Communication method and communication device

Abstract

Provided are a model monitoring method and a communication device, the method comprising: a network device sending first information to a terminal device, the first information being used for indicating a power distribution coefficient of preset information and first data, the power distribution coefficient being used for determining the power of the preset information and the first data borne on the same RE. Wherein the power distribution coefficient is a first value corresponding to first channel estimation completed based on an AI model, the power distribution coefficient is a second value corresponding to second channel estimation completed based on a non-AI model, and a result of the first channel estimation and a result of the second channel estimation are used for performing model monitoring on the AI model. Therefore, the performance evaluation of the AI model can be realized, and the communication reliability is ensured. In addition, model monitoring can be implicitly indicated through the power distribution coefficient, and signaling overhead is saved.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically, to a communication method and a communication device. Background Technology

[0002] To address the vision of a future of intelligent and inclusive connectivity, intelligence will further evolve at the wireless network architecture level, and artificial intelligence (AI) will be more deeply integrated with wireless networks. Taking channel estimation as an example, by deploying AI models in communication devices, AI-based channel estimation can be achieved, thereby improving the efficiency of channel estimation and enhancing communication performance.

[0003] In the process of AI-based channel estimation, how to evaluate the performance of the AI ​​model is crucial to the reliability of communication. Summary of the Invention

[0004] This application provides a communication method that enables performance evaluation of AI models and ensures communication reliability.

[0005] In a first aspect, a communication method is provided, which can be executed by a terminal device or a network device, or by a component (such as a chip, circuit, or module) used in a terminal device or a network device.

[0006] The method includes: receiving first information from a first device, the first information being used to indicate a power allocation coefficient for preset information and / or first data, the power allocation coefficient being used to determine the power of the preset information and / or first data, and the preset information and first data being carried on the same resource element (RE); and determining the power allocation coefficient based on the first information.

[0007] The first value of the power allocation coefficient corresponds to the first channel estimation based on the AI ​​model, and the second value of the power allocation coefficient corresponds to the second channel estimation based on the non-AI model. The results of the first channel estimation and the second channel estimation are used to monitor the AI ​​model.

[0008] Based on the above scheme, different values ​​of the power allocation coefficient can correspond to channel estimation based on the AI ​​model and channel estimation without the AI ​​model. The channel estimation results obtained by the two methods can be used to monitor the AI ​​model. In this way, the performance evaluation of the AI ​​model can be achieved, ensuring the reliability of communication.

[0009] In addition, the power allocation coefficient can implicitly indicate model monitoring, saving signaling overhead.

[0010] For example, the first device may be a terminal device or a network device.

[0011] It is understood that the method of the first aspect can be performed by a second device or a component of the second device (such as a chip, circuit or module), and the second device can be a terminal device or a network device.

[0012] In conjunction with the first aspect, in some implementations of the first aspect, the power allocation coefficient is applied to the first time domain resource when it is a first value, and applied to the second time domain resource when it is a second value. The resource unit is located within the first time domain resource or the second time domain resource. The first time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol. The second time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol.

[0013] Based on the above scheme, this application can use frames or subframes as the monitoring granularity, or time slots or symbols as the monitoring granularity, which has greater flexibility.

[0014] For example, at least some of the resources in the first time domain resource and the second time domain resource are located in the same frame, the same subframe, or the same time slot.

[0015] Based on the above scheme, this application can realize model monitoring in the same frame, or the same subframe, or the same time slot. In scenarios where the channel changes rapidly, the performance of the AI ​​model can be evaluated more accurately. At the same time, due to the small granularity of monitoring, less resources are used, which can also reduce resource waste.

[0016] In one implementation, the power allocation coefficient is the ratio of the transmission power of the preset information to the total transmission power on the resource unit.

[0017] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 1.

[0018] In another implementation, the power allocation factor is the ratio of the transmission power of the first data to the total transmission power on the resource unit.

[0019] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 0.

[0020] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending monitoring result information to the first device, the monitoring result information being used to determine whether the AI ​​model needs to be updated, the monitoring result information being determined based on the results of the first channel estimation and the results of the second channel estimation.

