Method and communication device for channel measurement

Abstract

The invention provides a method for channel measurement and a communication device, which are characterized in that when at least one measurement expert model for processing a channel measurement result associated with a certain terminal is determined, information capable of representing the channel measurement of the terminal and / or information of the terminal are / is taken as a determination basis. The at least one measurement expert model determined based on the information representing the channel measurement of the terminal and / or the information of the terminal can utilize the flexibility and nonlinear fitting advantage of the neural network, so that the at least one measurement expert model is used for processing the result of the channel measurement associated with the terminal; the generalization ability of the AI model and the precision of channel measurement can be improved.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and more specifically, to a method and communication apparatus for channel measurement. Background Technology

[0002] Machine learning (ML) is an important technological approach to achieving artificial intelligence (AI), and deep neural networks (DNNs) are a specific implementation of machine learning. Through approximation theorems, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while DNN-based deep learning communication systems can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0003] In AI-based channel measurement, most existing models have limited generalization ability and measurement accuracy when channel conditions change. Therefore, how to improve the generalization ability and measurement accuracy of the models is an urgent problem to be solved. Summary of the Invention

[0004] This application provides a method and communication apparatus for channel measurement, which can improve the generalization ability of the model and the accuracy of channel measurement.

[0005] Firstly, a method for channel measurement is provided, which can be executed by a communication device or a module for the communication device (e.g., a processor, chip, circuit, AI entity, etc., or a logic module, hardware, and / or software that can implement all or part of the functions of the communication device), wherein the communication device can correspond to a first network element in the method embodiment. The method may include: acquiring first information, the first information being used to characterize information of channel measurements associated with a first terminal and / or information of the first terminal; based on the first information, determining at least one measurement expert model and / or weights corresponding to each of the at least one measurement expert model from a measurement expert model library, wherein the at least one measurement expert model is used for processing the results of channel measurements associated with the first terminal, the weights being the weights of the first measurement expert model in the processing of the results of channel measurements associated with the first terminal, the first measurement expert model corresponding to the weights, and the at least one measurement expert model including the first measurement expert model.

[0006] In the technical solution of this application, when determining at least one measurement expert model for processing the results of channel measurements associated with a certain terminal (e.g., a first terminal), information characterizing the channel measurements of the terminal and / or information about the terminal are used as the basis for determination. Since the at least one measurement expert model determined based on the information characterizing the channel measurements of the terminal and / or the information about the terminal can utilize the flexibility and nonlinear fitting advantages of neural networks, using this at least one measurement expert model to process the results of channel measurements associated with the terminal can improve the model's generalization ability and the accuracy of the channel measurements.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: acquiring environmental perception information; the step of determining at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model from the measurement expert model library based on the first information includes: determining the weights corresponding to each of the at least one measurement expert model and / or the at least one measurement expert model from the measurement expert model library based on the environmental perception information and the first information.

[0008] In this implementation, in addition to the channel measurement information associated with the terminal and / or the terminal information, environmental awareness information can also be added. These different types of information collectively serve as the basis for selecting or determining the measurement expert model. Because the environmental awareness information further provides information about the environment related to channel measurement, it can further improve the model's generalization ability and the accuracy of channel measurements.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the channel measurement information associated with the first terminal is obtained based on measurements of uplink reference signals and / or downlink reference signals.

[0010] In this implementation, the channel measurement information associated with the terminal can be obtained from the uplink reference signal measured by the network side and / or from the downlink reference signal measured by the terminal side. This can be applied to different channel measurement scenarios, thereby improving the generalization ability of the model and the accuracy of channel measurement in different channel measurement scenarios.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending information of the at least one measurement expert model; and / or information of the weights corresponding to each of the at least one measurement expert model.

[0012] In this implementation, depending on the specific implementation, after determining at least one measurement expert model, the first network element can send information about the at least one measurement expert model and / or the corresponding weight information to reduce air interface overhead and save air interface resources.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: acquiring a first input, the first input including measurement results of an uplink reference signal and / or measurement results of a downlink reference signal; determining a processing result of the channel measurement result based on the first input and the weights corresponding to the at least one measurement expert model and / or the at least one measurement expert model; and sending the processing result.

[0014] In this implementation, the first network element processes the channel measurement results based on at least one measurement expert model and its corresponding weights, and sends the processing results to the access network equipment or terminal equipment. The access network equipment or terminal equipment then performs subsequent signal transmission or reception processing based on the processing results. This implementation helps the access network equipment to share the computational tasks and reduce its computational burden. In addition, it can save power consumption for terminals with weaker computing capabilities that are insufficient to process the channel measurement results themselves.

[0015] Secondly, a method for channel measurement is provided, which can be executed by a communication device or a module for the communication device (e.g., a processor, chip, circuit, AI entity, etc., or a logic module, hardware, and / or software that can implement all or part of the functions of the communication device). The communication device can correspond to the access network device or terminal device in the method embodiment. The method may include: sending first information, the first information being used to characterize information of channel measurement associated with a first terminal and / or information of the first terminal; receiving information of at least one measurement expert model and / or information of weights corresponding to each of the at least one measurement expert model, the at least one measurement expert model being used for processing the results of the channel measurement associated with the first terminal, the weights being the weights of the first measurement expert model in the process of processing the results of the channel measurement associated with the first terminal, the first measurement expert model corresponding to the weights, and the at least one measurement expert model including the first measurement expert model.

[0016] The technical effects of the second aspect can be referred to the corresponding explanation in the first aspect, and will not be repeated here.

[0017] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: obtaining a first input, the first input including measurement results of uplink reference signals and / or measurement results of downlink reference signals; and determining a processing result of the channel measurement results associated with the first terminal based on the first input and the weights corresponding to the at least one measurement expert model and / or the at least one measurement expert model.

[0018] In this implementation, after obtaining at least one measurement expert model, the access network device or terminal device can process the channel measurement results itself to facilitate subsequent signal transmission or reception processing based on the processing results. This approach is suitable for access network devices or some terminal devices with strong computing capabilities.

[0019] Thirdly, a method for channel measurement is provided, which can be executed by a communication device or a module for the communication device (e.g., a processor, chip, circuit, AI entity, etc., or a logic module, hardware, and / or software that can implement all or part of the functions of the communication device). The communication device can correspond to the access network device or terminal device in the method embodiment. The method may include: sending first information, the first information being used to characterize information of channel measurement associated with a first terminal and / or information of the first terminal; sending the result of channel measurement associated with the first terminal; receiving a processing result of the result of channel measurement associated with the first terminal, the processing result being obtained based on a first input and weights corresponding to at least one measurement expert model and / or at least one measurement expert model, the weights corresponding to the at least one measurement expert model and / or at least one measurement expert model being determined from a measurement expert model library based on the first information, the weights being the weights of the first measurement expert model in the processing of the result of channel measurement associated with the first terminal, the first measurement expert model corresponding to the weights, and the at least one measurement expert model including the first measurement expert model.

[0020] In this implementation, the access network device or terminal device (i.e., the first terminal) provides the first network element with first information (e.g., information about channel measurements associated with the first terminal and / or information about the first terminal), as well as the results of the channel measurements associated with the first terminal. This allows the first network element to process the results of the channel measurements associated with the first terminal based on at least one measurement expert model after determining the first information. Finally, the first network element provides the processing results to the access network device or terminal device, enabling the access network device or terminal device to perform subsequent signal transmission or reception processing based on the processing results. This helps reduce the computational burden on the access network device, save power consumption on the terminal device, and reduce the computational requirements of the terminal device.

[0021] In some implementations of the first to third aspects, the first information is used to indicate a first profile of the first terminal, the first profile being used to characterize information of channel measurements associated with the first terminal and / or information of the first terminal.

[0022] In this implementation, information from channel measurements associated with the terminal and / or the terminal's information can be used to construct a profile of the terminal. For different terminals, the terminal information (e.g., movement speed) may differ, and the information from channel measurements associated with the terminal may also differ, resulting in different profiles of the terminal. Alternatively, for the same terminal, the information from channel measurements associated with the terminal may differ in different channel measurements, and the terminal's information may also differ; therefore, the profile of a terminal constructed in different channel measurements may also differ, thus allowing for a more refined description of the channel measurement information associated with the terminal and / or the terminal's information. Furthermore, when the terminal profile constructed based on this information is used to determine the measurement expert model, the determined measurement expert model has higher accuracy in processing the results of channel measurements.

[0023] In some implementations of the first to third aspects, the first information includes one or more of the following: channel characteristic metrics, time-domain quality indicators of the channel, frequency-domain quality indicators of the channel, number of paths in the channel impulse response (CIR) whose energy is greater than k times the energy of the first path, average power of multiple sampling points, line-of-sight (LOS) probability, parameters reflecting the non-line-of-sight (NLOS) degree of the channel, signal-to-interference-plus-noise ratio, reference signal received power (RSRP), or user's moving speed, where k is a number greater than 0.

