintenance (OAM) network element, or OAM for short. Operations mainly complete the analysis, prediction, planning, and configuration of daily network and business operations; maintenance mainly involves daily operational activities such as testing and fault management of the network and its services. The network management can detect the network operating status, optimize network connections and performance, improve network operating stability, and reduce network maintenance costs.

It is understandable that all or part of the functions implemented by one or more of the terminal devices, base stations, or core network devices can be virtualized, that is, implemented by one or more of the proprietary processors or general-purpose processors and the corresponding software modules. Among them, since the terminal devices and base stations involve interfaces for air interface transmission, the transceiver functions of the interfaces can be implemented by hardware. Core network devices, such as the aforementioned OAM, can all be virtualized. Optionally, one or more functions of the virtualized terminal devices, base stations, or core network devices can be implemented by cloud devices, such as cloud devices in over-the-top (OTT) systems.

The communication between devices involved in this application includes one or more of the following: communication between a base station and a terminal device, or communication between base stations, such as communication between a macro base station and a micro base station in a wireless backhaul link, or communication between two terminal devices in a side link (SL), or communication between a terminal device and an OAM (network management), a terminal device and a core network device, a base station and a core network device, etc., without limitation.

The methods provided in this application involve artificial intelligence (AI). To facilitate understanding, the following introduces some of the AI terms involved in this application in conjunction with A1 to A4. It should be understood that this introduction does not limit this application.

A1, AI model

An AI model is a specific implementation of AI technology functionality. It represents the mapping relationship between the model's inputs and outputs. An AI model can be a neural network, linear regression model, decision tree model, support vector machine (SVM), Bayesian network, Q-learning model, or other machine learning (ML) model. In this application, the AI model is referred to as simply "model."

A2, Neural Network

Neural networks are a specific implementation of AI or machine learning technology. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, giving them the ability to learn arbitrary mappings.

The idea of neural networks comes from the neuron structure of brain tissue. For example, each neuron performs a weighted sum operation on its input values and outputs the operation result through an activation function. As shown in Figure 2A, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x = [x ₀ , x ₁ ,…, x _n ], and the weights corresponding to each input are w = [w, w ₁ ,…, w _n ], where w _i is the weight of x _i and is used to weight x _i . The bias for weighted summation of input values according to the weights is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), the output of the neuron is: For another example, if the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), or recurrent neural networks (RNNs).

A neural network generally includes multiple layers, each of which may include one or more neurons. Increasing the depth and/or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it comprises, while the number of neurons in each layer can be referred to as the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes input information through neurons and transmits the processing results to the output layer, which then generates the output of the neural network. In another implementation, a neural network includes an input layer, a hidden layer, and an output layer. For example, the FNN illustrated in FIG2B shows a fully connected FNN. Neurons in adjacent layers of the FNN are fully connected. The input layer processes input information through neurons and transmits the processing results to an intermediate hidden layer. The hidden layer calculates the received processing results to obtain a calculation result, which it then transmits to the output layer or an adjacent hidden layer. The output layer ultimately generates the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected in sequence, without limitation.

A3, training data and inference data

The training dataset is used to train the model. The training dataset may include the model's input data, or the model's input data and target output data. A training dataset includes one or more training data, which may be input data to the model or the model's target output data. Target output data may also be referred to as labels, output label data, or output label samples. Training datasets are an important part of machine learning. Model training is essentially about learning certain features from the training data so that the model's output data is as close to the target output data as possible, such as minimizing the difference between the model's output data and the target output data. The composition and selection of the training dataset can, to a certain extent, determine the performance of the trained model.

In addition, during the training process of a model (such as a neural network), a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the target output value. This application does not limit the specific form of the loss function. The training process of the model is to adjust the model parameters of the model so that the value of the loss function is less than the threshold, or the value of the loss function meets the target requirements.

Among them, the model parameters may include one or more of the following: structural parameters of the model (such as the number of layers and/or weights of the model, etc.). For example, if the model is a neural network, the structural parameters of the neural network include at least one of the following: the number of layers, width, weights of neurons, or parameters in the activation function of neurons of the neural network; input parameters of the model (such as input dimension, number of input ports); output parameters of the model (such as output dimension, number of output ports). It can be understood that the input dimension may refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may refer to the number of input data. Similarly, the output dimension may refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may refer to the number of output data.

Inference data can be used as input to a trained model for inference, validation, or monitoring of model performance. During model inference, inputting inference data into the model yields the corresponding output, which is the inference result. Optionally, the model input data included in the training dataset can also be used as inference data for inference, validation, or monitoring of model performance.

A4, AI model design

The design of an AI model primarily includes a data collection phase (e.g., collecting training data and/or inference data), a model training phase, and a model inference phase. It may further include an inference result application phase. See Figure 2C for an AI application framework. In the aforementioned data collection phase, a data source is used to provide a training data set and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. The AI model represents the mapping relationship between the model's input and output. Learning an AI model through model training nodes is equivalent to learning the mapping relationship between the model's input and output using training data. In the model inference phase, the AI model trained in the model training phase is used to perform inference based on the inference data provided by the data source to obtain an inference result. This phase can also be understood as: inputting inference data into the AI model, and obtaining output data through the AI model, which is the inference result. The inference result may indicate: configuration parameters used (executed) by the execution object, and/or operations performed by the execution object. In this application, an AI model can infer one parameter or multiple parameters. The reasoning results are published in the reasoning result application link. For example, the reasoning results can be uniformly planned by the execution (actor) entity. For example, the execution entity can send the reasoning results to one or more execution objects (for example, core network equipment, access network equipment, terminal equipment or network management, etc.) for execution.

The following describes in detail the communication scenarios in which the methods provided in the embodiments of the present application are applied.

Scenario 1: Wireless Sensing

Wireless sensing primarily achieves functions such as target location and tracking by acquiring information about the surrounding environment or objects. Traditional sensing technologies rely primarily on radio waves, radar, infrared light, and sensors. For example, radar is an electronic device that uses electromagnetic waves to detect targets. Radar transmits electromagnetic waves to illuminate the target and receives the echo, thereby obtaining information such as the distance from the target to the electromagnetic wave emission point, the rate of change of distance (radial velocity), direction, and altitude. Radar technology is currently widely used, for example in airborne, shipborne, and base-based radars for target detection and imaging.

Wireless communication systems primarily rely on the propagation of electromagnetic waves in free space to ensure the transmission of communication data. Wireless signals not only transmit data but also sense the environment. Radio waves generated by a transmitter propagate via multiple paths, including direct radiation, reflection, and scattering. The multipath signal formed at the receiver reflects the characteristics of the environment through which the signal has passed. In the communication system illustrated in Figure 1, the transmitter and receiver can be the same or different devices. As an example, Figure 3 illustrates the devices used as transmitters and receivers in several wireless sensing modes.

Specifically, (a) in Figure 3 illustrates a base station self-transmission and self-reception mode, that is, the same base station acts as a signal transmitter and a signal receiver, sending a signal and receiving a signal reflected by a target object. (b) in Figure 3 illustrates a collaborative perception mode between base stations, that is, the first base station acts as a signal transmitter and the second base station acts as a signal receiver. (c) in Figure 3 illustrates a base station as a signal transmitter and a UE as a signal receiver. (d) in Figure 3 illustrates a UE as a signal transmitter and a base station as a signal receiver. (e) in Figure 3 illustrates a terminal self-transmission and self-reception mode, that is, the same UE acts as a signal transmitter and a signal receiver, sending a signal and receiving a signal reflected by a target object. (f) in Figure 3 illustrates a collaborative perception mode between terminals, that is, the first UE acts as a signal transmitter and the second UE acts as a signal receiver. Optionally, the aforementioned target object can also be replaced by a description of a perceived object. It can be understood that the aforementioned example reflects the wireless perception related to UE and base station in the communication system. In other scenarios, the perception of non-3GPP type sensors (such as radars and cameras) may also be designed, and the embodiments of the present application are not limited to this.

For example, wireless sensing technology can be specifically applied to the following scenarios: object and intruder detection around smart homes, highways, railways, factories, and critical infrastructure; collision avoidance and trajectory of drones, vehicles, and AGVs; autonomous driving and navigation of cars; public safety search and rescue; rainfall and flood monitoring; and health and motion monitoring.

Specifically, wireless sensing can be divided into three categories based on how it processes wireless sensing measurement data: detection, estimation, and recognition. Detection involves making a binary (or multivariate) judgment on the state of a perceived target based on sensing measurement data. This state typically includes whether the target exists or not, or whether a target-related event has occurred. Examples include intrusion detection, vehicle detection, pedestrian detection, or drone detection. Estimation involves estimating parameters of the perceived target (such as distance, speed, angle, and position) based on sensing measurement data. Estimation performance can be measured using mean squared error. Recognition involves identifying the perceived target based on sensing measurement data. Examples include object type recognition, human activity recognition, and event recognition. This performance can be evaluated using recognition accuracy.

Using AI technology, perception measurement data can be input into the AI model to output the status, parameters or recognition results of the perceived target.

Scenario 2: AI-based terminal positioning

As shown in Figure 4, a communication system includes, in addition to base stations and terminal devices, core network elements, such as access and mobility management function (AMF) network elements and location management service function (LMF) network elements. The LMF network element is used to estimate the location of the terminal device.

The communication system illustrated in Figure 4 may include multiple base stations. These base stations may be of the same standard or of different network standards. For example, Figure 4 illustrates a 5G base station, such as a gNB, and a 4G base station, such as an ng-eNB, that can access the 5G core network. A terminal device (represented by a UE in Figure 4) and the gNB can communicate via the NR-Uu interface, for example, using the NR-Uu interface to transmit positioning-related signaling. The terminal device and the ng-eNB can communicate via the LTE-Uu interface, for example, using the LTE-Uu interface to transmit positioning-related signaling. The gNB and the AMF can communicate via the NG-C interface, and the ng-eNB and the AMF can communicate via the NG-C interface, for example, for transmitting positioning-related signaling. The AMF and the LMF can communicate via the NL1 interface, for example, using the NL1 interface to transmit positioning-related signaling.