[0021] Based on the above scheme, the second device sends monitoring result information to the first device so that the first device can know in a timely manner whether the AI ​​model needs to be updated. This helps the model monitoring to proceed smoothly and ensures the performance of the AI ​​model.

[0022] As one implementation method, the monitoring result information includes the error or similarity between the results of the first channel estimation and the results of the second channel estimation.

[0023] As another implementation, monitoring results information includes indications as to whether the AI ​​model needs to be updated.

[0024] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: receiving preset information or first data from the first device; and performing channel estimation based on an AI model or a non-AI model.

[0025] Specifically, channel estimation based on an AI model or a non-AI model includes: when the power allocation coefficient is a first value, performing a first channel estimation based on an AI model to obtain the result of the first channel estimation; and when the power allocation coefficient is a second value, performing a second channel estimation based on a non-AI model to obtain the result of the second channel estimation.

[0026] Based on the above scheme, this application can be used in downlink transmission scenarios to ensure the reliability of downlink channel estimation by monitoring the performance of the AI ​​model.

[0027] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: determining monitoring result information based on the first channel estimation result and the second channel estimation result.

[0028] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: receiving information from the AI ​​model of the first device.

[0029] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending preset information or first data to the first device according to the power allocation coefficient.

[0030] Specifically, sending preset information or first data to the first device according to the power allocation coefficient includes: when the power allocation coefficient is a first value, determining the power to send the preset information or first data within the first time domain resource according to the first value; when the power allocation coefficient is a second value, determining the power to send the preset information or first data within the second time domain resource according to the second value.

[0031] Based on the above scheme, the second device sends preset information or first data according to the power allocation coefficient, so that the first device can monitor the performance of the AI ​​model and ensure the reliability of channel estimation.

[0032] Secondly, a communication method is provided, which can be executed by a network device or a terminal device, or by a component (such as a chip, circuit, or module) used in a network device or a terminal device.

[0033] The method includes: sending first information to a second device, the first information being used to indicate a power allocation coefficient for preset information and / or first data, the power allocation coefficient being used to determine the power of the preset information and / or first data, and the preset information and first data being carried on the same resource unit, wherein a first value of the power allocation coefficient corresponds to a first channel estimation completed based on an AI model, a second value of the power allocation coefficient corresponds to a second channel estimation completed based on a non-AI model, and the results of the first channel estimation and the second channel estimation are used to monitor the AI ​​model.

[0034] Based on the above scheme, different values ​​of the power allocation coefficient can correspond to channel estimation based on the AI ​​model and channel estimation without the AI ​​model. The channel estimation results obtained by the two methods can be used to monitor the AI ​​model. In this way, the performance evaluation of the AI ​​model can be achieved, ensuring the reliability of communication.

[0035] In addition, the power allocation coefficient can implicitly indicate model monitoring, saving signaling overhead.

[0036] For example, the second device may be a terminal device or a network device.

[0037] It is understood that the second aspect of the method can be performed by the first device or a component of the first device (such as a chip, circuit, or module), and the first device can be a terminal device or a network device.

[0038] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: determining the first information.

[0039] In conjunction with the second aspect, in some implementations of the second aspect, the power allocation coefficient is applied to the first time domain resource when it is a first value, and applied to the second time domain resource when it is a second value. The resource unit is located within the first time domain resource or the second time domain resource. The first time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol in the time domain. The second time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol.

[0040] For example, at least some of the resources in the first time domain resource and the second time domain resource are located in the same frame, the same subframe, or the same time slot.

[0041] In one implementation, the power allocation coefficient is the ratio of the transmission power of the preset information to the total transmission power on the resource unit.

[0042] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 1.

[0043] In another implementation, the power allocation factor is the ratio of the transmission power of the first data to the total transmission power on the resource unit.

[0044] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 0.

[0045] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving monitoring result information from the second device, the monitoring result information being used to determine whether the AI ​​model needs to be updated, the monitoring result information being determined based on the results of the first channel estimation and the results of the second channel estimation.

[0046] As one implementation method, the monitoring result information includes the error or similarity between the first channel estimation result and the second channel estimation result. The method also includes: determining whether the AI ​​model needs to be updated based on the error or similarity.