[0024] In some implementations of the first to third aspects, the channel feature metric is used to characterize channel features.

[0025] In this implementation, the first information can be used to indicate the channel characteristic metric, which is a parameter or index used to characterize the channel characteristics. It can be a parameter used in the existing communication system or a new parameter or index introduced in the future communication system to evaluate the performance or quality of the channel, without limitation.

[0026] In some implementations of the first to third aspects, the information of the channel measurement associated with the first terminal is obtained based on the measurement of the uplink reference signal and / or the downlink reference signal.

[0027] Fourthly, a communication device is provided, the communication device having the function of implementing the method in the first aspect or any possible implementation of the first aspect. The function can be implemented by hardware, or by software, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above-described function.

[0028] Fifthly, a communication device is provided, the communication device having the function of implementing the method in the second aspect or any possible implementation of the second aspect. The function can be implemented by hardware, or by software, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above-described function.

[0029] Sixthly, a communication device is provided, the communication device having the function of implementing the method in the third aspect or any possible implementation of the third aspect. The function can be implemented by hardware, or by software, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above-described function.

[0030] A seventh aspect provides a communication device including at least one processor configured to cause the communication device to perform a method of the first aspect or any possible implementation thereof; or perform a method of the second aspect or any possible implementation thereof; or perform a method of the third aspect or any possible implementation thereof. Optionally, the at least one processor is coupled to at least one memory for storing a computer program or instructions, and the at least one processor is configured to call and run the computer program or instructions from the at least one memory, causing the communication device to perform a method of the first aspect or any possible implementation thereof; or perform a method of the second aspect or any possible implementation thereof; or perform a method of the third aspect or any possible implementation thereof. Optionally, the at least one processor may be included in the communication device or may be configured externally to the communication device. Optionally, the communication device further includes the at least one memory. Optionally, the communication device further includes a communication interface.

[0031] Eighthly, a communication device is provided, comprising a communication interface and a circuit. The communication interface is configured to receive a signal to be processed and transmit the signal to the circuit. The circuit is configured to process the signal to perform a method as described in the first aspect or any possible implementation thereof; or to perform a method as described in the second aspect or any possible implementation thereof; or to perform a method as described in the third aspect or any possible implementation thereof. Optionally, the communication interface is further configured to output the signal processed by the circuit. As an example, the communication interface may be a transceiver, hardware circuit, bus, module, pin, or other type of communication interface. The signal includes information and / or data. Optionally, the communication device may be a chip.

[0032] A ninth aspect provides a computer-readable storage medium storing computer program code or instructions that, when executed on a computer, cause the method of the first aspect or any possible implementation thereof to be implemented; or the method of the second aspect or any possible implementation thereof to be implemented; or the method of the third aspect or any possible implementation thereof to be implemented.

[0033] In a tenth aspect, a computer program product is provided, the computer program product comprising computer program code or instructions, which, when executed on a computer, cause the method in the first aspect or any possible implementation thereof to be implemented; or, as in the second aspect or any possible implementation thereof, the method to be implemented; or, as in the third aspect or any possible implementation thereof, the method to be implemented.

[0034] Eleventh aspect: A wireless communication system is provided, including the communication device as described in the fourth aspect, and the communication device as described in the fifth and / or sixth aspects. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a communication system applicable to embodiments of this application.

[0036] Figure 2 This is another schematic diagram of a communication system applicable to embodiments of this application.

[0037] Figure 3 This is a schematic diagram of a possible application framework in a communication system.

[0038] Figure 4 This is a schematic diagram of another possible application framework in a communication system.

[0039] Figure 5 This is a schematic diagram of the AI-based CSI feedback process.

[0040] Figure 6 A schematic flowchart of the channel measurement method 200 provided in this application.

[0041] Figure 7 An example of a method for channel measurement provided in this application.

[0042] Figure 8 Another example of the method for channel measurement provided in this application.

[0043] Figure 9 This is yet another example of the method for channel measurement provided in this application.

[0044] Figure 10 This is yet another example of the method for channel measurement provided in this application.

[0045] Figure 11 This is yet another example of the method for channel measurement provided in this application.

[0046] Figure 12 A schematic block diagram of the communication device 1000 provided in this application.

[0047] Figure 13 A schematic block diagram of another communication device 1100 provided in this application.

[0048] Figure 14 A schematic diagram of the system architecture of the chip provided in this application. Detailed Implementation

[0049] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0050] The technical solutions provided in this application can be applied to various communication systems, such as 5th 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, and satellite communication systems. Furthermore, they can be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), as well as Internet of Things (IoT) communication systems, future communication systems, or integrated systems of multiple systems.

[0051] In a communication system, a network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. A network element can also be replaced by an entity, network entity, device, communication equipment, communication module, node, communication node, etc. This application describes the concept of a network element as an example. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device.

[0052] Optionally, the communication system may also include at least one AI node.

[0053] Figure 1 This is a schematic diagram of a communication system applicable to embodiments of this application. For example... Figure 1 As shown, the communication system 100 may include at least one network device, such as... Figure 1 The network device 110 shown; the communication system 100 may also include at least one terminal device, such as Figure 1 The terminal devices 120 and 130 are shown. Network device 110 can communicate with the terminal devices (such as terminal devices 120 and 130) via a wireless link. Communication devices in this communication system, for example, network device 110 and terminal device 120, can communicate via multi-antenna technology.

[0054] Figure 2 This is another schematic diagram of a communication system applicable to embodiments of this application. Compared to Figure 1 Regarding the communication system 100 shown, Figure 2 The communication system 200 shown also includes an AI network element 140. The AI ​​network element 140 is used to perform AI-related operations, such as building training datasets, training AI models, or performing inference on AI models.

[0055] In one implementation, network device 110 can send data related to AI model training to AI network element 140, which then constructs a training dataset and trains the AI ​​model. As an example, the data related to AI model training may include data reported by terminal devices. AI network element 140 can send the results of AI model-related operations to network device 110, which then forwards them to the terminal devices. For example, the results of AI model-related operations may include at least one of the following: a trained AI model, model evaluation results, or test results. Exemplarily, a portion of the trained AI model may be deployed on network device 110, and another portion on the terminal devices. Optionally, the trained AI model may be deployed on network device 110, or it may be deployed on the terminal devices.

[0056] It should be understood that Figure 2 This explanation only uses the direct connection between AI network element 140 and network device 110 as an example. In other scenarios, AI network element 140 can also be connected to terminal devices. Alternatively, AI network element 140 can be connected to both network device 110 and terminal devices simultaneously. Alternatively, AI network element 140 can also be connected to both network device 110 and terminal devices through a third-party network element. This application embodiment does not limit the connection relationship between AI network elements and other network elements.

[0057] Alternatively, in another implementation, the AI ​​network element 140 can also be configured as a module in network devices and / or terminal devices, for example, configured in... Figure 1 In the network device 110 or terminal device shown.

[0058] It should be noted that, Figure 1 and Figure 2 This is a diagram for ease of understanding only. The communication system may also include other devices, such as wireless repeaters and / or wireless backhaul devices. Figure 1 and Figure 2 The figures are not shown. Furthermore, in practical applications, a communication system may include multiple network devices or multiple terminal devices. This application does not limit the number of network devices and terminal devices.

[0059] In the embodiments of this application, the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user apparatus. The terminal device can be a device that provides voice / data, such as a handheld device or vehicle-mounted device with wireless connectivity. Currently, examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving vehicles, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, wearable devices, terminal devices in 5G networks, or future public land mobile communication networks. Terminal devices in a network (PLMN), etc., are not limited to this in the embodiments of this application.

[0060] As an example and not a limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0061] In this embodiment, the device used to implement the functions of the terminal device can be the terminal device itself, or it can be any device capable of supporting the terminal device in implementing the corresponding functions, such as a chip system. This device can be configured in the terminal device or used in conjunction with the terminal device. The chip system can consist of chips or include chips and other discrete components. In this embodiment, the terminal device is used as an example to illustrate the device used to implement the functions of the terminal device, and this does not constitute a limitation on the solution of this embodiment.

[0062] The network device in this application embodiment can be a device for communicating with a terminal device, and can refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network, such as a base station. A base station can broadly encompass, or be replaced by, various names including: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, auxiliary station, motor slide retainer (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), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), etc. A base station can be a macro base station, micro base station, relay node, donor node, or similar entities, or combinations thereof. A base station can also refer to a communication module, modem, or chip installed within the aforementioned equipment or apparatus. A base station can also be a mobile switching center, a device that performs base station functions in D2D, V2X, and M2M communications, or a device that performs base station functions in future communication systems. A base station can support networks using the same or different access technologies. Optionally, a RAN node can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

[0063] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0064] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, DU, or CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0065] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio frequency equipment or radio frequency units, such as RRUs, AAUs, or RRHs.