Among them, a model for terminal device positioning is deployed in LMF, and the method of using the model in LMF to determine the position of the terminal device is mainly used in downlink positioning scenarios and uplink positioning scenarios.

(1) In the downlink positioning scenario, multiple base stations or cell nodes of base stations send positioning reference signals (PRS) to the terminal device, and the terminal device measures the PRS to obtain the downlink channel response. In one possible implementation, an AI model for positioning can be deployed in the terminal device, and the terminal device can input the multiple downlink channel responses obtained into the AI model, and the AI model outputs the location information of the terminal device. In another possible implementation, an AI model for positioning can be deployed in the LMF. The terminal device can report the obtained downlink channel response to the LMF, or the terminal device can extract features from the obtained downlink channel response based on the AI model and report the features based on the downlink channel response to the LMF. The LMF can input the obtained downlink channel response or the features based on the downlink channel response into the AI model for positioning, and the AI model for positioning outputs the location information of the terminal device.

For example, Figure 5A illustrates an AI-based downlink positioning scenario, showing that the AI model for positioning is deployed on the LMF side. Multiple base stations, namely base station 1, base station 2, and base station 3, send PRSs to the terminal device. The terminal device measures the multiple PRSs to obtain multiple downlink channel responses, namely channel response 1, channel response 2, and channel response 3. The terminal device sends the multiple downlink channel responses to the LMF, which uses the multiple downlink channel responses as input to the AI model for positioning and outputs the location information of the terminal device.

FIG5B illustrates another AI-based downlink positioning scenario. The AI model can be deployed on the terminal device and the LMF side. The terminal device measures the PRSs of multiple base stations to obtain multiple downlink channel responses, namely, channel response 1, channel response 2, and channel response 3. Based on its deployed AI model, the terminal device uses the obtained multiple downlink channel responses as input to the AI model and outputs features based on the multiple downlink channel responses, referred to as multiple channel features, including channel feature 1, channel feature 2, and channel feature 3. The terminal device sends the multiple channel features to the LMF. The LMF uses the multiple downlink channel responses as input to the AI model used for positioning and outputs the terminal device's location information. It will be understood that in the method illustrated in FIG5B , the LMF outputs the terminal device's location. However, unlike the method illustrated in FIG5B , where the input information for the AI model in the LMF is the channel response, in FIG5B , the input information for the AI model in the LMF is the features of the channel response obtained by the terminal device.

It is understandable that the AI model of the terminal device described above can be located within the terminal device or in another device that communicates with the terminal device, such as a cloud server. The downlink channel response described above may be optional. The downlink channel response may be a channel impulse response (CIR), a power delay profile (PDP), a channel frequency response (CFR), an angle of arrival (AOA) corresponding to the terminal device, and/or a time of arrival (TOA) corresponding to the terminal device.

(2) In the uplink positioning scenario, the terminal device may send a sounding reference signal (SRS) to the base station or the base station's cell node. The base station or the base station's cell node measures the SRS to obtain an uplink channel response. The base station or the base station's cell node may then report the obtained uplink channel response to a location management function (LMF) network element, which may also be referred to as an LMF. Alternatively, the base station or the base station's cell node may extract features from the obtained uplink channel response based on an AI model and report the features based on the uplink channel response to the LMF. The LMF may then determine the terminal device's location based on the AI model used for positioning and the obtained uplink channel response or the features based on the uplink channel response.

For example, as shown in Figure 6A, an AI-based uplink positioning scenario can be deployed on the LMF side. Multiple base stations, namely base station 1, base station 2, and base station 3, respectively send channel responses (i.e., uplink channel responses) to the LMF. The LMF uses the channel responses of multiple base stations as input to the AI model and outputs the location information of the terminal device.

For example, as shown in FIG6B , another AI-based uplink positioning scenario is illustrated. The AI model can be deployed on the base station and LMF sides. Each of the multiple base stations uses the acquired channel response as the input of the AI model based on the AI model and outputs features based on the channel response, referred to as channel features. Furthermore, each of the multiple base stations sends the channel features to the LMF. Based on the AI model for positioning deployed by itself, the LMF uses the channel features of multiple base stations as the input of the AI model for positioning and outputs the location information of the terminal device. It can be understood that in the method illustrated in FIG6B , the location of the terminal device is output by the LMF, but unlike the method illustrated in FIG6A , the input information of the AI model in the LMF is the channel response. In FIG6B , the input information of the AI model in the LMF is the feature of the channel response obtained by the terminal device.

The uplink channel response described above may be a channel impulse response (CIR), a power delay profile (PDP), a channel frequency domain response (CFR), an angle of arrival (AOA) corresponding to a base station, and/or a time of arrival (TOA) corresponding to a base station. Furthermore, it is understood that AI-based positioning is also considered a case of wireless sensing estimation.

Currently, in AI-based wireless sensing scenarios, transmission channel estimation results between network elements sometimes lead to unnecessary waste of transmission resources. The present application provides a communication method that can reduce this waste of transmission resources. The communication method provided in the present application is described in detail below.

FIG7 shows a communication method, which mainly includes the following steps.

S701: A second network element measures an mth reference signal among M reference signals to obtain one or more measurement results.

Wherein, M is a positive integer, i.e., the value of M is greater than or equal to 1, and m is a positive integer less than or equal to M, such as m sequentially takes integers from 1 to M. Optionally, the value of M may be indicated by the first network element to the second network element, or the value of M may be determined by the second network element and notified to the first network element, or the value of M may be pre-configured, and this embodiment of the present application is not limited in this regard.

Illustratively, a measurement result obtained for a reference signal may include one or more of the following: reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR). For example, a second network element may obtain two measurement results, namely, a first measurement result and a second measurement result, by measuring the mth reference signal. The first measurement result may be one of RSRP, RSSI, RSRQ, or SINR, and the second measurement result may be one of RSRP, RSSI, RSRQ, or SINR. The first measurement result is different from the second measurement result.

It is understandable that the second network element can receive reference signals from other network elements, and implement the measurement of the channel between the other network elements and the second network element based on the measurement of the reference signals. When M is greater than 1, the M reference signals can come from one or more other network elements, and the embodiments of the present application are not limited to this. Based on this, the second network element can also be understood as a network element for measuring channels, and the second network element can also be replaced and described as a channel measurement network element. Applied to wireless sensing scenarios, the second network element can be a sensing network element or a sensing data measurement network element. The second network element receives the reference signal that has passed through the sensed target, and implements the measurement of the channel between the sensed target and the second network element based on the measurement of the reference signal.

S702: The second network element sends M channel reports to the first network element.

In the case where the second network element is replaced by a channel measurement network element, the first network element can also be understood as a network element for analyzing channel measurement results, that is, the first network element can also be replaced by an information processing network element. Optionally, the second network element can send M channel reports to the first network element via K signaling; wherein K is 1, that is, the second network element can include the M channel reports in one signaling and send the one signaling to the first network element; or, K is M, that is, the second network element sends M signaling to the first network element, each of the M signaling includes one channel report among the M channel reports, and different signalings among the M signalings include different channel reports; or, K is an integer greater than 1 and less than M, then the second network element sends K signaling to the first network element, each of the K signalings includes at least one channel report among the M channel reports, and there is at least one signaling among the K signalings that includes at least two channel reports among the M channel reports.

The M channel reports correspond one-to-one to the M reference signals. Step S702 may also be understood as: the second network element obtains one or more measurement results by measuring the mth reference signal among the M reference signals, and sends the mth channel report among the M channel reports.

Taking the example of the second network element measuring the mth reference signal among M reference signals to obtain the first measurement result and the second measurement result, a first condition corresponding to the mth reference signal can be configured for the first measurement result obtained by measuring the mth reference signal. The first condition is used by the second network element to decide the content included in the mth channel report, that is, the second network element can selectively send the mth channel report to the first network element under the constraint of the first condition corresponding to the mth reference signal. The first condition can also be replaced by describing it as a first criterion or other names, and the embodiments of the present application are not limited to this. For example, when the first measurement result meets the first condition corresponding to the mth reference signal, the mth channel report includes one or more of the following: the first measurement result obtained by measuring the mth reference signal; the second measurement result obtained by measuring the mth reference signal; and the channel information obtained based on the mth reference signal. For another example, when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result.

Optionally, the first condition corresponding to the mth reference signal may be defined by the protocol, or the first condition corresponding to the mth reference signal may be pre-configured in the second network element, or the first network element may indicate the first condition corresponding to the mth reference signal to the second network element. As an example, FIG7 illustrates the optional step S700 with a dotted line: the first network element sends first information to the second network element, and the first information indicates the first condition corresponding to the mth reference signal. It is understandable that, when executing S700, S700 and S701 are executed simultaneously, or S700 is executed first and then S701, or S701 is executed first and then S700, and the embodiment of the present application is not limited to this.

Exemplarily, the first condition corresponding to the mth reference signal indicates a parameter range that the first measurement result obtained by measuring the mth reference signal needs to meet, which is manifested as: if the first measurement result obtained by the second network element measuring the mth reference signal is included in the parameter range, it can be determined that the first measurement result meets the first condition corresponding to the mth reference signal; if the first measurement result obtained by the second network element measuring the mth reference signal is not included in the parameter range, it can be determined that the first measurement result does not meet the first condition.

Exemplarily, the first condition corresponding to the mth reference signal indicates a threshold that the first measurement result obtained by measuring the mth reference signal needs to reach, which is manifested as: if the first measurement result obtained by the second network element measuring the mth reference signal is greater than or equal to the threshold, it can be determined that the first measurement result meets the first condition corresponding to the mth reference signal; if the first measurement result obtained by the second network element measuring the mth reference signal is less than the threshold, it can be determined that the first measurement result does not meet the first condition.

Furthermore, in one possible design, the first conditions corresponding to the M reference signals may be the same, that is, when the value of m is different, the first conditions corresponding to the m-th reference signal are the same. Based on this design, the number of first conditions carried in the first information described in S700 is 1. In the example where the first condition indicates a parameter range, the first conditions corresponding to the M reference signals being the same can be understood as: the parameter ranges corresponding to the M reference signals being the same, that is, when the value of m is different, the range parameter values corresponding to the m-th reference signal being the same; in the example where the first condition indicates a threshold, the first conditions corresponding to the M reference signals being the same can be understood as: the threshold values corresponding to the M reference signals being the same, that is, when the value of m is different, the threshold values corresponding to the m-th reference signal being the same.