[0047] As another implementation method, the monitoring results information includes indications as to whether the AI ​​model needs to be updated.

[0048] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: sending preset information or first data to the second device according to the power allocation coefficient.

[0049] Specifically, sending preset information or first data to the second device according to the power allocation coefficient includes: when the power allocation coefficient is a first value, determining the power to send the preset information or first data within the first time domain resource according to the first value; when the power allocation coefficient is a second value, determining the power to send the preset information or first data within the second time domain resource according to the second value.

[0050] Based on the above scheme, the first device sends preset information or first data according to the power allocation coefficient, so that the second device can monitor the performance of the AI ​​model and ensure the reliability of the channel estimation.

[0051] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: sending information about the AI ​​model to the second device.

[0052] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving preset information or first data from the second device; and performing channel estimation based on an AI model or a non-AI model.

[0053] Specifically, channel estimation based on an AI model or a non-AI model includes: when the power allocation coefficient is a first value, performing a first channel estimation based on an AI model to obtain the result of the first channel estimation; and when the power allocation coefficient is a second value, performing a second channel estimation based on a non-AI model to obtain the result of the second channel estimation.

[0054] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: determining whether the AI ​​model needs to be updated based on the results of the first channel estimation and the second channel estimation.

[0055] Based on the above scheme, monitoring the performance of the AI ​​model through the first device can ensure the reliability of channel estimation.

[0056] Thirdly, a communication device is provided, which can be a terminal device or a network device, or a component (such as a chip, circuit or module) for a terminal device or a network device.

[0057] The device includes: a transceiver unit for receiving first information from a first device, the first information indicating a power allocation coefficient for preset information and / or first data, the power allocation coefficient being used to determine the power of the preset information and / or the first data, and the preset information and the first data being carried on the same resource unit; and a processing unit for determining the power allocation coefficient based on the first information. The power allocation coefficient is a first value corresponding to a first channel estimation based on an AI model, and a second value corresponding to a second channel estimation based on a non-AI model. The results of the first and second channel estimations are used for model monitoring of the AI ​​model.

[0058] In conjunction with the third aspect, in some implementations of the third aspect, the power allocation coefficient is applied to the first time domain resource when it is a first value, and applied to the second time domain resource when it is a second value. The resource unit is located within the first time domain resource or the second time domain resource. The first time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol. The second time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol.

[0059] For example, at least some of the resources in the first time domain resource and the second time domain resource are located in the same frame, the same subframe, or the same time slot.

[0060] In one implementation, the power allocation coefficient is the ratio of the transmission power of the preset information to the total transmission power on the resource unit.

[0061] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 1.

[0062] In another implementation, the power allocation factor is the ratio of the transmission power of the first data to the total transmission power on the resource unit.

[0063] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 0.

[0064] In conjunction with the third aspect, in some implementations of the third aspect, the transceiver unit is also used to: send monitoring result information to the first device, the monitoring result information being used to determine whether the AI ​​model needs to be updated, and the monitoring result information being determined based on the results of the first channel estimation and the second channel estimation.

[0065] As one implementation method, the monitoring result information includes the error or similarity between the results of the first channel estimation and the results of the second channel estimation.

[0066] As another implementation, monitoring results information includes indications as to whether the AI ​​model needs to be updated.

[0067] In conjunction with the third aspect, in some implementations of the third aspect, the transceiver unit is further configured to: receive preset information or first data from the first device; the processing unit is further configured to: perform channel estimation based on an AI model or a non-AI model.

[0068] Specifically, the processing unit is used to: perform a first channel estimation based on an AI model when the power allocation coefficient is a first value, and obtain the result of the first channel estimation; and perform a second channel estimation based on a non-AI model when the power allocation coefficient is a second value, and obtain the result of the second channel estimation.

[0069] In conjunction with the third aspect, in some implementations of the third aspect, the processing unit is also used to: determine monitoring result information based on the first channel estimation result and the second channel estimation result.

[0070] In conjunction with the third aspect, in some implementations of the third aspect, the transceiver unit is also used to: receive information from the AI ​​model of the first device.