[0066] In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0067] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0068] In this embodiment, the apparatus for implementing the functions of a network device can be a network device itself; it can also be an apparatus capable of supporting the network device in implementing corresponding functions, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This apparatus can be configured in the network device or used in conjunction with the network device. In this embodiment, the example of a network device being used to implement the functions of a network device is used only, and does not constitute a limitation on the solutions of this embodiment.

[0069] Network devices and / or terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application does not limit the scenario in which the network devices and terminal devices are located. Furthermore, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware or general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities that include dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of the terminal devices and network devices.

[0070] Optionally, AI nodes can be deployed in one or more of the following locations within the communication system: access network devices, terminal devices, or core network devices, etc. Alternatively, AI nodes can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. AI nodes can communicate with other devices in the communication system, which can be, for example, one or more of the following: network devices, terminal devices, or core network elements, etc.

[0071] This application does not limit the number of AI nodes. For example, when there are multiple AI nodes, they can be divided based on function, such as different AI nodes being responsible for different functions.

[0072] Optionally, AI nodes can be independent devices, integrated into the same device to implement different functions, or they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the AI ​​nodes described above. AI nodes can also be called AI network elements or AI modules.

[0073] Figure 3 This is a schematic diagram of a possible application framework in a communication system. For example... Figure 3 As shown, network elements in a communication system are connected via interfaces (e.g., NG, Xn) or air interfaces. These network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in the OAM, are equipped with one or more AI modules (for clarity, ...). Figure 3 (Only one is shown in the image). An access network node can be a single RAN node or can include multiple RAN nodes, such as a CU and a DU. The CU and / or DU can also be configured with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are configured in the CU-CP and / or CU-UP.

[0074] The AI ​​module is used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI ​​module can implement different functions. The AI ​​module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or bias in the activation function), input parameters (e.g., type and / or dimension of input parameters), or output parameters (e.g., type and / or dimension of output parameters). The bias in the activation function can also be referred to as the neural network bias.

[0075] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0076] The network device can be a network device equipped with one or more AI modules. For example, the network device can be... Figure 3 The core network equipment, access network node (RAN node), or one or more devices in the OAM are shown. The AI ​​module can be... Figure 4 The RAN intelligent controller (RIC) shown can be a near real-time RIC or a non-real-time RIC. For example, a near real-time RIC is set in a RAN node (e.g., in a CU or DU), while a non-real-time RIC is set in an OAM, a cloud server, a core network device, or other network device.

[0077] Figure 4 This is a schematic diagram of another possible application framework in a communication system. For example... Figure 4 As shown, the communication system includes a RIC. For example, the RIC could be... Figure 1 The AI ​​module in the RAN node shown is used to implement AI-related functions. RICs include near-real-time RICs (near-RT RICs) and non-real-time RICs (Non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0078] Near real-time RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. Near real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or as data for inference.

[0079] Optionally, near real-time RIC can deliver inference results to RAN nodes and / or terminals.

[0080] Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, near real-time RIC submits inference results to DU, and DU sends them to RU.

[0081] Non-real-time RICs are also used for model training and inference. For example, they can be used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or as inference data, and the inference results can be delivered to the RAN nodes and / or terminals.

[0082] Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, a non-real-time RIC can submit inference results to DU, which in turn can send them to RU.

[0083] Near real-time RICs and non-real-time RICs can also be configured as separate devices. Alternatively, near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be configured in RAN nodes (e.g., CU, DU), while non-real-time RICs can be configured in OAM, cloud servers, core network devices, or other devices.

[0084] Optionally, the AI ​​model can be implemented as hardware circuitry, software, or a combination of both, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application program, or software application, etc.

[0085] In LTE and NR communication systems, the base station acquires downlink channel state information (CSI) to determine the resources, modulation and coding scheme (MCS), precoding, and other configurations for scheduling the downlink data channel of the UE. In time division duplex (TDD) systems, due to the reciprocity of uplink and downlink channels, the base station can obtain the uplink CSI by measuring the uplink reference signal and then infer a more accurate downlink CSI, for example, using the uplink CSI as the downlink CSI. In frequency division duplex (FDD) systems, uplink and downlink reciprocity cannot be guaranteed. The downlink CSI is obtained by the UE measuring the downlink reference signal, such as the CSI-RS or the synchronizing signal / physical broadcast channel block (SSB). Therefore, the UE needs to generate a CSI report according to the protocol predefined method or the base station configuration to feed the CSI back to the base station, enabling the base station to acquire the downlink CSI.

[0086] Figure 5 This is a schematic diagram of the AI-based CSI feedback process. In AI-CSI feedback, when the AI ​​model is deployed on the access network equipment side, the access network equipment needs to obtain the CSI-RS estimation results on the UE side as labels (also known as ground truth labels) for training. The AE model consists of two sub-models: an encoder and a decoder. AE can generally refer to a network structure composed of two sub-models. The AE model can also be called a bilateral model, a two-end model, or a collaborative model. The encoder and decoder of the AE are usually trained together and can be used in a matched manner. CSI feedback can be implemented based on the AI ​​model of the AE. For example, the UE side performs CSI compression and quantization through the encoder, and the access network equipment recovers the CSI through the decoder. Figure 5 As shown, for access network equipment, the input of the AI ​​model is the CSI fed back by the UE side, and the output is the recovered CSI. The training of the model requires the CSI measured by the UE side as the ground truth label of the recovered CSI.

[0087] Mixture of experts (MoE) is a widely used architecture in machine learning and deep learning. Its design theory is based on the following fundamental principles:

[0088] 1) Definition of an expert: In the MoE architecture, an "expert" refers to an independent model that learns and optimizes specifically for different subspaces of the dataset. Each expert model may focus on solving a certain class of problems or processing a specific type of data, which allows the model to perform better on local data.

[0089] 2) Gating Mechanism: The MoE architecture includes a gating network, which determines which experts should process a given input, or the degree of contribution of each expert to the final output. The gating network dynamically assigns weights to different experts based on the characteristics of the input data, thus enabling adaptive processing of the input.

[0090] 3) Sparsity Principle: MoE leverages the sparsity of knowledge, meaning that not all experts need to participate in processing every input. In practice, only a few experts are activated, which helps reduce computation and improve efficiency. This conditional computation allows the model to scale to very large sizes without sacrificing performance;

[0091] 4) Parallelization and Distributed Training: The MoE architecture natively supports parallelization and distributed training because expert models can be trained independently and then combined during the inference phase. This is especially useful for large-scale models and large datasets, as it can effectively utilize hardware resources such as multiple GPUs or TPUs;

[0092] 5) Learning and Optimization: The learning process of the MoE model involves joint optimization of expert models and gating networks. Expert models are typically updated using traditional gradient descent methods, while the gating network needs to learn how to correctly allocate inputs to the experts. The loss function of the entire system considers the outputs of all experts and the decisions of the gating network.

[0093] 6) Scalability and Flexibility: Another advantage of the MoE architecture is its scalability and flexibility. The complexity and expressive power of the model can be enhanced by adding more expert models, while keeping the computational cost within a controllable range.

[0094] As mentioned in the background section, in AI-based measurements, most existing models have limited generalization ability and measurement accuracy when channel conditions change.

[0095] Therefore, this application provides a method for channel measurement, which can improve the generalization ability of AI models and the accuracy of channel measurement. In the technical solution provided in this application, a first network element acquires channel measurement information associated with a terminal and / or terminal information, and selects a measurement expert model for processing the channel measurement results based on this information. Optionally, the first network element can also acquire environmental awareness information, and use the channel measurement information associated with the terminal and / or terminal information, along with the environmental awareness information, as the basis for selecting the measurement expert model, which can improve the generalization ability of AI models and the accuracy of channel measurement.

[0096] In the embodiments of this application, "instruction" can include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information for instructing A, it can be understood that the instruction information carries A, which can be a direct instruction to A or an indirect instruction to A. Indirect instruction can refer to directly instructing B through the instruction information, and the correspondence between B and A, to achieve the purpose of instructing A through the instruction information. The correspondence between B and A can be predefined by the protocol, pre-stored, or obtained through configuration between network elements.