In another possible design, the first conditions corresponding to the M reference signals may be different, that is, when the value of m is different, the first condition corresponding to the m-th reference signal is different. Based on this design, the number of first conditions carried in the first information described in S700 is M. In the example where the first condition indicates a parameter range, the different first conditions corresponding to the M reference signals can be understood as: the M reference signals respectively corresponding to different parameter ranges have different values, that is, when the value of m is different, the value of the range parameter corresponding to the m-th reference signal is different; in the example where the first condition indicates a threshold, the different first conditions corresponding to the M reference signals can be understood as: the M reference signals respectively corresponding to different threshold values, that is, when the value of m is different, the value of the threshold corresponding to the m-th reference signal is different.

Based on the above design, the channel estimation result (ie, channel information) may be transmitted only when the first measurement result satisfies the first condition. Such a design can reduce the waste of transmission resources.

Furthermore, the first network element may utilize AI technology to perform wireless sensing based on the received M first channel reports to obtain corresponding sensing results. For example, after executing S702, step S703 may also be executed. It is understood that S703 is indicated as an optional step by a dotted line in FIG7 .

S703: The first network element determines a perception result according to the M channel reports and at least one model.

In a possible implementation, the first network element may determine the first perception result based on M channel reports and the first model.

Among them, the first model is obtained by training based on multiple channel reports and/or data sets related to channel reports received historically, and the first model is used to analyze the corresponding perception results from the input data. In this implementation, the input of the first model is determined based on the M channel reports, and the output of the first model includes the first perception result. For example, when M is 1, the input of the first model may include the information carried in one channel report; for example, when M is greater than 1, the input of the first model may include the information after the information carried by the M channel reports is spliced together; for example, when M is greater than 1, the information carried by the M channel reports may be subjected to the same preprocessing to obtain multiple preprocessed information, and the multiple preprocessed information may be used as the input of the first model.

In another possible implementation, when M is greater than 1, the first network element can determine M perception results based on M channel reports and M models respectively; wherein the M models correspond one-to-one to the M channel reports, and the input of the mth model among the M models is based on the mth model in the M channel reports, and then the M perception results are fused to obtain a final perception result.

For ease of implementation, taking the aforementioned M equal to 1 as an example, FIG8 illustrates a communication method, which mainly includes the following steps.

S801. A first network element sends first information to a second network element, where the first information is used to indicate a first condition.

The definition of the first condition can be understood by referring to the description under S702 above. In addition, referring to the solution described in FIG7 , it can also be understood that the first condition can also be defined by the protocol or pre-configured in the second network element. In this case, the first network element does not need to send the first information, so S801 is an optional step, which is indicated by a dotted line in FIG8 .

S802: The second network element sends a first channel report to the first network element according to the first condition.

For example, the second network element receives a first reference signal and measures the first reference signal to obtain a first measurement result and a second measurement result; wherein the first condition is configured for the first measurement result, the second network element can determine whether the first measurement result meets the first condition, and determine the information included in the first channel report based on the judgment result.

In one possible implementation, when the first measurement result of the second network element measuring the first reference signal meets the first condition, the second network element obtains (such as estimating) first channel information based on the first reference signal, and includes one or more of the following in the first channel report: the first measurement result; the second measurement result; the first channel information.

For example, the first measurement result includes the SINR corresponding to the first reference signal, and the second measurement result includes the RSRP, RSSI, or RSRQ corresponding to the first reference signal. The first condition indicates a threshold corresponding to the SINR, such as 10dB. When the SINR corresponding to the first reference signal is greater than or equal to 10dB, the second network element can determine that the first measurement result meets the first condition, and then estimate the first channel information based on the first reference signal, and carry at least one of the first measurement result (i.e., SINR), the second measurement result (such as RSRP), and the first channel information in the first channel report. The following is a detailed description using the example where the first measurement result meets the first condition and the first channel report includes the first measurement result (SINR) and the first channel information.

Taking the wireless sensing scenario illustrated in (c) of Figure 3 as an example, the second network element may be a UE, and the first network element may be a core network device such as an SMF or an AMF. The first reference signal received by the UE from the base station may be a PRS, a channel state information reference signal (CSI-RS), or a demodulation reference signal (DMRS). Taking the first reference signal being a PRS as an example, the UE measures the PRS and obtains a first measurement result that is the SINR corresponding to the PRS. When the SINR corresponding to the PRS is greater than or equal to 10dB, the UE may determine that the first measurement result meets the first condition. The first channel information estimated by the UE based on the PRS may be the downlink channel response described above, and the UE may carry the SINR corresponding to the PRS and the downlink channel response in the first channel report sent to the core network device.

Taking the wireless sensing scenario illustrated in (d) in Figure 3 as an example, the second network element may be a base station, and the first network element may be a core network device such as an SMF or an AMF. The first reference signal received by the base station from the UE may be an SRS, and the base station measures the SRS to obtain a first measurement result which is the SINR corresponding to the SRS. When the SINR corresponding to the SRS is greater than or equal to 10dB, the base station may determine that the first measurement result meets the first condition, and the first channel information estimated by the base station based on the SRS may be the uplink channel response described above, and the first channel report that the base station may send to the core network device carries the SINR corresponding to the SRS and the uplink channel response.

Taking the wireless sensing scenario illustrated in (f) of Figure 3 as an example, the second network element may be a second UE, and the first network element may be a base station. The first reference signal received by the second UE from the first UE may be an SL-PRS, and the second UE measures the SL-PRS to obtain a first measurement result as the SINR corresponding to the SL-PRS. When the SINR corresponding to the SL-PRS is greater than or equal to 10dB, the second UE may determine that the first measurement result satisfies the first condition, and the second UE may send a first channel report to the base station that carries the SINR corresponding to the SL-PRS and the first channel information.

In another possible implementation, when the first measurement result of the second network element measuring the first reference signal does not meet the first condition, the second network element does not perform the operation of estimating the first channel information based on the first reference signal, and includes the first measurement result or the second measurement result in the first channel report, but does not include the aforementioned first channel information.

For example, the first measurement result includes the SINR corresponding to the first reference signal, and the second measurement result includes the RSRP, RSSI, or RSRQ corresponding to the first reference signal. The first condition indicates a threshold corresponding to the SINR, such as 10 dB. If the SINR corresponding to the first reference signal is less than 10 dB, the second network element can determine that the first measurement result does not meet the first condition, and then carry the first measurement result (i.e., SINR) or the second measurement result (such as RSRP) in the first channel report. The following is a detailed description using the example where the first measurement result does not meet the first condition and the first channel report only includes the first measurement result (SINR).

Taking the wireless sensing scenario illustrated in (c) of Figure 3 as an example, the second network element may be a UE, and the first network element may be a core network device such as an SMF or AMF. The first reference signal received by the UE from the base station may be a CSI-RS, a DMRS, or a PRS. Taking the first reference signal being a PRS as an example, the UE measures the PRS and obtains a first measurement result that is the SINR corresponding to the PRS. If the SINR corresponding to the PRS is less than 10 dB, the UE may determine that the first measurement result does not meet the first condition, and the UE may then carry only the SINR corresponding to the PRS in the first channel report sent to the core network device.

Taking the wireless sensing scenario illustrated in (d) of Figure 3 as an example, the second network element may be a base station, and the first network element may be a core network device such as an SMF or AMF. The first reference signal received by the base station from the UE may be an SRS, and the base station measures the SRS to obtain a first measurement result that is the SINR corresponding to the SRS. If the SINR corresponding to the SRS is less than 10dB, the base station may determine that the first measurement result does not meet the first condition, and the base station may send a first channel report to the core network device that only carries the SINR corresponding to the SRS.

Taking the wireless sensing scenario illustrated in (f) of Figure 3 as an example, the second network element may be a second UE, and the first network element may be a base station. The first reference signal received by the second UE from the first UE may be an SL-PRS, and the second UE measures the SL-PRS to obtain a first measurement result that is the SINR corresponding to the SL-PRS. When the SINR corresponding to the SL-PRS is less than 10dB, the second UE may determine that the first measurement result does not meet the first condition, and then the second UE may send a first channel report to the base station that only carries the SINR corresponding to the SL-PRS.

In the above embodiment, by configuring the first condition, the amount of data in the first channel report sent by the channel measurement network element is controlled to be reduced, thereby reducing the waste of transmission resources.

Further optionally, the first network element may utilize AI technology to determine the result of wireless sensing based on the received first channel report. For example, after executing S802, step S803 may also be executed. It is understood that step S803 is indicated as an optional step in FIG8 by a dotted line.

S803: The first network element determines a first perception result according to the first channel report and the first model.

It is understood that the first model is trained based on multiple channel reports and/or data sets related to channel reports received historically, and the first model is used to analyze the corresponding perception results from the input data. In step S803, the input of the first model is determined based on the first channel report, and the output of the first model includes the first perception result.

Taking the wireless sensing scenario illustrated in (c) of Figure 3 as an example, the second network element may be a UE, and the first network element may be a core network device such as an SMF or an AMF. When the SINR corresponding to the PRS sent by the base station to the UE is greater than or equal to 10 dB, the first channel report received by the core network device includes the SINR corresponding to the PRS and the first channel information, and the core network device inputs the SINR corresponding to the PRS and the first channel information into the first model; when the SINR corresponding to the PRS sent by the base station to the UE is less than 10 dB, the first channel report received by the core network device includes the SINR corresponding to the PRS, and the core network device inputs the SINR corresponding to the PRS into the first model; in these two cases, assuming that the target object of the wireless sensing includes a pedestrian, the first sensing result output by the first model indicates that the target object is a pedestrian, or the first sensing result indicates the state of the pedestrian, such as stationary, walking, running, or falling.