[0071] In conjunction with the third aspect, in some implementations of the third aspect, the transceiver unit is also used to: send preset information or first data to the first device according to the power allocation coefficient.

[0072] Specifically, the transceiver unit is used to: determine the power for transmitting preset information or first data within a first time domain resource based on the first value when the power allocation coefficient is a first value; and determine the power for transmitting preset information or first data within a second time domain resource based on the second value when the power allocation coefficient is a second value.

[0073] Fourthly, a communication device is provided, which may be a network device or a terminal device, or may be a component (such as a chip, circuit or module) for a network device or a network device.

[0074] The device includes: a transceiver unit for sending first information to a second device, the first information being used to indicate a power allocation coefficient for preset information and / or first data, the power allocation coefficient being used to determine the power of the preset information and / or first data, and the preset information and first data being carried on the same resource unit, wherein a first value of the power allocation coefficient corresponds to a first channel estimation completed based on an AI model, a second value of the power allocation coefficient corresponds to a second channel estimation completed based on a non-AI model, and the results of the first channel estimation and the second channel estimation are used to monitor the AI ​​model.

[0075] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the apparatus further includes: a processing unit for determining the first information.

[0076] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the power allocation coefficient is applied to the first time domain resource when it is a first value, and applied to the second time domain resource when it is a second value. The resource unit is located within the first time domain resource or the second time domain resource. The first time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol in the time domain. The second time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol.

[0077] For example, at least some of the resources in the first time domain resource and the second time domain resource are located in the same frame, the same subframe, or the same time slot.

[0078] In one implementation, the power allocation coefficient is the ratio of the transmission power of the preset information to the total transmission power on the resource unit.

[0079] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 1.

[0080] In another implementation, the power allocation factor is the ratio of the transmission power of the first data to the total transmission power on the resource unit.

[0081] For example, in this implementation, the first value is greater than 0 and less than 1, and the second value is equal to 0.

[0082] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the transceiver unit is also used to: receive monitoring result information from the second device, the monitoring result information being used to determine whether the AI ​​model needs to be updated, and the monitoring result information being determined based on the results of the first channel estimation and the second channel estimation.

[0083] As one implementation, the monitoring result information includes the error or similarity between the first channel estimation result and the second channel estimation result. The processing unit is also used to determine whether the AI ​​model needs to be updated based on the error or similarity.

[0084] As another implementation method, the monitoring results information includes indications as to whether the AI ​​model needs to be updated.

[0085] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the transceiver unit is also used to: send preset information or first data to the second device according to the power allocation coefficient.

[0086] Specifically, the transceiver unit is used to: determine the power for transmitting preset information or first data within a first time domain resource based on the first value when the power allocation coefficient is a first value; and determine the power for transmitting preset information or first data within a second time domain resource based on the second value when the power allocation coefficient is a second value.

[0087] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the transceiver unit is also used to: send information about the AI ​​model to the second device.

[0088] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the transceiver unit is also used to: receive preset information or first data from the second device; the processing unit is also used to: perform channel estimation based on an AI model or a non-AI model.

[0089] Specifically, the processing unit is used to: perform a first channel estimation based on an AI model when the power allocation coefficient is a first value, and obtain the result of the first channel estimation; and perform a second channel estimation based on a non-AI model when the power allocation coefficient is a second value, and obtain the result of the second channel estimation.

[0090] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the processing unit is also used to: determine whether the AI ​​model needs to be updated based on the results of the first channel estimation and the second channel estimation.

[0091] Fifthly, a communication device is provided, comprising: at least one processor for executing a computer program or instructions to perform the method in any possible implementation of the first or second aspect described above. Optionally, the device further comprises a memory for storing the computer program or instructions. Optionally, the device further comprises a communication interface through which the processor reads the computer program or instructions.

[0092] In one implementation, the device is a communication device (such as a first device, or a second device).

[0093] In another implementation, the device is a chip, chip system, or circuit for a communication device (such as the first device or the second device).

[0094] A sixth aspect provides a processor for performing the methods provided in the first or second aspect described above.

[0095] Unless otherwise specified, or if it does not contradict its actual function or internal logic in the relevant description, the transmission and acquisition / reception operations involved in the processor can be understood as processor output and reception, input and other operations, or as transmission and reception operations performed by radio frequency circuits and antennas. This application does not limit them in this regard.