[0097] Figure 6 This is a schematic flowchart of the channel measurement method 200 provided in this application. Method 200 involves a first network element and a second network element. The first network element is a network element that can select a measurement expert model from a measurement expert model library. Optionally, the first network element can also be an AI model inference network element, for example, performing inference based on the selected measurement expert model. As an example, the first network element is an intelligent network element. The second network element can be a network element that provides information to the first network element for selecting a measurement expert model, such as an access network device or a terminal device. Optionally, the second network element can also perform model inference. The third network element can also be a network element that provides information to the first network element for selecting a measurement expert model, such as a sensing network element that provides environmental perception information. The first network element, the second network element, and the third network element can be logically deployed separately, or physically deployed in the same network element or different network elements, without limitation.

[0098] Optionally, the network elements involved in method 200 (e.g., the first network element, the second network element, or the third network element, etc.) can also be replaced by devices used for these network elements. For example, the first network element can be replaced by a first device, which can be a chip, circuit, or AI entity applied to the first network element, or an AI entity serving the first network element. The AI ​​entity can be deployed on the first network element or outside the first network element. As an example, the AI ​​entity can be an over-the-top (OTT) server or a cloud server. The second and third network elements can also be replaced by corresponding devices, which will not be described in detail here. In the following embodiments, "network element" is used as an example for description.

[0099] 210. The first network element acquires first information, which is used to characterize the channel measurement information associated with the first terminal and / or the information of the first terminal.

[0100] For example, the first network element receives first information from the second network element. The second network element includes access network equipment and / or terminal equipment.

[0101] In this embodiment of the application, the first information may include one or more of the following:

[0102] Channel characteristic metrics, time-domain quality indicators of the channel, frequency-domain quality indicators of the channel, number of paths in the channel impulse response (CIR) whose energy is greater than k times the energy of the first path (k is a number greater than 0; optionally, k is a number greater than 0 and less than 1), average power of multiple sampling points, LOS probability, parameters reflecting the degree of NLOS of the channel, signal to interference plus noise ratio (SINR), reference signal receiving power (RSRP), or user's moving speed.

[0103] In one implementation, the first information may include information about channel measurements associated with the first terminal and / or information about the first terminal. The information about channel measurements associated with the first terminal may include time-domain and / or frequency-domain quality indicators of the channel. For example, these quality indicators can be obtained by measuring a reference signal or by processing the measurement results of the reference signal accordingly; the specific processing procedure is not limited here. Exemplarily, the parameter reflecting the NLOS degree of the channel may be the Rice factor. The Rice factor represents the ratio of the power of the LOS path to the power of the NLOS path in a multipath. In a LOS scenario, the energy of the LOS path is higher than the sum of the NLOS paths. The Rice factor is typically used to define the ratio of the power of the LOS path to the power of the NLOS path, where the power of the NLOS path represents the sum of the power of all NLOS paths. An NLOS scenario includes NLOS paths but not necessarily LOS paths. Therefore, generally speaking, the higher the Rice factor, the higher the energy of the LOS path relative to the NLOS, and the less severe the NLOS degree. Optionally, CIR can be replaced with any of the following: time-aligned CIR, cross-correlation sequence of multiple CIR sequences, or normalized CIR, without restriction. When determining the time-domain quality index of the channel based on CIR, there is no restriction on the number of sampling points.

[0104] For example, the base station performs channel measurements based on the uplink reference signal to obtain the channel frequency response (CFR), and calculates the channel's frequency domain quality index based on the CFR. In this embodiment, when calculating the channel's frequency domain quality index based on the CFR, there are no restrictions on the bandwidth, subband, or number of ports of the CFR. Furthermore, the CFR can be replaced with a normalized CFR. This will not be elaborated further below.

[0105] As an example, the frequency domain quality metric of a channel can be either the Doppler frequency offset or the Doppler frequency, which reflects the user's movement speed.

[0106] As an example, the information of the first terminal may include, but is not limited to, the moving speed of the first terminal. The moving speed of the first terminal can be obtained by measuring the speed sensor of the first terminal itself.

[0107] In another implementation, the first information includes channel characteristic metrics.

[0108] In the embodiments of this application, the channel characteristic metric can be a parameter or index used to represent channel characteristics, or it can also be called a channel measurement metric; or, the channel characteristic metric is used to evaluate whether a signal can meet one or more performance requirements when transmitted on the channel, such as whether the signal-to-noise ratio reaches a set threshold, the bit error rate reaches a set threshold, etc. As an example, the channel characteristic metric can be represented as an index of measurement quality. Optionally, the channel characteristics indicated by the channel characteristic metric can include one or more of the channel measurement-related information, such as time-domain quality indicators, frequency-domain quality indicators, LOS probability, or parameters reflecting the NLOS degree of the channel in the above embodiments; or other parameters different from the channel measurement-related information, such as signal-to-noise ratio, multipath degree, bit error rate (BER) of the channel, etc.; or an index after combining one or more of the channel measurement-related information with one or more newly introduced parameters, etc. Therefore, the channel characteristic metric can be an index used to measure one or more information related to the channel measurement, an index used to measure one or more newly introduced parameters, or an index used to measure combined parameters. Optionally, the channel characteristic corresponding to the channel characteristic metric can be a parameter or indicator used in existing communication systems to evaluate the performance or quality of the channel, or a new parameter or indicator introduced in future communication systems to evaluate the performance or quality of the channel, without limitation. The second network element performs channel measurement and indicates the channel characteristics determined based on the channel measurement through the channel characteristic metric. As an example, the channel characteristic metric can indicate different channel characteristics through different levels. For example, the channel characteristic metric can include three levels, A, B, and C, which correspond to three intervals of channel characteristics, and these three intervals can be defined by setting corresponding thresholds. When the second network element obtains the measurement result of the channel measurement, it can determine the level corresponding to the current channel characteristic by comparing it with the corresponding threshold, thereby determining the identifier of the corresponding level. By indicating the identifier of the corresponding level, the other end can know the current channel characteristics. As another example, the channel characteristic metric is used to characterize whether the signal meets one or more performance requirements when transmitted on the channel. When channel measurements are performed under different channel characteristics, the channel characteristic metrics obtained will be different, thus reflecting different channel characteristics.

[0109] Optionally, the first information is used to indicate a first profile of the first terminal, the first profile being used to characterize information about channel measurements associated with the first terminal and / or information about the first terminal. As an example, the first profile may include information about channel measurements associated with the first terminal and / or information about the first terminal.

[0110] As an example, the second network element obtains a first profile of the first terminal through channel measurement. In one implementation, the second network element sends first information to the first network element, whereby the first information is the first profile of the first terminal itself. Specifically, sending the first information from the second network element to the first network element could mean sending the first profile of the first terminal to the first network element. In another implementation, the second network element determines a channel feature metric value based on the first profile of the first terminal obtained through channel measurement. In this case, the second network element could send the channel feature metric value corresponding to the first profile of the first terminal to the first network element.

[0111] In one implementation, the second network element can be an access network device. In this implementation, the first information can be obtained by the access network device through measuring the uplink reference signal from the terminal device, or in other words, uplink channel measurement. In another implementation, the second network element can be a terminal device. In this implementation, the first information can be obtained by the terminal device through measuring the downlink reference signal from the access network device, or in other words, downlink channel measurement. Furthermore, optionally, there can be two or more first network elements; for example, the first network element can include an access network device and a terminal device. For example, the first network element receives first information from the access network device and also receives first information from the terminal device. In this case, the first information obtained by the first network element includes first information from the access network device and first information from the terminal device. Optionally, when the first information includes first information from the access network device, there can be one or more access network devices. For example, the first network element receives first information from two access network devices and first information from the terminal device. It should be understood that the terminal device in these embodiments is a first terminal.

[0112] 220. Based on the first information, the first network element determines at least one measurement expert model and / or the weights corresponding to each of the measurement expert models from the measurement expert model library.

[0113] The measurement expert model library includes one or more measurement expert models (hereinafter referred to as models). The first network element selects a measurement expert model based on the first information, and determines at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model from the measurement expert model library (hereinafter referred to as the model library). The at least one measurement expert model is used for processing the results of channel measurements associated with the first terminal.

[0114] In one implementation, the first network element selects at least one measurement expert model from a measurement expert model library based on first information. As an example, the weights corresponding to each of the at least one measurement expert model are preset or determined by other means, and the first network element determines the at least one first measurement expert model based on the first information.

[0115] In another implementation, the first network element determines at least one measurement expert model and the weights corresponding to each of the at least one measurement expert model based on the first information.

[0116] In another implementation, the first network element determines the weights corresponding to at least one measurement expert model based on the first information. As an example, since the at least one measurement expert model has already been determined, the first network element only determines the weights of each of the at least one measurement expert model in the processing of the channel measurement results associated with the first terminal. For instance, in a certain application scenario, when processing the channel measurement results associated with the first terminal, at least one measurement expert model is used. This at least one measurement expert model has already been determined, and the first network element needs to redetermine the weights corresponding to each of the at least one measurement expert model when using it. As another example, all measurement expert models in the measurement expert model library are used in the processing of the channel measurement results associated with the first terminal, and the first network element determines the weights corresponding to each measurement expert model in the measurement expert model library based on the first information.