Taking the wireless sensing scenario illustrated in (d) of Figure 3 as an example, the second network element may be a base station, and the first network element may be a core network device such as an SMF or an AMF. When the SINR corresponding to the SRS sent by the UE to the base station is greater than or equal to 10 dB, the first channel report received by the core network device includes the SINR corresponding to the SRS and the first channel information, and the core network device inputs the SINR corresponding to the SRS and the first channel information into the first model; when the SINR corresponding to the SRS sent by the UE to the base station is less than 10 dB, the first channel report received by the core network device includes the SINR corresponding to the SRS, and the core network device inputs the SINR corresponding to the SRS into the first model; in these two cases, assuming that the target object of the wireless sensing includes a drone, the first sensing result output by the first model indicates that the target object is a drone, or the first sensing result indicates the state of the drone, such as stationary or flying, or the first sensing result indicates the flight parameters of the drone between the base station and the UE, such as flight speed or flight altitude.

Taking the wireless sensing scenario illustrated in (f) of Figure 3 as an example, the second network element may be the second UE, and the first network element may be the base station. When the SINR corresponding to the SL-PRS sent by the first UE to the second UE is greater than or equal to 10dB, the first channel report received by the base station includes the SINR corresponding to the SL-PRS and the first channel information, and the base station inputs the SINR corresponding to the SL-PRS and the first channel information into the first model; when the SINR corresponding to the SL-PRS sent by the first UE to the second UE is less than 10dB, the first channel report received by the base station includes the SINR corresponding to the SL-PRS, and the base station inputs the SINR corresponding to the SL-PRS into the first model; in these two cases, assuming that the target object of the wireless sensing includes a vehicle, the first sensing result output by the first model indicates that the target object is a vehicle, or the first sensing result indicates the state of the vehicle, such as stationary or moving, or the first sensing result indicates the driving parameters of the vehicle, such as driving speed or driving route, etc.

In the above example, when the measurement result of the reference signal does not meet the first condition, that is, the channel environment is poor, the channel information estimated based on the reference signal is not used as the input of the model. This can avoid the impact of low channel estimation accuracy on model performance and help improve model accuracy.

Taking the aforementioned M equal to 2 and the second network element measuring the first reference signal and the second reference signal as an example, Figure 9 illustrates a communication method, in which the first network element can jointly analyze the two channel reports reported by the second network element to obtain the corresponding perception results. The method mainly includes the following steps.

S901. A first network element sends first information to a second network element, where the first information indicates a first condition corresponding to a first reference signal and a first condition corresponding to a second reference signal.

The first condition corresponding to the first reference signal is configured for a first measurement result obtained by the first network element measuring the first reference signal. The definition of the first condition corresponding to the first reference signal can be specifically understood with reference to the description in S702 and S801, and is not further described in this embodiment of the present application. The first condition corresponding to the second reference signal is configured for a first measurement result obtained by the first network element measuring the second reference signal. The definition of the first condition corresponding to the second reference signal can be specifically understood with reference to the description in S702 and S801, and is not further described in this embodiment of the present application.

Optionally, the first condition corresponding to the first reference signal and the first condition corresponding to the second reference signal are the same or different. Taking the first measurement result as SINR, and the first condition indicating the threshold that the SINR needs to meet as an example, the first condition corresponding to the first reference signal and the first condition corresponding to the second reference signal are the same, which can be understood as: the first condition corresponding to the first reference signal indicates the first threshold (such as 10dB), and the first condition corresponding to the second reference signal also indicates the first threshold (such as 10dB); the first condition corresponding to the first reference signal and the first condition corresponding to the second reference signal are different, which can be understood as: the first condition corresponding to the first reference signal indicates the first threshold (such as 10dB), and the first condition corresponding to the second reference signal indicates the second threshold (such as 20dB).

S902a: The second network element sends a first channel report to the first network element according to the first condition corresponding to the first reference signal.

For example, the first measurement result obtained by the second network element for measuring the first reference signal is SINR, recorded as SINR1; the first condition corresponding to the first reference signal indicates that the first threshold is 10dB. If SINR1 is greater than or equal to 10dB, the second network element can determine that SINR1 meets the first condition, estimate the channel information based on SINR1, and carry SINR1 and the channel information obtained based on SINR1 in the first channel report. If SINR1 is less than 10dB, the second network element can determine that SINR1 does not meet the first condition, and only carry SINR1 in the first channel report.

S902b: The second network element sends a second channel report to the first network element according to the first condition corresponding to the second reference signal.

For example, the first measurement result obtained by the second network element for measuring the first reference signal is SINR, which is recorded as SINR2; the first condition corresponding to the second reference signal indicates that the second threshold is 20dB. When SINR2 is greater than or equal to 20dB, the second network element can determine that SINR2 meets the first condition corresponding to the second reference signal, and then estimate the channel information based on SINR2, and carry SINR2 and the channel information obtained based on SINR2 in the second channel report. When SINR2 is less than 20dB, the second network element can determine that SINR2 does not meet the first condition corresponding to the second reference signal, and then only carry SINR2 in the second channel report.

This application does not limit the execution order of the above-mentioned S902a and S902b. For example, S902a and S902b can be executed at the same time. For example, the second network element can send a second message to the first network element, and the second message includes a first channel report and a second channel report; or S902a can be executed first and then S902b, or S902b can be executed first and then S902a.

The following describes in detail possible implementations of the first channel report and the second channel report with reference to examples.

In a first possible implementation, the first measurement result obtained by the second network element on the first reference signal meets the first condition corresponding to the first reference signal, and the first channel report includes the first measurement result obtained on the first reference signal and the channel information obtained based on the first reference signal; and the first measurement result obtained by the second network element on the second reference signal meets the first condition corresponding to the second reference signal, and the second channel report includes the first measurement result obtained on the second reference signal and the channel information obtained based on the second reference signal.

Taking the wireless sensing scenario illustrated in (c) of Figure 3 as an example, the second network element may be a UE, and the first network element may be a core network device such as an SMF or AMF. The UE receives a first reference signal from base station 1, and the UE receives a second reference signal from base station 2. Base station 1 may be a base station belonging to a neighboring cell of the UE. The UE receiving the first reference signal from base station 1 can also be understood as the UE receiving the first reference signal from the neighboring cell. Base station 2 may be a base station belonging to a serving cell of the UE. The UE receiving the second reference signal from base station 1 can also be understood as the UE receiving the second reference signal from the serving cell. The first reference signal and the second reference signal may both be CSI-RS, PRS, or DMRS. For example, if the first and second reference signals are CSI-RS, the first reference signal is denoted as CSI-RS1, and the second reference signal is denoted as CSI-RS2. The UE measures CSI-RS1 to obtain the SINR corresponding to CSI-RS1, and the UE measures CSI-RS2 to obtain the SINR corresponding to CSI-RS2. Taking the first condition corresponding to the first reference signal indicating the first threshold (10dB) and the first condition corresponding to the second reference signal indicating the second threshold (20dB) as an example, when the SINR corresponding to CSI-RS1 is greater than or equal to 10dB, the CSI estimated by the UE based on CSI-RS1 is recorded as CSI1, and then the UE can carry the SINR and CSI1 corresponding to CSI-RS1 in the first channel report sent to the core network device; when CSI-RS2 is greater than or equal to 20dB, the CSI estimated by the UE based on CSI-RS2 is recorded as CSI2, and then the UE can carry the SINR and CSI2 corresponding to CSI-RS2 in the second channel report sent to the core network device.

In a second possible implementation, the first measurement result obtained by the second network element on the first reference signal meets the first condition corresponding to the first reference signal, and the first channel report includes the first measurement result obtained on the first reference signal and the channel information obtained based on the first reference signal; and the first measurement result obtained by the second network element on the second reference signal does not meet the first condition corresponding to the second reference signal, and the second channel report only includes the first measurement result obtained based on the second reference signal.

Taking the wireless sensing scenario illustrated in (d) of Figure 3 as an example, the second network element may be a base station, and the first network element may be a core network device such as an SMF or AMF. The base station receives a first reference signal from UE1 and a second reference signal from UE2. The first reference signal and the second reference signal may both be SRSs, denoted as SRS1 and SRS2, respectively. UE1 and UE2 may be UEs within the base station's coverage area. For example, where the first condition corresponding to the first reference signal indicates a first threshold (10 dB), and the first condition corresponding to the second reference signal indicates a second threshold (20 dB), the base station measures SRS1 to obtain the SINR corresponding to SRS1. If the SINR corresponding to SRS1 is greater than or equal to 10 dB, the base station may estimate channel information (such as an uplink channel response) based on SRS1. The base station may then send a first channel report to the core network device that carries the SINR corresponding to SRS1 and the uplink channel response. The base station measures SRS2 to obtain the SINR corresponding to SRS2. If the SINR corresponding to SRS2 is less than 20 dB, the base station may send a second channel report to the core network device that carries only the SINR corresponding to SRS2.

In a third possible implementation, the first measurement result obtained by the second network element for measuring the first reference signal does not meet the first condition corresponding to the first reference signal, and the first channel report only includes the first measurement result obtained for measuring the first reference signal; and the first measurement result obtained by the second network element for measuring the second reference signal meets the first condition corresponding to the second reference signal, and the second channel report includes the first measurement result obtained for measuring the second reference signal and the channel information obtained based on the second reference signal.

Taking the wireless sensing scenario illustrated in (c) of Figure 3 as an example, the second network element may be a UE, and the first network element may be a core network device such as an SMF or AMF. The UE receives a first reference signal from base station 1, and the UE receives a second reference signal from base station 2. Base station 1 may be a base station belonging to a neighboring cell of the UE. The UE receiving the first reference signal from base station 1 can also be understood as the UE receiving the first reference signal from the neighboring cell. Base station 2 may be a base station belonging to a serving cell of the UE. The UE receiving the second reference signal from base station 1 can also be understood as the UE receiving the second reference signal from the serving cell. The first reference signal and the second reference signal may both be CSI-RS, PRS, or DMRS. For example, if the first and second reference signals are CSI-RS, the first reference signal is denoted as CSI-RS1, and the second reference signal is denoted as CSI-RS2. The UE measures CSI-RS1 to obtain a first measurement result, which is the SINR corresponding to CSI-RS1. The UE measures CSI-RS2 to obtain a second measurement result, which is CSI-RS2. Taking the first condition corresponding to the first reference signal indicating the first threshold (10dB) and the first condition corresponding to the second reference signal indicating the second threshold (20dB) as an example, when CSI-RS1 is less than 10dB, the UE can only carry the SINR corresponding to CSI-RS1 in the first channel report sent to the core network device; when CSI-RS2 is greater than or equal to 20dB, the UE estimates the CSI based on CSI-RS2 (denoted as CSI2), and then the UE can carry the SINR corresponding to CSI-RS2 and CSI2 in the second channel report sent to the core network device.