[0096] Optionally, the device further includes: a memory for storing a program; correspondingly, at least one processor for executing the computer program or instructions in the memory.

[0097] Optionally, the device also includes a communication interface. The communication interface is coupled to the processor and can be used to input information to the processor or output information from the processor.

[0098] In a seventh aspect, a computer-readable storage medium is provided that stores program code for execution by a device, the program code including methods for performing any possible implementation of the first or second aspect described above.

[0099] Eighthly, a computer program product containing instructions is provided, which, when run on a computer, causes the computer to perform the method in any possible implementation of the first or second aspect described above.

[0100] Ninth aspect, a chip is provided, the chip including a processor and a communication interface, the processor reading instructions from a memory through the communication interface and executing the method provided by any of the above implementations of the first or second aspect.

[0101] Optionally, as one implementation, the chip also includes a memory storing computer programs or instructions, and a processor for executing the computer programs or instructions in the memory. When the computer programs or instructions are executed, the processor is used to perform the method provided by any of the above implementations of the first or second aspect.

[0102] In a tenth aspect, a communication system is provided, comprising a second device and a first device, wherein the second device is configured to implement the method provided by the first aspect and any possible implementation thereof; and the first device is configured to implement the method provided by the second aspect and any possible implementation thereof.

[0103] It should be understood that the beneficial effects of aspects two through ten and any of their implementations can be referenced in aspect one and any of its implementations. Attached Figure Description

[0104] Figure 1 This is a schematic diagram of the communication system used in the embodiments of this application.

[0105] Figure 2 This is a schematic diagram of another communication system applicable to embodiments of this application.

[0106] Figure 3 This is a schematic diagram of a possible application framework in a communication system.

[0107] Figure 4 This is a schematic diagram of a possible application framework in a communication system.

[0108] Figure 5 This is a schematic diagram of the structure of data and pilot signals within time-frequency resources.

[0109] Figure 6 This is a schematic flowchart of a communication method 600 provided in this application.

[0110] Figure 7 This is a schematic flowchart of a communication method 700 provided in this application.

[0111] Figure 8 and Figure 9 This is a schematic diagram of two application scenarios for the power allocation factor.

[0112] Figure 10 and Figure 11 A schematic block diagram of a communication device provided in an embodiment of this application. Detailed Implementation

[0113] The scheme of this application will now be described with reference to the accompanying drawings.

[0114] 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, satellite communication systems, future communication systems such as 6th generation (6G) mobile communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0115] In a communication system, a device can send signals to or receive signals from another device. These signals can include information, signaling, or data. The device can also be replaced by an entity, network entity, network element, communication equipment, communication module, node, communication node, etc. This application uses a device as an example for description. 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. It is understood that the terminal device in this application can be replaced by a second device, and the network device can be replaced by a first device, both performing the corresponding communication methods described in this application.

[0116] Figure 1 This is a schematic diagram of a communication system applicable to an embodiment 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.

[0117] 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.

[0118] Terminal devices can be devices that provide voice / data, such as handheld devices with wireless connectivity, in-vehicle devices, etc. 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, 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.

[0119] By way of example and not 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.

[0120] In this embodiment, the device for implementing 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 those functions, such as a chip system. This device can be installed in or used in conjunction with the terminal device. In this embodiment, the chip system can be composed of chips or may include chips and other discrete components. This embodiment only uses the terminal device as an example to illustrate the device for implementing the functions of the terminal device, and does not constitute a limitation on the solution of this embodiment.

[0121] The network device in this application embodiment may include a device for communicating with a terminal device. This network device may include an access network device or a radio access network device, such as a base station. In this application embodiment, the access network device may refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network. 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), positioning node, 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, equipment performing base station functions in D2D, V2X, and M2M communications, network-side equipment in 6G networks, and equipment performing 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.

[0122] 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.

[0123] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CUs, DUs, or devices including both CUs and DUs, 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, network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0124] 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.