[0117] When the first network element selects at least two measurement expert models from the measurement expert model library, the operating mode of these at least two measurement expert models in processing the channel measurement results associated with the first terminal is not limited. For example, the at least two measurement expert models can operate in either a serial or parallel mode. Here, a serial operating mode can mean that the output of one of the at least two measurement expert models is the input of the other, and the at least two measurement expert models operate "serially" to process the channel measurement results associated with the first terminal. A parallel operating mode can mean that each of the at least two measurement expert models does not use the output of the other measurement expert model as its input. Alternatively, some of the at least two measurement expert models determined by the first network element may operate serially, while others may operate in parallel. Furthermore, the at least two measurement expert models determined by the first network element can also process the channel measurement results associated with the first terminal in other operating modes.

[0118] As an example, some (one or more) of the at least two measurement expert models are used for preprocessing of channel measurement results, such as interference removal, while other measurement expert models are used to infer the preprocessed results to obtain the inferred channel measurement results. For another example, if the first information acquired by the first network element includes first information from the access network device and first information from the terminal device, then the first network element selects a measurement expert model suitable for processing the fused measurement results of downlink and uplink channel measurements. The measurement expert model selected by the first network element takes the uplink and downlink measurement results as input to obtain the corresponding processing results.

[0119] When the first network element determines a measurement expert model, the weight corresponding to that measurement expert model is less than or equal to 1. When the first network element determines at least two measurement expert models, the sum of the weights of each of the at least two measurement expert models in the processing of channel measurements associated with the first terminal is less than or equal to 1. When the first network element selects only one measurement expert model from the measurement expert model library, the weight corresponding to that measurement expert model is less than 1, indicating that this measurement expert model can be used together with other measurement expert models to process the results of channel measurements associated with the first terminal; when the weight corresponding to that measurement expert model is equal to 1, it indicates that this measurement expert model is used to process the results of channel measurements associated with the first terminal. Similarly, when the sum of the weights corresponding to the at least two measurement expert models determined by the first network element is less than 1, it can indicate that in addition to these at least two measurement expert models, other measurement expert models or other AI models can participate in the processing of channel measurements associated with the first terminal. The other measurement expert models can be models that come from the measurement expert model library and have already been determined, without requiring the first network element to select them based on the first information; or the other AI models may not belong to the measurement expert model library. Alternatively, if the sum of the weights of each of the at least two measurement expert models equals 1, it can be indicated that no other measurement expert model or other AI model participates in the channel measurement process associated with the first terminal, apart from these at least two measurement expert models.

[0120] As an example, one of the ways to implement the second network element determining at least one measurement expert model is through a hybrid expert model mechanism. For details on the hybrid expert model mechanism, please refer to the above description, which will not be repeated here.

[0121] As an example, the measurement expert model library provided in this application may include one or more of the measurement expert models listed in Table 1 below. The indexing format of the measurement expert models in the library may be a numeric index, an alphabetical index, or other indexing format that can distinguish between different models, without limitation.

[0122] Table 1

[0123]

[0124] Optionally, method 200 further includes step 230.

[0125] 230. The first network element acquires environmental perception information.

[0126] As an example, the first network element obtains environmental perception information from the third network element.

[0127] Perception refers to detecting the state of the surrounding environment, the position, direction, height, speed, and distance of objects, as well as judging the shape or material of objects, and even judging human actions and gestures, etc., but it is not limited to these and can also include other perceptible content.

[0128] The measurement of the sensing reference signal by a network device can be: the measurement of the sensing reference signal sent by the network device itself, the measurement of the sensing reference signal sent by the network device to the terminal device, or the measurement of the sensing reference signal sent by the network device to other network devices.

[0129] A sensing task can be a sensing task performed by a sensor, and correspondingly, a sensing result can be the sensing result of the sensor.

[0130] Sensors can be any of the following: image sensors, position sensors, rain sensors, speed sensors, acceleration sensors, gas sensors, optical sensors, etc. When the sensing device corresponding to the sensing task of the sensing result can be an image sensor, position sensor, rain sensor, speed sensor, acceleration sensor, gas sensor, or optical sensor, the sensing type of the sensing result can be determined based on the type of sensing device. In this case, the sensing types are respectively image, position, rainfall, speed, acceleration, gas concentration and composition, light intensity, etc. The sensing function of network devices can be understood as: while transmitting information through a wireless channel, actively recognizing and analyzing the characteristics of the channel to perceive the physical characteristics of the surrounding environment, thereby enhancing communication and sensing functions. For example, using base station signals to perceive surrounding environmental information and designing communication links can avoid obstacles and improve communication performance.

[0131] In this embodiment, the environmental perception information provided by the third network element can be one or more of the aforementioned perception results. The third network element can be a network-side device or a terminal-side device; for example, the third network element can be another access network device, without limitation.

[0132] It should be understood that if method 200 includes step 230, in step 220, the at least one measurement expert model is determined by the first network element based on the first information and environmental perception information. That is, the first network element selects a measurement expert model from the model library based on the first information and environmental perception information, thereby determining the at least one measurement expert model.

[0133] Optionally, method 200 may also include one or more of steps 240 to 250.

[0134] 240. The second network element obtains the first information.

[0135] As an example, the second network element performs reference signal measurement and obtains first information based on the reference signal measurement. For example, the second network element is an access network device, which performs uplink reference signal measurement to obtain the first information, wherein the uplink reference signal originates from the first terminal. As another example, the second network element is a terminal device (specifically, the first terminal), which performs downlink reference signal measurement to obtain the first information. As yet another example, the second network element is an access network device, which measures the uplink reference signal and obtains a first profile of the first terminal based on the measurement result. As yet another example, the second network element is a terminal device, which measures the downlink reference signal and obtains a first profile of the first terminal based on the measurement result. The first information includes the first profile of the first terminal.

[0136] 250. The first network element sends information about the at least one measurement expert model and / or information about the weights corresponding to each of the at least one measurement expert model to the second network element; or, the first network element sends the processing result of the channel measurement associated with the first terminal to the second network element.

[0137] In one implementation, after obtaining at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model, the first network element provides the second network element with information about the at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model. In another implementation, after obtaining at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model, the first network element can use the at least one measurement expert model and its corresponding weights to process the results of channel measurements associated with the first terminal to obtain a processing result. Further, the first network element sends the processing result to the second network element.

[0138] As an example, when the first network element sends the at least one measurement expert model to the second network element, it can directly send the at least one measurement expert model itself; or, send the index of each of the at least one measurement expert model; or, send other information that can determine the at least one measurement expert model. When the first network element sends the weights corresponding to each of the at least one measurement expert model to the second network element, one way is to directly send at least one weight corresponding to each of the at least one measurement expert model; or, send information about at least one weight, where the number of at least one weight is not equal to the number of at least one measurement expert models, for example, less than the number of measurement expert models. As an example, some of the at least one measurement expert models have the same weight. In this case, to save the overhead of indicating weights, at least one weight can be indicated, where each of the at least one weight is associated with the index of one or more measurement expert models, and the first weight in the at least one weight corresponds to one or more measurement expert models associated with the first weight. For example, weight 1 is associated with measurement expert model 1, measurement expert model 2, and weight 2 is associated with measurement expert model 3, indicating that the weights corresponding to measurement expert model 1 and measurement expert model 2 are weight 1, and the weight corresponding to measurement expert model 3 is weight 2. As another example, the first network element sends the quantized information of the weights corresponding to each of the at least one measurement expert model to the second network element; or, the first network element indicates the differential information of the weights corresponding to the at least one measurement expert model to the second network element. For example, in order to reduce the indication overhead, a reference weight is indicated, and the differential information of other weights relative to the reference weight is indicated. The reference weight can be one of the at least one weights corresponding to the at least one measurement expert model, or it can be set according to the specific value of the at least one weight, so as to reduce the indication overhead, without limitation.

[0139] In summary, the technical solution of this application obtains channel measurement information associated with the terminal and / or terminal information, or may further obtain environmental perception information, and then determines the AI ​​model based on the channel measurement information associated with the terminal and / or terminal information, or may further obtain environmental perception information. This utilizes the flexibility and nonlinear fitting advantages of neural networks, thereby improving the generalization ability of the AI ​​model and the measurement accuracy of the channel.

[0140] The channel measurement method provided in this application is illustrated below with some examples. In the following examples, the first network element is an intelligent network element; in some examples, the second network element is an access network device; and in other examples, the second network element is a terminal device.

[0141] Example 1

[0142] Figure 7An example of a method for channel measurement provided in this application.