In a fourth possible implementation, the first measurement result obtained by the second network element for measuring the first reference signal does not meet the first condition corresponding to the first reference signal, and the first channel report includes the first measurement result obtained for measuring the first reference signal; and the first measurement result obtained by the second network element for measuring the second reference signal does not meet the first condition corresponding to the second reference signal, and the second channel report includes the first measurement result obtained based on the second reference signal.

Taking the wireless sensing scenario illustrated in (d) of Figure 3 as an example, the second network element may be a base station, and the first network element may be a core network device such as an SMF or AMF. The base station receives a first reference signal from UE1 and a second reference signal from UE2. The first reference signal and the second reference signal may both be SRSs, denoted as SRS1 and SRS2, respectively. UE1 and UE2 may be UEs within the base station's coverage area. For example, if the first condition corresponding to the first reference signal indicates a first threshold (10 dB), and the first condition corresponding to the second reference signal indicates a second threshold (20 dB), the base station measures SRS1 to obtain the SINR corresponding to SRS1. If the SINR corresponding to SRS1 is less than 10 dB, the base station may send a first channel report to the core network device containing only the SINR corresponding to SRS1. The base station then measures SRS2 to obtain the SINR corresponding to SRS2. If the SINR corresponding to SRS2 is less than 20 dB, the base station may send a second channel report to the core network device containing only the SINR corresponding to SRS2.

Further, optionally, the first network element may utilize AI technology to determine the wireless sensing result based on the received first channel report and second channel report. For example, in one possible implementation, after executing S902a and S902b, step S903 may also be executed. In another possible implementation, after executing S902a and S902b, steps S904 to S906 may also be executed. It will be appreciated that S903 and S904 to S906 are indicated as optional steps by dashed lines in Figure 9.

S903: The first network element determines a first perception result according to the first channel report, the second channel report and the first model.

This step can be understood with reference to the description in S803 , where the input of the first model is determined based on the first channel report and the second channel report, and the output of the first model includes the first perception result.

In one possible implementation, the information carried in the first channel report and the information carried in the second channel report can be spliced together as input to the first model. For example, Figure 10A illustrates that the first channel report includes the measurement result of the first reference signal and the first channel information. The measurement result of the first reference signal includes at least one measurement result obtained by measuring the first reference signal, and the first channel information is channel information obtained based on the first reference signal; the second channel report only includes the measurement result of the second reference signal. The measurement result of the second reference signal includes at least one measurement result obtained by measuring the second reference signal. The first network element can splice the measurement result of the first reference signal, the first channel information, and the measurement result of the second reference signal together and input them into the first model. The output of the first model is the first perception result.

Taking the first possible implementation as an example, in the wireless sensing scenario illustrated in (c) of FIG3 , the second network element may be a UE, and the first network element may be a core network device such as an SMF or AMF. If the SINR corresponding to the CSI-RS1 sent by base station 1 to the UE is greater than or equal to 10 dB, and the SINR corresponding to the CSI-RS2 sent by the base station to UE2 is greater than or equal to 20 dB, then the first channel report received by the core network device includes the SINR corresponding to CSI-RS1 and CSI1, and the second channel report includes the SINR corresponding to CSI-RS2 and CSI2. Based on this, the input of the first model includes the concatenation of the SINR corresponding to CSI-RS1, CSI1, and the SINR corresponding to CSI-RS2 and CSI2. Assuming that the target object of the wireless sensing includes a pedestrian, one possible result is: the first sensing result output by the first model indicates that the target object is a pedestrian, or the first sensing result indicates the state of the pedestrian, such as being stationary, walking, running, or fallen.

In another possible implementation, the information carried in the first channel report and the information carried in the second channel report can be uniformly preprocessed and then input into the first model. For example, Figure 10B illustrates that the first channel report only includes the measurement results of the first reference signal, and the measurement results of the first reference signal include at least one measurement result obtained by measuring the first reference signal. The second channel report includes the measurement results of the second reference signal and second channel information; wherein the measurement results of the second reference signal include at least one measurement result obtained by measuring the second reference signal, and the second channel information is channel information obtained based on the second reference signal. The first network element preprocesses the measurement results of the first reference signal to obtain first preprocessed information, and preprocesses the measurement results of the second reference signal and the second channel information to obtain second preprocessed information. The first network element then inputs the first preprocessed information and the second preprocessed information into the first model, and the output of the first model is the first perception result. Optionally, the aforementioned information preprocessing can be based on AI or non-AI information preprocessing, and this is not limited in this embodiment of the present application.

Taking the second possible implementation as an example, in the wireless sensing scenario illustrated in (d) of Figure 3 , the second network element may be a base station, and the first network element may be a core network device such as an SMF or AMF. The base station measures SRS1 from UE1 and obtains a SINR corresponding to SRS1 greater than or equal to 10dB, and measures SRS2 from UE2 and obtains a SINR corresponding to SRS2 less than 20dB. The core network device's first channel report carries the SINR corresponding to SRS1 and the uplink channel response, and the second channel report carries the SINR corresponding to SRS2. Based on this, the core network device can pre-process the SINR corresponding to SRS1 and the uplink channel response to obtain first pre-processing information, and pre-process the SINR corresponding to SRS2 to obtain second pre-processing information; and then input the first pre-processing information and the second pre-processing information into the first model. Assuming that the target object of wireless perception includes a drone, a possible result is: the first perception result output by the first model indicates that the target object is a drone, or the first perception result indicates the state of the drone, such as stationary or flying, or the first perception result indicates the flight parameters of the drone between the base station and the UE, such as flight speed or flight altitude.

S904: The first network element determines a second perception result according to the first channel report and the second model.

The input of the second model is determined based on the first channel report, and the output of the second model includes the second perception result.

S905: The first network element determines a third perception result based on the second channel report and the third model.

The input of the third model is determined based on the second channel report, and the output of the third model includes the third perception result.

S906: The first network element fuses the second perception result and the third perception result to obtain a fourth perception result.

For example, in the wireless sensing scenario illustrated in (c) of Figure 3, the second sensing result indicates that the target object is a pedestrian, and the third sensing result indicates that the pedestrian is in a fallen state. Then the fourth sensing result obtained by fusing the second sensing result and the third sensing result indicates that a pedestrian fall is detected.

For example, in the wireless sensing scenario illustrated in (d) of FIG3 , the second sensing result indicates that the target object is a drone, and the third sensing result also indicates a drone. Then, the fourth sensing result obtained by fusing the second sensing result and the third sensing result indicates that a drone is detected.

For example, in the wireless sensing scenario shown in (f) in Figure 3, the second perception result indicates that the target object is a vehicle, and the third perception result also indicates the vehicle's driving speed. The fourth perception result obtained by fusing the second perception result and the third perception result indicates that the vehicle is detected traveling at a certain speed.

For ease of understanding, for example, FIG10C illustrates that the measurement result of the first reference signal included in the first channel report from the second network element is input into the second model to obtain a second perception result, and the measurement result of the second reference signal and the second channel information included in the second channel report from the third network element are input into the third model to obtain a third perception result; and then the second perception result and the third perception result are fused to obtain a fourth perception result. The definitions of the measurement result of the first reference signal, the measurement result of the second reference signal, and the second channel information can be understood with reference to the description in FIG10B. Optionally, the aforementioned fusion of the perception results can be based on an AI preprocessing method or a non-AI information preprocessing method, which is not limited in this embodiment of the present application.

The solution illustrated in FIG9 takes a channel measurement network element reporting two channel reports as an example, and describes a solution in which the information processing network element uses AI technology to jointly process the two channel reports. In other communication scenarios, AI technology can also be used to jointly process more than two channel reports reported by the channel measurement network element. For example, in the downlink positioning scenario illustrated in FIG5A, the channel measurement network element is a terminal device, and the information processing network element is an LMF. Similar to the solution illustrated in FIG9, the LMF can configure at least one condition corresponding to the channel measurement to the terminal device; the terminal device measures the reference signal (such as PRS) sent by base station 1, base station 2, and base station 3 respectively, and determines the channel report according to the at least one condition. For example, the at least one condition includes condition 1, condition 2, and condition 3, where condition 1 is used to constrain (or measure) the measurement result of the PRS from base station 1, condition 2 is used to constrain the measurement result of the PRS from base station 2, and condition 3 is used to constrain the measurement result of the PRS from base station 3. For ease of distinction, the PRSs of base stations 1 to 3 are denoted as PRS1, PRS2, and PRS3, respectively. If the measurement result of PRS1 satisfies condition 1, the terminal device carries the measurement result of PRS1 and channel response 1 estimated based on PRS1 in channel report 1. Alternatively, if the measurement result of PRS1 does not satisfy condition 1, the terminal device carries only the measurement result of PRS1 in channel report 1. If the measurement result of PRS2 satisfies condition 2, the terminal device carries the measurement result of PRS2 and channel response 2 estimated based on PRS2 in channel report 2. Alternatively, if the measurement result of PRS2 does not satisfy condition 2, the terminal device carries only the measurement result of PRS2 in channel report 2. If the measurement result of PRS3 satisfies condition 3, the terminal device carries the measurement result of PRS3 and channel response 3 estimated based on PRS3 in channel report 3. Alternatively, if the measurement result of PRS3 does not satisfy condition 3, the terminal device carries only the measurement result of PRS3 in channel report 3. Furthermore, LMF can use AI technology to jointly process channel reports 1 to 3 reported by the terminal device. For example, according to the method described in S903, LMF can determine the corresponding perception results, that is, the location information of the terminal device, based on channel report 1, channel report 2, channel report 3 and the AI model.

In the above embodiment, multiple conditions corresponding to channel measurement are indicated for the same channel measurement network element respectively, and the amount of data when the channel measurement network element reports multiple channel reports is controlled and reduced, thereby reducing the waste of transmission resources.