[0143] 301. The base station measures the SRS from the UE and obtains the SRS measurement results.

[0144] 302. The base station obtains the first information based on the measurement results of SRS.

[0145] As an example, the base station obtains the first profile of the terminal based on the SRS measurement results.

[0146] 303. The base station sends the first information to the intelligent network element.

[0147] 304. The intelligent network element determines at least one measurement expert model and / or the weights corresponding to each of the measurement expert models from the measurement expert model library based on the first information.

[0148] Step 304 can be referred to the explanation in step 220, and will not be repeated here. As an example, the intelligent network element determines two measurement expert models from the measurement model expert library, namely a denoising model and a high-speed time-domain prediction model. The weights of these two measurement expert models are 0.8 and 0.2, respectively.

[0149] Optionally, method 300 may include steps 305 and / or 306.

[0150] 305. The intelligent network element sends information about the at least one measurement expert model to the base station.

[0151] 306. The intelligent network element sends information about the weights corresponding to each of the at least one measurement expert model to the base station.

[0152] As an example, if the at least one measurement expert model is predetermined, the intelligent network element sends the weight information corresponding to each of the at least one measurement expert model to the base station; or, if the weights of the at least one measurement expert model are predetermined or have been provided to the base station, the intelligent network element may send the information of the at least one measurement expert model; or, the intelligent network element sends the at least one measurement expert model and the weight information corresponding to each of the at least one measurement expert model to the base station.

[0153] Optionally, the first information, the information of at least one measurement expert model, or the information of the weights corresponding to each of the at least one measurement expert model can be indicated or configured by a single message or by multiple messages, without limitation.

[0154] 307. The base station processes the results of channel measurements associated with the terminal based on at least one measurement expert model and the weights corresponding to each of the at least one measurement expert model. As an example, the channel measurement results include SRS measurement results.

[0155] In Example 1, the base station acquires first information based on uplink channel measurements and transmits this first information to an intelligent network element. The intelligent network element selects at least one measurement expert model based on the first information for processing the results of channel measurements associated with the terminal. Because the at least one measurement expert model determined by the intelligent network element utilizes the flexibility and nonlinear fitting advantages of neural networks, the accuracy of channel measurements can be improved.

[0156] Example 2

[0157] Figure 8 Another example of the method for channel measurement provided in this application.

[0158] 401. The base station measures the SRS from the terminal and obtains the SRS measurement results.

[0159] 402. The base station obtains the first information based on the measurement results of SRS.

[0160] 403. The base station sends the first information and the SRS measurement results to the intelligent network element.

[0161] The base station can send the first information and the SRS measurement result separately through two messages, or send the first information and the SRS measurement result through one message, without limitation.

[0162] 404. The intelligent network element determines at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model from the measurement expert model library based on the first information, and processes the measurement results of the SRS based on the selected at least one measurement expert model to obtain the processing result.

[0163] As an example, the intelligent network element infers the measurement results of the SRS based on at least one determined measurement expert model and its corresponding weights to obtain the corresponding inference results.

[0164] 405. Processing results of SRS measurement results sent by intelligent network elements to base stations.

[0165] In Example 2, the base station acquires first information based on uplink channel measurements and transmits this information to an intelligent network element. The intelligent network element determines at least one measurement expert model from a measurement expert model library based on the first information, processes (e.g., infers) the SRS measurement results based on the determined at least one measurement expert model, and finally sends the processing result to the base station so that the base station can process subsequent signal transmission or reception according to the processing result. Because the at least one measurement expert model determined by the intelligent network element utilizes the flexibility and nonlinear fitting advantages of neural networks, the accuracy of channel measurements can be improved.

[0166] Example 3

[0167] Figure 9 This is yet another example of the method for channel measurement provided in this application.

[0168] 501. The UE measures the channel state information-reference signal (CSI-RS) from the base station and obtains the CSI-RS measurement results.

[0169] 502. The UE obtains the first information based on the measurement results of CSI-RS.

[0170] 503. The UE sends the first information to the intelligent network element.

[0171] 504. Based on the first information, the intelligent network element determines at least one measurement expert model and / or the weights corresponding to each of the measurement expert models from the measurement expert model library.

[0172] 505. The intelligent network element sends information about the determined at least one measurement expert model to the UE.

[0173] 506. The intelligent network element sends information about the weights corresponding to each of the at least one measurement expert model to the UE.

[0174] Subsequently, the UE can process the CSI-RS measurement results based on the at least one measurement expert model and its corresponding weights to obtain the processing results, and then process the subsequent signals for transmission or reception based on the processing results.

[0175] In Example 3, the UE obtains first information through downlink channel measurement and transmits this first information to the intelligent network element. Based on the first information, the intelligent network element selects at least one measurement expert model from the measurement expert model library, which can improve the accuracy of channel measurement.

[0176] Example 4

[0177] Figure 10This is yet another example of the method for channel measurement provided in this application.

[0178] 601. The UE measures the channel state information-reference signal (CSI-RS) from the base station and obtains the CSI-RS measurement results.

[0179] 602. The UE obtains the first information based on the measurement results of CSI-RS.

[0180] 603. The UE sends the first information and the CSI-RS measurement results to the intelligent network element.

[0181] 604. The intelligent network element selects at least one measurement expert model and / or the corresponding weights of at least one measurement expert model from the measurement expert model library based on the first information. The intelligent network element processes the CSI-RS measurement results based on the determined measurement expert model to obtain the processing result. For example, the intelligent network element performs inference on the CSI-RS measurement results based on at least one determined measurement expert model to obtain the inference result.

[0182] 605. The intelligent network element sends the CSI-RS measurement results to the UE.

[0183] For example, intelligent network elements send inference results of CSI-RS measurement results to the UE.

[0184] In Example 4, the UE obtains first information through downlink channel measurement and transmits the first information and the measurement results of the downlink channel measurement to the intelligent network element. The intelligent network element selects a measurement expert model based on the first information and processes the measurement results of the downlink channel measurement according to the selected measurement expert model, which can improve the accuracy of channel measurement.

[0185] It should be understood that Examples 1 to 4 above use uplink channel measurement or downlink channel measurement as examples. As shown above, uplink channel measurement and downlink channel measurement can also be combined. For example, the intelligent network element receives first information from the base station and first information from the UE, wherein the first information from the base station is obtained based on the measurement results of SRS, and the first information from the UE is obtained based on the measurement results of CSI-RS. The intelligent network element uses the first information from the base station and the first information from the UE as the basis for determining at least one measurement expert model.

[0186] In another implementation, the base station receives first information from the UE, which is obtained by the UE through CSI-RS measurement. The base station provides the first information obtained based on SRS measurement and the first information from the UE to the intelligent network element for the selection of at least one measurement expert model. Optionally, when the intelligent network element processes the channel measurement results associated with the first terminal based on at least one determined measurement expert model, the intelligent network element receives the SRS measurement results from the base station and the CSI-RS measurement results from the UE. Optionally, the base station receives the CSI-RS measurement results from the UE and sends the CSI-RS measurement results from the UE and the base station's SRS measurement results to the intelligent network element for processing the CSI-RS measurement results and SRS measurement results.

[0187] Example 5

[0188] Figure 11 This is yet another example of the method for channel measurement provided in this application.

[0189] 701. The base station measures the SRS from the terminal and obtains the SRS measurement results.

[0190] 702. The base station obtains the first information based on the measurement results of SRS.

[0191] 703. The base station sends the first information and the SRS measurement results to the intelligent network element.

[0192] 704. Intelligent network elements receive environmental perception information from sensing network elements.

[0193] 705. The intelligent network element, based on the first information and environmental perception information, determines at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model in the measurement expert model library, and processes the results of channel measurements associated with the terminal based on the determined at least one measurement expert model to obtain the processing result.

[0194] 706. The intelligent network element sends the processing result to the base station to facilitate the base station's subsequent signal transmission or reception processing.

[0195] In Example 5, the base station acquires first information through uplink channel measurement and transmits this information to the intelligent network element. Furthermore, the intelligent network element obtains environmental awareness information from the sensing network element. Based on the first information and the environmental awareness information, the intelligent network element determines at least one measurement expert model from a measurement expert model library, which can improve the accuracy of channel measurements.

[0196] Furthermore, other implementation methods can be obtained by combining the above examples 1 to 5.

[0197] For example, an intelligent network element can receive first information from the UE and environmental awareness information from a sensing network element, and use the first information from the UE and the environmental awareness information from the sensing network element as the basis for determining at least one measurement expert model. After determining the at least one measurement expert model, the intelligent network element can use the at least one measurement expert model to process the CSI-RS measurement results from the UE and send the processing results to the UE; or, after determining the at least one measurement expert model, the intelligent network element can send information about the at least one measurement expert model to the UE so that the UE can use the at least one measurement expert model to process the CSI-RS measurement results and obtain the processing results.

[0198] For example, in Example 5, the intelligent network element uses first information from the base station and environmental perception information from the sensing network element as the basis for determining at least one measurement expert model. Example 5 can also be combined with Example 3 or Example 4 above. For example, the intelligent network element can also receive first information from the UE, thereby using the first information from the base station, the environmental perception information from the sensing network element, and the first information from the UE as the basis for determining at least one measurement expert model.

[0199] For example, in Example 5, after the intelligent network element determines at least one measurement expert model, it sends information about the at least one measurement expert model to the base station so that the base station can process the measurement results of the SRS according to the at least one measurement expert model.

[0200] For example, in other implementations obtained by combination, after determining at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model, the intelligent network element sends the information of the at least one measurement expert model and / or the information of the weights corresponding to each of the at least one measurement expert model to the base station or UE to facilitate subsequent processing by the base station or UE. Alternatively, the intelligent network element may also obtain the SRS measurement results from the base station and / or the CSI-RS measurement results from the UE, and after determining the at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model, process the SRS measurement results and / or the CSI-RS measurement results based on the determined weights corresponding to the at least one measurement expert model and / or the at least one measurement expert model.

[0201] The method for channel measurement provided in this application has been described in detail above. The corresponding communication device is described below.

[0202] Figure 12 A schematic block diagram of the communication device 1000 provided in this application. Figure 12The communication device 1000 may include a processing module 1001 and a communication module 1002. The communication device 1000 may be a terminal device, or a communication device applied to or used in conjunction with a terminal device to achieve the corresponding functions of the terminal device, such as a processor, chip, circuit, or AI entity. Alternatively, the communication device 1000 may be a network device, or a communication device applied to or used in conjunction with a network device to achieve the corresponding functions of the network device, such as a processor, chip, circuit, or AI entity. Exemplarily, the network device may be the first network element or access network device in the method embodiments of this application.

[0203] The communication module can also be called a transceiver module, transceiver, transceiver unit, or transceiver device. The processing module can also be called a processor, processing board, processing unit, or processing device. Optionally, the communication module is used to perform the sending and receiving operations on the terminal device side or network device side in the above method. The device in the communication module that implements the receiving function can be regarded as a receiving unit, and the device in the communication module that implements the sending function can be regarded as a sending unit. That is, the communication module includes a receiving unit and a sending unit.

[0204] When the communication device 1000 is applied to a terminal device, the processing module 1001 can be used to implement... Figures 6 to 11 The communication module 1002 can be used to implement the processing functions of the terminal device in each embodiment. Figures 5-10 The transmit and receive functions of the terminal device described in each embodiment.

[0205] When the communication device 1000 is applied to a network device, the processing module 1001 can be used to implement... Figures 6 to 11 In various embodiments, the processing functions of the network device (e.g., the first network element or access network device) are implemented, and the communication module 1002 can be used to implement them. Figures 6 to 11 The transmit and receive functions of the network device in each embodiment.

[0206] It should be noted that, Figure 6 The second network element shown can be different network elements in different method embodiments, such as access network equipment or terminal equipment, which have been described in detail in the foregoing method embodiments and will not be repeated here.

[0207] Furthermore, 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 software functional units or virtual devices, and the communication module can be implemented through software functions or virtual devices. Alternatively, the processing module or communication module can also be implemented through physical devices. 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 operation) and output operations (corresponding to the aforementioned sending operation); the processing module is an integrated processor, microprocessor, or integrated circuit.

[0208] The module division in this application is illustrative and represents only one logical functional division. In actual implementation, other division methods are possible. Furthermore, the functional modules in the various examples of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware, as software functional modules, or a combination of hardware and software.

[0209] Figure 13 A schematic block diagram of another communication device 1100 provided in this application. Optionally, the communication device 1100 may be a chip or a chip system. Optionally, in this application, the chip system may be composed of chips or may include chips and other discrete devices.

[0210] The communication device 1100 can be used to implement the functions of any of the network elements (e.g., a first network element, access network equipment, or terminal equipment) described in the foregoing embodiments. The communication device 1100 may include at least one processor 1110. Optionally, the processor 1110 is coupled to a memory, which may be located within the communication device 1100, integrated with the processor, or located outside the communication device 1100. As an example, the communication device 1100 may also include at least one memory 1120. The memory 1120 stores the necessary computer programs (or computer instructions) and / or data for implementing the corresponding functions of any of the network elements in any of the above method embodiments; the processor 1110 may execute the computer programs stored in the memory 1120 to complete the methods implemented by any of the network elements in any of the above method embodiments.

[0211] The communication device 1100 may also include a communication interface 1130, through which the communication device 1100 can interact with other devices. For example, the communication interface 1130 may be a transceiver, circuit, bus, module, pin, or other type of communication interface. When the communication device 1100 is a chip-based device or circuit, the communication interface 1130 in the device 1100 may also be an input / output circuit, capable of inputting information (or receiving information) and outputting information (or sending information). The processor may be an integrated processor, a microprocessor, an integrated circuit, or a logic circuit, and the processor can determine the output information based on the input information.

[0212] The coupling in this application refers to indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, used for information exchange between devices, units, or modules. The processor 1110 may operate in conjunction with the memory 1120 and the communication interface 1130. This application does not limit the specific connection medium between the processor 1110, the memory 1120, and the communication interface 1130.

[0213] Optionally, such as Figure 13 As shown, the processor 1110, the memory 1120, and the communication interface 1130 are interconnected via a bus 1140. The bus 1140 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 13 The bus 1140 is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0214] This application also provides a chip including a circuit and a communication interface. The circuit can be a logic circuit, an integrated circuit, etc., and the communication interface can also be called an input / output circuit, input / output interface, interface circuit, etc., which can input information (or receive information) or output information (or send information). The chip can execute the methods performed by the terminal device or network device (e.g., a first network element or access network device) in the various embodiments of this application.

[0215] In this application, the processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0216] In this application, the memory can be non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM). Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited to this. The memory in this application can also be a circuit or any other device capable of implementing storage functions for storing computer programs and / or data.

[0217] In one implementation, the communication device 1100 can be applied to the network side, such as the first network element, access network device, or host or cloud device in an OTT system, as described in this application embodiment. Specifically, the communication device 1100 can be a network device or a device capable of supporting the network device to implement the corresponding functions of the network device in any of the above method embodiments. The memory 1120 stores computer programs (or computer instructions) and / or data that implement the corresponding functions of the network device. The processor 1110 can execute the computer programs or instructions stored in the memory 1120 to complete the methods executed by the network device in any of the above method embodiments. As an example, the communication device 1100 corresponds to the first network element, and the communication interface in the communication device 1100 can be used to interact with a second network element (e.g., an access network device or a terminal device), for example, sending information to or receiving information from the second network element; in addition, optionally, the communication interface in the communication device 1000 can also be used to interact with other network elements (e.g., a third network element), for example, receiving environmental awareness information from the third network element.

[0218] In another implementation, the communication device 1100 can be applied to the terminal side. For example, the communication device 1100 can be a terminal device, or an apparatus capable of supporting the terminal device and implementing the corresponding functions of the terminal device in any of the above method embodiments. The memory 1120 stores computer programs (or computer instructions) and / or data that implement the corresponding functions of the terminal device in any of the above method embodiments. The processor 1110 can execute the computer program stored in the memory 1120 to complete the method executed by the terminal device in any of the above method embodiments. The communication interface in the communication device 1100 can be used to interact with network devices (e.g., a first network element or access network device) to send information to or receive information from the network device.

[0219] Figure 14 This is a schematic diagram of the system architecture of the chip provided in this application. This chip system architecture can be used in network devices or terminal devices. Input / output control manages the input and output signals of the device; for example, input / output control can be represented as a modem, keyboard, mouse, touchscreen, etc. Input / output control may also be part of the processor. The receiver / transmitter is used to communicate with other devices; the receiver / transmitter may include a modem for modulating or demodulating information. The antenna is used to transmit or receive signals. Storage may include random access memory (RAM) or read-only memory (ROM), which may be used to store computer code that can be executed by the processor to implement the corresponding functions of the device. The processor may include intelligent hardware devices such as general-purpose processors, digital signal processors (DSPs), central processing units (CPUs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), neural network processors (NNs), etc. Figure 14 The chip provided can be used to implement the corresponding functions of the network device or terminal device in the embodiments of this application. The network device can be a first network element (the intelligent network element in the corresponding embodiment) or an access network device (or base station).

[0220] In addition, this application also provides a computer-readable storage medium storing computer instructions, which, when executed on a computer, cause operations and / or processes performed by a terminal device or network device (e.g., a first network element or access network device) in the various method embodiments of this application to be executed.