As shown in Figure 11, an embodiment of the present application further provides a communication method that can reduce transmission resource waste in sensor devices. This communication method uses a third network element representing a sensor device such as a radar or camera, and the interaction between the third network element and the first network element as an example for detailed description.

S1101. A first network element sends second information to a third network element, where the second information indicates a second condition.

Exemplarily, in a scenario where the third network element collects image information, the second condition may indicate a condition that the image information needs to meet.

In one possible implementation, the second condition indicates an image difference threshold. When the difference between the image information currently collected by the third network element and the image information collected last time is greater than or equal to the image difference threshold, the third network element can determine that the currently collected image information meets the second condition; or, when the difference between the image information currently collected by the third network element and the image information collected last time is less than the image difference threshold, the third network element can determine that the currently collected image information does not meet the second condition.

For example, the relative rate of change is used to represent the difference between images, and the image difference threshold can be a ratio threshold (such as 50%). Based on this, the difference between the image information currently collected by the third network element and the image information collected last time can also be replaced by: the ratio of the difference between the image information currently collected by the third network element and the image information collected last time to the image information collected last time. When the ratio is greater than or equal to the ratio threshold (such as 50%), the third network element can determine that the currently collected image information meets the second condition; or when the third network element determines that the ratio is less than the ratio threshold (such as 50%), the third network element can determine that the currently collected image information does not meet the second condition.

In another possible implementation, the second condition indicates an image difference range. When the difference between the image information currently collected by the third network element and the image information collected last time is included in the image difference range, the third network element can determine that the currently collected image information meets the second condition; or, when the difference between the image information currently collected by the third network element and the image information collected last time is not included in the image difference range, the third network element can determine that the currently collected image information does not meet the second condition.

For example, the relative rate of change is used to represent the difference between images, and the image difference range can be a ratio range (such as 60% to 100%). Based on this, the difference between the image information currently collected by the third network element and the image information collected last time can also be replaced by the description as: the ratio of the difference between the image information currently collected by the third network element and the image information collected last time to the image information collected last time. When the ratio is included in the ratio range (such as 60% to 100%), the third network element can determine that the currently collected image information meets the second condition; or the third network element can determine that the currently collected image information does not meet the second condition when it is determined that the ratio is not included in the ratio range (such as 60% to 100%).

Furthermore, the third network element may, according to the above implementation manner, execute the following step S1102 when it is determined that the previously collected image information meets the second condition.

S1102: The second network element sends image information to the first network element.

It can be understood that the image information sent by the second network element is the currently collected image information described in S1101, and the currently collected image information meets the second condition.

In the above solution, the waste of transmission resources caused by the transmission of image information by the sensor device is reduced by configuring conditions.

In conjunction with the methods described in Figures 7 to 9 and 11, embodiments of the present application further provide a communication method that supports an information processing network element in performing a joint analysis of at least one channel report reported by a channel measurement network element (i.e., a second network element) and image information reported by a sensing device (a third network element) to obtain corresponding perception results. Taking the second network element reporting a first channel report as an example, Figure 12 illustrates a communication method that primarily includes the following steps.

S1201a. The first network element sends first information to the second network element, where the first information is used to indicate a first condition.

The first condition in this step can be understood by referring to the description in S702 and S801, and will not be elaborated in detail in this embodiment of the present application.

S1201b. The first network element sends second information to the third network element, where the second information is used to indicate a second condition.

The second condition in this step can be understood by referring to the description in S1101, and will not be elaborated in this embodiment of the present application.

This application does not limit the execution order of the above S1201a and S1201b. For example, S1201a and S1201b can be executed at the same time; or S1201a can be executed first and then S1201b, or S1201b can be executed first and then S1201a.

S1202a: The second network element sends a first channel report to the first network element.

This step can be understood with reference to the description in S802, and will not be described in detail in this embodiment of the present application.

S1202b: The third network element sends image information to the first network element.

The image information sent by the second network element is the currently collected image information, and the currently collected image information meets the second condition. That is, the second network element sends the image information when the currently collected image information meets the second condition.

This application does not limit the execution order of the above S1202a and S1202b. For example, S1202a and S1202b can be executed at the same time; or S1202a can be executed first and then S1202b, or S1202b can be executed first and then S1202a.

Further optionally, the first network element may use AI technology to determine the result of wireless sensing based on the received first channel report and image information. For example, in one possible implementation, after executing S1202a and S1202b, step S1203 may also be executed.

S1203: The first network element determines a fifth perception result based on the first channel report, the image information, and the fourth model.

The input of the fourth model is determined based on the first channel report and the image information, and the output of the fourth model includes the fifth perception result. For example, the first network element concatenates the information included in the first channel report with the image information and inputs them into the fourth model, and the fourth model outputs the fifth perception result. In another example, the first network element preprocesses the first channel report to obtain first preprocessed information and preprocesses the image to obtain third preprocessed information; then, the first and third preprocessed information are input into the fourth model, and the fourth model outputs the fifth perception result.

The present application also provides a communication method that supports an information processing network element in jointly analyzing channel reports reported by multiple channel measurement network elements to obtain corresponding perception results. Figure 13 illustrates another communication method, using the example of a first network element (an information processing network element) jointly analyzing channel reports reported by two channel measurement network elements (e.g., a second network element and a fourth network element) to obtain corresponding perception results. This method primarily includes the following steps.

S1301a. The first network element sends first information to the second network element, where the first information is used to indicate a first condition.

This step can be understood with reference to the description in S801, and will not be described in detail in this embodiment of the present application.

S1301b. The first network element sends third information to the fourth network element, where the third information is used to indicate a third condition.

The definition of the third condition can be understood by referring to the definition of the first condition in S801, and will not be elaborated in this embodiment of the present application.

Optionally, the third condition may be the same as or different from the first condition. The third condition being the same as the first condition may be understood as: the parameter range in the third condition is different from the parameter range in the first condition, or the third threshold in the third condition is the same as the first threshold in the first condition. The third condition being different from the first condition may be understood as: the parameter range in the third condition is different from the parameter range in the first condition, or the third threshold in the third condition is different from the first threshold in the first condition.

The present application does not limit the execution order of the above S1301a and S1301b. For example, S1301a and S1301b can be executed simultaneously, or S1301a can be executed first and then S1301b, or S1301b can be executed first and then S1301a. After executing S1301a and S1301b, S1302a and S1302b are further executed.

S1302a: The second network element sends a first channel report to the first network element according to the first condition.

For example, in the first condition, the first threshold value for SINR is configured as 10 dB. When the SINR measured by the first network element for the first reference signal is greater than or equal to 10 dB, the first network element determines that the first channel report includes one or more of the following: the SINR measured for the first reference signal; other measurement results (such as RSRP, RSSI, or RSRQ) measured for the first reference signal; and channel information obtained based on the first reference signal. When the SINR measured by the first network element for the first reference signal is less than 10 dB, the first network element determines that the first channel report includes one or more of the following: the SINR measured for the first reference signal or other measurement results (such as RSRP, RSSI, or RSRQ) measured for the first reference signal.

In addition, the embodiment of this step can be understood by referring to the description in S802, and the embodiment of this application will not be described in detail.

S1302b: The second network element sends a third channel report to the first network element according to the third condition.

For example, in the third condition, the third threshold value for SINR is configured as 20 dB. When the SINR measured by the first network element for the third reference signal is greater than or equal to 20 dB, the first network element determines that the first channel report includes one or more of the following: the SINR measured for the third reference signal; other measurement results (such as RSRP, RSSI, or RSRQ) measured for the third reference signal; and channel information obtained based on the third reference signal. When the SINR measured by the first network element for the third reference signal is less than 10 dB, the first network element determines that the first channel report includes one or more of the following: the SINR measured for the third reference signal or other measurement results (such as RSRP, RSSI, or RSRQ) measured for the third reference signal.

In addition, the embodiment of this step can be understood by referring to the description in S802, and the embodiment of this application will not be described in detail.

Further, optionally, the first network element may utilize AI technology to determine the wireless sensing result based on the received first channel report and third channel report. For example, in one possible implementation, after executing S1302a and S1302b, step S1303 may also be executed. For example, in another possible implementation, after executing S1302a and S1302b, steps S1304 to S1306 may also be executed. It is understood that S1303 and S1304 to S1306 are indicated as optional steps by dashed lines in Figure 13.

S1303: The first network element determines a sixth perception result based on the first channel report, the third channel report and the fifth model.

The input of the fifth model is determined based on the first channel report and the third channel report, and the output of the fifth model includes the sixth perception result.

In one possible implementation, the information carried in the first channel report and the information carried in the third channel report can be spliced together and used as input to the fifth model. Similar to the design illustrated in FIG10A , if the first channel report from the second network element includes the measurement result of the first reference signal and the first channel information, and the third channel report from the fourth network element includes the measurement result of the third reference signal, the measurement result of the first reference signal, the first channel information, and the measurement result of the third reference signal can be spliced together and input to the fifth model, and the output of the fifth model is the sixth perception result.

Taking the wireless sensing scenario illustrated in (c) of Figure 3 as an example, the second network element and the fourth network element can both be UEs. For ease of distinction, the second network element is denoted as UE1 and the fourth network element is denoted as UE2. The first network element can be a core network device such as an SMF or AMF. Assuming that the SINR corresponding to the PRS sent by the base station to UE1 is greater than or equal to 10dB, and the SINR corresponding to the PRS sent by the base station to UE2 is less than 20dB, the first channel report received by the core network device includes the SINR corresponding to the PRS (denoted as UE1-SINR) and the first channel information (denoted as UE1-H), and the third channel report received by the core network device includes the SINR corresponding to the PRS, denoted as UE2-SINR. Based on this, the input of the fifth model includes the concatenated information of UE1-SINR, UE1-H and UE2-SINR. Assuming that the target object of wireless sensing includes a pedestrian, the sixth sensing result output by the fifth model indicates that the target object is a pedestrian, or the sixth sensing result indicates the state of the pedestrian, such as stationary, walking, running or falling.

In another possible implementation, the information carried in the first channel report and the information carried in the third channel report can be uniformly preprocessed and then input into the fifth model. Similar to the design shown in Figure 10B, the measurement result of the first reference signal included in the first channel report from the second network element is preprocessed to obtain first preprocessed information, and the measurement result of the third reference signal and the third channel information included in the third channel report from the fourth network element are preprocessed to obtain third preprocessed information, and then the first preprocessed information and the third preprocessed information are input into the fifth model, and the output of the fifth model is the sixth perception result. Optionally, the aforementioned preprocessing of the information can be an AI-based preprocessing method or a non-AI information preprocessing method, which is not limited in this embodiment of the present application.

Taking the wireless sensing scenario illustrated in (d) of Figure 3 as an example, the second network element and the fourth network element can both be base stations. For ease of distinction, the second network element is denoted as base station 1, the fourth network element is denoted as base station 2, and the first network element can be a core network device such as SMF or AMF. Assuming that the SINR corresponding to the SRS sent by the UE to base station 1 is greater than or equal to 10dB, and the SINR corresponding to the SRS sent by the UE to base station 2 is less than 20dB, the core network device receives the first channel report from base station 1 including the SINR corresponding to the SRS (denoted as TRP1-SINR), and the core network device receives the third channel report from base station 2 including the SINR corresponding to the SRS (denoted as TRP3-SINR) and the third channel information (denoted as TRP3-H). Based on this, the core network device can preprocess TRP1-SINR to obtain first preprocessing information, and preprocess TRP3-SINR and TRP3-H to obtain third preprocessing information; and then input the first preprocessing information and the third preprocessing information into the fifth model. Assuming that the target object of wireless perception includes a drone, the sixth perception result output by the fifth model indicates that the target object is a drone, or the fifth perception result indicates the state of the drone, such as stationary or flying, or the sixth perception result indicates the flight parameters of the drone between the base station and the UE, such as flight speed or flight altitude.

S1304: The first network element determines a second perception result according to the first channel report and the second model.

This step can be implemented with reference to the description in S803, and will not be described in detail in this embodiment of the present application.

S1305: The first network element determines a seventh perception result based on the third channel report and the sixth model.

The input of the sixth model is determined based on the third channel report, and the output of the sixth model includes the seventh perception result.

Similar to the first channel report of the second network element in the wireless sensing scenario illustrated in (c) of Figure 3, the fourth network element may be a UE, and the first network element may be a core network device such as an SMF or an AMF. When the SINR corresponding to the PRS sent by the base station to the UE is greater than or equal to 20 dB, the third channel report received by the core network device includes the SINR corresponding to the PRS and the third channel information, and the core network device inputs the SINR corresponding to the PRS and the third channel information into the sixth model; when the SINR corresponding to the PRS sent by the base station to the UE is less than 20 dB, the third channel report received by the core network device includes the SINR corresponding to the PRS, and the core network device inputs the SINR corresponding to the PRS into the sixth model; in these two cases, assuming that the target object of the wireless sensing includes a pedestrian, the seventh sensing result output by the sixth model indicates that the target object is a pedestrian, or the seventh sensing result indicates the state of the pedestrian, such as stationary, walking, running, or falling.

Similar to the first channel report of the second network element in the wireless sensing scenario illustrated in (d) of Figure 3, the fourth network element may be a base station, and the first network element may be a core network device such as an SMF or an AMF. When the SINR corresponding to the SRS sent by the UE to the base station is greater than or equal to 20 dB, the third channel report received by the core network device includes the SINR corresponding to the SRS and the third channel information, and the core network device inputs the SINR corresponding to the SRS and the third channel information into the sixth model; when the SINR corresponding to the SRS sent by the UE to the base station is less than 20 dB, the third channel report received by the core network device includes the SINR corresponding to the SRS, and the core network device inputs the SINR corresponding to the SRS into the sixth model; in these two cases, assuming that the target object of the wireless sensing includes a drone, the seventh sensing result output by the sixth model indicates that the target object is a drone, or the seventh sensing result indicates the state of the drone, such as stationary or flying, or the seventh sensing result indicates the flight parameters of the drone between the base station and the UE, such as flight speed or flight altitude.

Similar to the first channel report of the second network element in the wireless sensing scenario illustrated in (f) of Figure 3, the fourth network element may be the second UE, and the first network element may be the base station. When the SINR corresponding to the SL-PRS sent by the second UE to the second UE is greater than or equal to 20dB, the third channel report received by the base station includes the SINR corresponding to the SL-PRS and the third channel information, and the base station inputs the SINR corresponding to the SL-PRS and the third channel information into the sixth model; when the SINR corresponding to the SL-PRS sent by the second UE to the second UE is less than 20dB, the third channel report received by the base station includes the SINR corresponding to the SL-PRS, and the base station inputs the SINR corresponding to the SL-PRS into the sixth model; in these two cases, assuming that the target object of the wireless sensing includes a vehicle, the seventh sensing result output by the sixth model indicates that the target object is a vehicle, or the seventh sensing result indicates the state of the vehicle, such as stationary or moving, or the seventh sensing result indicates the driving parameters of the vehicle, such as driving speed or driving route, etc.

S1306: The first network element fuses the second perception result and the seventh perception result to obtain an eighth perception result.

For example, in the wireless sensing scenario illustrated in (c) of Figure 3 described in S1305, the second perception result indicates that the target object is a pedestrian, and the seventh perception result indicates that the pedestrian is in a fallen state. Then the seventh perception result obtained by fusing the second perception result and the seventh perception result indicates that a pedestrian is detected to have fallen.

For example, in the wireless sensing scenario illustrated in (d) of Figure 3 described in S1305, the second perception result indicates that the target object is a drone, and the seventh perception result also indicates a drone. The seventh perception result obtained by fusing the second perception result and the seventh perception result indicates that a drone is detected.

For example, in the wireless sensing scenario illustrated in (f) in Figure 3 described in S1305, the second perception result indicates that the target object is a vehicle, and the seventh perception result also indicates the vehicle's driving speed. The seventh perception result obtained by fusing the second perception result and the seventh perception result indicates that the vehicle is detected traveling at a certain speed.

Similar to the design illustrated in FIG10C , the measurement result of the first reference signal included in the first channel report from the second network element is input into the second model to obtain a second perception result, and the measurement result of the third reference signal and the third channel information included in the third channel report from the fourth network element are input into the fifth model to obtain a sixth perception result. The second perception result and the sixth perception result are then fused to obtain a seventh perception result. Optionally, the aforementioned fusion of the perception results may be performed using either an AI-based preprocessing method or a non-AI information preprocessing method, which is not limited in this embodiment of the present application.

The solution illustrated in FIG13 takes two channel measurement network elements as an example, and describes a solution in which the information processing network element uses AI technology to jointly process the channel reports of the two channel measurement network elements. In other communication scenarios, AI technology can also be used to jointly process the channel reports of more than two channel measurement network elements. For example, in the uplink positioning scenario illustrated in FIG6A, the channel measurement network element is base station 1, base station 2, or base station 3, and the information processing network element is LMF. Similar to the solution illustrated in FIG13, LMF can configure channel measurement conditions for base stations 1 to 3 respectively, and the conditions corresponding to different base stations can be the same or different; furthermore, base station 1, base station 2, and base station 3 respectively measure the reference signal (such as SRS) sent by the terminal device and determine the channel report according to their respective corresponding conditions. For example, when the signal measurement result of base station 1 does not meet the corresponding condition, only the signal measurement result is included in the channel report; when the signal measurement result of base station 2 meets the corresponding condition, the signal measurement result and the channel information estimated based on the reference signal are included in the channel report; when the signal measurement result of base station 3 meets the corresponding condition, the signal measurement result and the channel information estimated based on the reference signal are included in the channel report. Furthermore, LMF can use AI technology to jointly process the channel reports reported by base stations 1 to 3. For example, according to the method described in S1303, LMF can determine the corresponding perception results, that is, the location information of the terminal device, based on the channel report reported by base station 1, the channel report reported by base station 2, the channel report reported by base station 3 and the AI model.

In the above embodiment, the conditions corresponding to the channel measurements are respectively indicated to the multiple channel measurement network elements, and the amount of data of the channel reports sent by the multiple channel measurement network elements is controlled to be reduced, thereby reducing the waste of transmission resources.

Furthermore, in one possible implementation, the solutions described in FIG. 12 and FIG. 13 may be implemented in combination. For example, the first network element may jointly analyze the channel report from the second network element, the image information from the third network element, and the channel report from the fourth network element to obtain a corresponding perception result. This embodiment of the present application is not limited in this regard.

Based on the same concept, referring to FIG14 , an embodiment of the present application provides a communication device 1400, which includes a processing module 1401 and a communication module 1402. The communication device 1400 may be a first network element, or a communication device applied to or used in conjunction with the first network element, capable of implementing a communication method executed on the first network element side; or the communication device 1400 may be a second network element, or a communication device applied to or used in conjunction with the second network element, capable of implementing a communication method executed on the second network element side; or the communication device 1400 may be a third network element, or a communication device applied to or used in conjunction with the third network element, capable of implementing a communication method executed on the third network element side; or the communication device 1400 may be a fourth network element, or a communication device applied to or used in conjunction with the fourth network element, capable of implementing a communication method executed on the fourth network element side.

The communication module may also be referred to as a transceiver module, transceiver, transceiver, or transceiver device. The processing module may also be referred to as a processor, processing board, processing unit, or processing device. Optionally, the communication module is used to perform the sending and receiving operations on the LMF side or the first device side in the above method. The device in the communication module that implements the receiving function may be considered a receiving unit, and the device in the communication module that implements the sending function may be considered a sending unit. That is, the communication module includes a receiving unit and a sending unit.

When the communication device 1400 is applied to a first network element, the processing module 1401 may be used to implement the processing functions of the first network element in any of the embodiments shown in Figures 7 to 9 and 11 to 13, and the communication module 1402 may be used to implement the transceiver functions of the first network element in any of the embodiments shown in Figures 7 to 9 and 11 to 13. When the communication device 1400 is applied to a second network element, the processing module 1401 may be used to implement the processing functions of the second network element in the embodiments shown in Figures 7 to 9, 12, or 13, and the communication module 1402 may be used to implement the transceiver functions of the second network element in the embodiments shown in Figures 7 to 9, 12, or 13. When the communication device 1400 is applied to a third network element, the processing module 1401 may be used to implement the processing functions of the third network element in the embodiments shown in Figures 11 or 12, and the communication module 1402 may be used to implement the transceiver functions of the third network element in the embodiments shown in Figures 11 or 12.