[0221] This application also provides a computer program product, which includes computer program code or instructions. When the computer program code or instructions are run on a computer, the operations and / or processes performed by a terminal device or network device (e.g., a first network element or access network device) in the various method embodiments of this application are executed.

[0222] This application also provides a chip including a processor, and a memory for storing a computer program, disposed independently of the chip. The processor executes the computer program stored in the memory, such that operations and / or processes performed by a terminal device or network device (a first network element or access network device) in any method embodiment are executed. Further, the chip may also include a communication interface. The communication interface may be an input / output interface or an interface circuit, etc. Further, the chip may also include a memory.

[0223] This application also provides a chip, which may include circuitry and an input / output interface. The circuitry may be logic circuitry, integrated circuits, etc., and exemplaryly, the circuitry may be one or more processors, or all or part of the circuitry in one or more processors used to implement one or more processing, control, or computing functions. The input / output interface may also be an input / output circuit, or an interface circuit, capable of inputting information (or receiving information) and / or outputting information (or sending information). The chip may include a chip system. Optionally, the chip system may be composed of chips or may include chips and other discrete devices. The chip may be used to execute the methods implemented by network devices (e.g., a first network element or access network device) or terminal devices in the various embodiments of this application. Optionally, the chip may be a baseband chip, also known as a modem.

[0224] Furthermore, this application provides a communication system. In one example, the communication system includes a first network element and a second network element. In another example, the communication system includes a first network element, a second network element, and a third network element. This communication system can achieve... Figures 6 to 11 The method for channel measurement provided in any of the embodiments.

[0225] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0226] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the various embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0227] In this application, examples may reference each other without logical contradiction. For example, methods and / or terms between method embodiments may reference each other, functions and / or terms between device embodiments may reference each other, and functions and / or terms between device examples and method examples may reference each other.

[0228] The technical solutions provided in this 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 as 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, all or part of the processes or functions described in this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal device, an access network device, 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 a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media, etc.

[0229] In the embodiments of this application, "at least one" refers to one or more items. "More than one" means two or more items. "And / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0230] The term "comprising" and any variations thereof used in the embodiments of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0231] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0232] In the several embodiments provided in this application, the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0233] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0234] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for channel measurement, characterized in that, include: Obtain first information, which is used to characterize information about channel measurements associated with the first terminal and / or information about the first terminal; Based on the first information, at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model are determined from the measurement expert model library. The at least one measurement expert model is used for processing the results of channel measurements associated with the first terminal. The weights are the weights of the first measurement expert model in the process of processing the results of channel measurements associated with the first terminal. The first measurement expert model corresponds to the weights. The at least one measurement expert model includes the first measurement expert model.

2. The method according to claim 1, characterized in that, The first information is used to indicate a first profile of the first terminal, and the first profile is used to characterize information of channel measurements associated with the first terminal and / or information of the first terminal.

3. The method according to claim 1 or 2, characterized in that, The first information includes one or more of the following: Channel characteristic metrics, channel time-domain quality indicators, channel frequency-domain quality indicators, number of paths in the channel impulse response (CIR) whose energy is greater than k times the energy of the first path, average power of multiple sampling points, line-of-sight (LOS) probability, parameters reflecting the non-line-of-sight (NLOS) degree of the channel, signal-to-interference-plus-noise ratio, reference signal received power (RSRP), or user's moving speed, where k is a number greater than 0.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Acquire environmental perception information; The step of determining at least one measurement expert model and / or the weights corresponding to each of the at least one measurement expert model from the measurement expert model library based on the first information includes: Based on the environmental perception information and the first information, the weights corresponding to the at least one measurement expert model and / or the at least one measurement expert model are determined from the measurement expert model library.

5. The method according to any one of claims 1 to 4, characterized in that, The channel measurement information associated with the first terminal is obtained based on measurements of uplink reference signals and / or downlink reference signals.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Send information from the at least one measurement expert model; and / or, The information of the weights corresponding to each of the at least one measurement expert model.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain a first input, which includes the measurement results of the uplink reference signal and / or the measurement results of the downlink reference signal; Based on the first input and the weights corresponding to the at least one measurement expert model and / or the at least one measurement expert model, the processing result of the channel measurement is determined; Send the processing result.

8. A method for channel measurement, characterized in that, include: Send first information, which is used to characterize information of channel measurements associated with the first terminal and / or information of the first terminal; The system receives information about at least one measurement expert model and / or information about the weights corresponding to each of the at least one measurement expert model. The at least one measurement expert model is used to process the results of channel measurements associated with the first terminal. The weights are the weights of the first measurement expert model in the process of processing the results of channel measurements associated with the first terminal. The first measurement expert model corresponds to the weights. The at least one measurement expert model includes the first measurement expert model.

9. The method according to claim 8, characterized in that, The method further includes: Obtain a first input, which includes the measurement results of the uplink reference signal and / or the measurement results of the downlink reference signal; Based on the first input and the weights corresponding to the at least one measurement expert model and / or the at least one measurement expert model, the processing result of the channel measurement associated with the first terminal is determined.

10. The method according to claim 8 or 9, characterized in that, The first information is used to indicate a first profile of the first terminal, and the first profile is used to characterize information of channel measurements associated with the first terminal and / or information of the first terminal.

11. The method according to any one of claims 8 to 10, characterized in that, The first information includes one or more of the following: Channel characteristic metrics, channel time-domain quality indicators, channel frequency-domain quality indicators, number of paths in the channel impulse response (CIR) whose energy is greater than k times the energy of the first path, average power of multiple sampling points, line-of-sight (LOS) probability, parameters reflecting the non-line-of-sight (NLOS) degree of the channel, signal-to-interference-plus-noise ratio, reference signal received power (RSRP), or user's moving speed, where k is a number greater than 0.

12. The method according to any one of claims 8 to 11, characterized in that, The channel measurement information associated with the first terminal is obtained based on measurements of uplink reference signals and / or downlink reference signals.

13. A method for channel measurement, characterized in that, include: Send first information, which is used to characterize information of channel measurements associated with the first terminal and / or information of the first terminal; Send the results of the channel measurement associated with the first terminal; The processing result of receiving the channel measurement result associated with the first terminal is obtained based on the first input and the weights corresponding to at least one measurement expert model and / or at least one measurement expert model. The weights corresponding to the at least one measurement expert model and / or at least one measurement expert model are determined from the measurement expert model library based on the first information. The weights are the weights of the first measurement expert model in the processing of the channel measurement result associated with the first terminal. The first measurement expert model corresponds to the weights, and the at least one measurement expert model includes the first measurement expert model.

14. The method according to claim 13, characterized in that, The first information is used to indicate a first profile of the first terminal, and the first profile is used to characterize information of channel measurements associated with the first terminal and / or information of the first terminal.

15. The method according to claim 13 or 14, characterized in that, The first information includes one or more of the following: Channel characteristic metrics, channel time-domain quality indicators, channel frequency-domain quality indicators, number of paths in the channel impulse response (CIR) whose energy is greater than k times the energy of the first path, average power of multiple sampling points, line-of-sight (LOS) probability, parameters reflecting the non-line-of-sight (NLOS) degree of the channel, signal-to-interference-plus-noise ratio, reference signal received power (RSRP), or user's moving speed, where k is a number greater than 0.

16. The method according to any one of claims 13 to 15, characterized in that, The channel measurement information associated with the first terminal is obtained based on measurements of uplink reference signals and / or downlink reference signals.

17. A communication device, characterized in that, It includes modules or units for implementing the method as described in any one of claims 1-7; or includes modules or units for implementing the method as described in any one of claims 8-12; or includes modules or units for implementing the method as described in any one of claims 13-16.

18. A communication device, characterized in that, The method includes at least one processor, which is configured to execute a computer program or instructions stored in a memory to cause the method of any one of claims 1-7 to be executed; or to cause the method of any one of claims 8-12 to be executed; or to cause the method of any one of claims 13-16 to be executed.

19. A chip, characterized in that, The device includes a circuit and a communication interface, wherein the communication interface is used to receive a signal to be processed and to send the signal to be processed to the circuit; the circuit is used to process the received signal so that the method as described in any one of claims 1-7 is executed; or, the method as described in any one of claims 8-12 is executed; or the method as described in any one of claims 13-16 is executed.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions that, when executed on a communication device, cause the communication device to perform the method as described in any one of claims 1-7; or the method as described in any one of claims 8-12; or the method as described in any one of claims 13-16.

21. A computer program product, characterized in that, The computer program product includes a computer program or instructions for performing the method as described in any one of claims 1-7, or the method as described in any one of claims 8-12, or the method as described in any one of claims 13-16.

Images