In addition, it should be noted that the aforementioned communication module and/or processing module can be implemented through virtual modules, for example, the processing module can be implemented through a software functional unit or a virtual device, and the communication module can be implemented through a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented through a physical device, for example, if the device is implemented using a chip/chip circuit, the communication module can be an input/output circuit and/or a communication interface, performing input operations (corresponding to the aforementioned receiving operations) and output operations (corresponding to the aforementioned sending operations); the processing module is an integrated processor or microprocessor or integrated circuit.

The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

Based on the same technical concept, the embodiment of the present application further provides a communication device 1500. For example, the communication device 1500 can be a chip or a chip system. Optionally, in the embodiment of the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices.

The communication device 1500 can be used to implement the functions of any network element in the communication system described in the aforementioned embodiments. The communication device 1500 may include at least one processor 1510, which is coupled to a memory. Optionally, the memory may be located within the device, the memory may be integrated with the processor, or the memory may be located outside the device. For example, the communication device 1500 may also include at least one memory 1520. The memory 1520 stores the necessary computer programs, computer programs or instructions and/or data for implementing any of the aforementioned embodiments; the processor 1510 may execute the computer program stored in the memory 1520 to complete the method in any of the aforementioned embodiments.

The communication device 1500 may also include a communication interface 1530, through which the communication device 1500 can exchange information with other devices. Exemplarily, the communication interface 1530 may be a transceiver, circuit, bus, module, pin, or other type of communication interface. When the communication device 1500 is a chip-type device or circuit, the communication interface 1530 in the device 1500 may also be an input-output circuit that can input information (or receive information) and output information (or send information). The processor is an integrated processor or microprocessor or integrated circuit or logic circuit, and the processor can determine output information based on input information.

The coupling in the embodiments of the present application is an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information exchange between devices, units, or modules. The processor 1510 may operate in conjunction with the memory 1520 and the communication interface 1530. The specific connection medium between the processor 1510, memory 1520, and communication interface 1530 is not limited in the embodiments of the present application.

Optionally, referring to FIG. 15 , the processor 1510, the memory 1520, and the communication interface 1530 are interconnected via a bus 1540. The bus 1540 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, FIG. 15 shows only one thick line, but this does not imply that there is only one bus or only one type of bus.

In the embodiments of the present application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

In the embodiments of the present application, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory (volatile memory), such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store 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 in the embodiments of the present application may also be a circuit or any other device that can implement a storage function, for storing program instructions and/or data.

In one possible implementation, the communication device 1500 can be applied to a first network element. Specifically, the communication device 1500 can be the first network element, or it can be a device that can support the first network element and implement the functions of the first network element in any of the above-mentioned embodiments. The memory 1520 stores computer programs (or instructions) and/or data that implement the functions of the first network element in any of the above-mentioned embodiments. The processor 1510 can execute the computer program stored in the memory 1520 to complete the method executed by the first network element in any of the above-mentioned embodiments. Applied to the first network element, the communication interface in the communication device 1500 can be used to interact with the second network element, the third network element or the fourth network element, for example, to send information to the second network element or the third network element, or to receive information from the second network element, the third network element or the fourth network element.

In one possible implementation, the communication device 1500 can be applied to a second network element. Specifically, the communication device 1500 can be a second network element, or a device that can support the second network element and implement the functions of the second network element in any of the above-mentioned embodiments. The memory 1520 stores computer programs (or instructions) and/or data that implement the functions of the second network element in any of the above-mentioned embodiments. The processor 1510 can execute the computer program stored in the memory 1520 to complete the method executed by the second network element in any of the above-mentioned embodiments. Applied to the second network element, the communication interface in the communication device 1500 can be used to interact with the first network element, such as sending information to the first network element or receiving information from the first network element.

In one possible implementation, the communication device 1500 can be applied to a third network element. Specifically, the communication device 1500 can be a third network element, or a device that can support the third network element and implement the functions of the third network element in any of the above-mentioned embodiments. The memory 1520 stores computer programs (or instructions) and/or data that implement the functions of the third network element in any of the above-mentioned embodiments. The processor 1510 can execute the computer program stored in the memory 1520 to complete the method executed by the third network element in any of the above-mentioned embodiments. Applied to the third network element, the communication interface in the communication device 1500 can be used to interact with the first network element, such as sending information to the first network element or receiving information from the first network element.

In one possible implementation, the communication device 1500 can be applied to a fourth network element. Specifically, the communication device 1500 can be a fourth network element, or a device that can support the fourth network element and implement the functions of the fourth network element in any of the above-mentioned embodiments. The memory 1520 stores computer programs (or instructions) and/or data that implement the functions of the fourth network element in any of the above-mentioned embodiments. The processor 1510 can execute the computer program stored in the memory 1520 to complete the method executed by the fourth network element in any of the above-mentioned embodiments. Applied to the fourth network element, the communication interface in the communication device 1500 can be used to interact with the first network element, such as sending information to the first network element or receiving information from the first network element.

Since the communication device 1500 provided in this embodiment can be applied to the first network element, the second network element, the third network element, or the fourth network element to perform the method performed by the first network element, the second network element, the third network element, or the fourth network element, the technical effects that can be achieved can be referred to the above method examples and will not be repeated here.

Based on the above embodiments, an embodiment of the present application provides a communication system, including a first network element and a second network element. Optionally, a third network element and/or a fourth network element are further included. The first network element and the second network element can implement the communication method provided in the embodiments shown in Figures 7 to 9, the first network element, the second network element, and the third network element can implement the communication method provided in the embodiment shown in Figure 12, and the first network element, the second network element, and the third network element can implement the communication method provided in the embodiment shown in Figure 13.

The technical solutions provided by the embodiments of the present application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a LMF, a terminal device, a cell node, a core network element, 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. 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 or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium, etc.

In the embodiments of the present application, under the premise that there is no logical contradiction, the embodiments may reference each other, for example, the methods and/or terms between method embodiments may reference each other, for example, the functions and/or terms between device embodiments may reference each other, for example, the functions and/or terms between device embodiments and method embodiments may reference each other.

Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present application without departing from the scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the embodiments of the present application and their equivalents, the embodiments of the present application are intended to include these modifications and variations.

Claims (15)

A communication method, characterized by being applied to a second network element, comprising: Sending M channel reports to the first network element; wherein the mth channel report among the M channel reports is determined by the second network element based on a first measurement result obtained by measuring the mth reference signal among the M reference signals, where M is a positive integer and m is a positive integer ranging from 1 to M in sequence; When the first measurement result satisfies a first condition corresponding to the m-th reference signal, the m-th channel report includes one or more of the following: the first measurement result obtained by measuring the m-th reference signal; a second measurement result obtained by measuring the m-th reference signal; Channel information obtained based on the mth reference signal; Alternatively, when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result. The method according to claim 1, further comprising: First information is received from the first network element, where the first information is used to indicate a first condition corresponding to the mth reference signal. A communication method, characterized by being applied to a third network element, comprising: receiving second information from the first network element, where the second information indicates a second condition; When the image information collected by the third network element meets the second condition, the image information is sent to the first network element. A communication method, characterized by being applied to a first network element, comprising: receiving M channel reports from a second network element; wherein an mth channel report among the M channel reports is determined by the second network element based on a first measurement result obtained by measuring an mth reference signal among the M reference signals, where M is a positive integer and m is a positive integer ranging from 1 to M in sequence; When the first measurement result satisfies a first condition corresponding to the m-th reference signal, the m-th channel report includes one or more of the following: the first measurement result obtained by measuring the m-th reference signal; a second measurement result obtained by measuring the m-th reference signal; Channel information obtained based on the mth reference signal; Alternatively, when the first measurement result does not meet the first condition corresponding to the mth reference signal, the mth channel report includes the first measurement result or the second measurement result. The method according to claim 4, further comprising: Sending first information to the second network element, where the first information is used to indicate a first condition corresponding to the mth reference signal. The method according to claim 4 or 5, further comprising: A first perception result is determined based on the M channel reports and a first model; wherein an input of the first model is determined based on the M channel reports, and an output of the first model includes the first perception result. The method according to claim 4 or 5, wherein the M channel reports include a first channel report and a second channel report; the method further comprising: Determining a second perception result based on the first channel report and a second model; wherein an input of the second model is determined based on the first channel report, and an output of the second model includes the second perception result; Determining a third perception result based on the second channel report and a third model; wherein an input of the third model is determined based on the second channel report, and an output of the third model includes the third perception result; The second perception result and the third perception result are fused to obtain a fourth perception result. The method according to claim 4 or 5, further comprising: sending second information to the third network element, where the second information indicates a second condition; Image information is received from the third network element, where the image information meets the second condition. The method according to claim 8, further comprising: Determine a fifth perception result based on the M channel reports, the image information and the fourth model; wherein the input of the fourth model is determined based on the M channel reports and the image information, and the output of the fourth model includes the fifth perception result. A communication device, characterized in that it includes a module for executing the method according to claim 1 or 2, or includes a module for executing the method according to claim 3, or includes a module for executing any one of the methods according to claim 4-9. A communication device, comprising: A processor, the processor being coupled to a memory, the processor being configured to call computer program instructions stored in the memory to execute the method according to claim 1 or 2, or the method according to claim 3, or the method according to any one of claims 4 to 9. A communication system, characterized by comprising a communication device for executing the method according to claim 1 or 2, a communication device for executing the method according to claim 3, and a communication device for executing the method according to any one of claims 4-9. A computer-readable storage medium, characterized in that instructions are stored on the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 9. A computer program product, characterized in that it includes program instructions, and when the program instructions are executed, the method according to claim 1 or 2, or the method according to claim 3, or the method according to any one of claims 4 to 9 is executed. A chip, characterized in that it includes a processor, wherein the processor is used to read a computer program stored in a memory to execute the method according to claim 1 or 2, or the method according to claim 3, or the method according to any one of claims 4 to 9.