[0125] RAN nodes can support one or more types of fronthaul interfaces, with different fronthaul interfaces corresponding to DUs and RUs with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is an enhanced common public radio interface (eCPRI), compared to CPRI, some downlink and / or uplink baseband functions are moved from the DU to the RU. Different splitting methods between DUs and RUs correspond to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0126] Taking eCPRI Cat A as an example, for downlink transmission, layer mapping is used as the dividing line. DU is configured to implement one or more functions preceding layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions following layer mapping (e.g., RE mapping, digital beamforming (BF), or one or more inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to RU. For uplink transmission, deRE mapping is used as the dividing line. DU is configured to implement one or more functions preceding deRE mapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and deRE mapping), while other functions following deRE mapping (e.g., digital BF or fast Fourier transform (FFT) / CP removal) are moved to RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0127] 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.

[0128] 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.

[0129] 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 those functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed 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 provided only and does not constitute a limitation on the solutions described in this embodiment.

[0130] 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.

[0131] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, leading to increasingly diverse requirements. For example, networks need to support ultra-high speeds, ultra-low latency, and / or massive connectivity. This characteristic makes network planning, network configuration, and / or resource scheduling increasingly complex. Furthermore, as network functions become more powerful, such as supporting higher spectrum levels, supporting higher-order multiple-input multiple-output (MIMO) technologies, supporting beamforming, and / or supporting beam management, network energy efficiency has become a hot research topic. These new requirements, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence technology can be introduced into wireless communication networks to achieve network intelligence.

[0132] To support AI technology in wireless networks, AI nodes may also be introduced into the network.

[0133] Optionally, the AI ​​node can be deployed in one or more of the following locations within the communication system: access network equipment, terminal equipment, or core network equipment, etc. Alternatively, the AI ​​node 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. The AI ​​node can communicate with other devices in the communication system, which can be one or more of the following: network equipment, terminal equipment, or core network elements, etc.

[0134] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, these nodes can be divided based on function, such as different AI nodes being responsible for different functions.

[0135] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to achieve different functions. Alternatively, 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 aforementioned AI nodes.

[0136] AI nodes can be AI network elements or AI modules. The following combines... Figures 2 to 4 Please provide an explanation.

[0137] Figure 2 This is a schematic diagram of another 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 or training AI models.

[0138] In one possible implementation, network device 110 can send data related to the training of the AI ​​model to AI network element 140, which then constructs a training dataset and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. AI network element 140 can send the results of operations related to the AI ​​model to network device 110, which then forwards them to the terminal device. For example, the results of operations related to the AI ​​model 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 device. Alternatively, the trained AI model may be deployed on network device 110. Or, the trained AI model may be deployed on the terminal device.

[0139] 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 a terminal device. Alternatively, AI network element 140 can be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 can also be connected to network device 110 through a third-party network element. This application embodiment does not limit the connection relationship between AI network element and other network elements.

[0140] 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.

[0141] 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 an operations, administration, and maintenance (OAM) system, are equipped with one or more AI modules (for clarity, ...). Figure 3 (Only one is shown in the image). The access network node can be a single RAN node or can include multiple RAN nodes, such as CU and 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 CU-CP and / or CU-UP.

[0142] 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.

[0143] 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.

[0144] Figure 4 This is a schematic diagram of a possible application framework in a communication system. For example... Figure 4 As shown, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be... Figure 3The AI ​​modules 117 and 118 shown are used to implement AI-related functions. The RIC includes near-real-time RIC (near-RT RIC) and non-real-time RIC (non-RT RIC). Non-real-time RIC primarily processes non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RIC primarily processes near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0145] The near real-time RIC is used for model training and inference. For example, it can be used to train an AI model and then use that AI model for inference. The near real-time RIC 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 inference data. Optionally, the near real-time RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the near real-time RIC delivers the inference result to the DU, and the DU sends it to the RU.

[0146] The non-real-time RIC is also used for model training and inference. For example, it can be used to train an AI model and then use that model for inference. The non-real-time RIC 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 inference data, and the inference results can be delivered to the RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the non-real-time RIC delivers the inference results to the DU, which then forwards them to the RU.

[0147] The near real-time RIC and non-real-time RIC can also be set up as separate network elements. Optionally, the near real-time RIC and non-real-time RIC can also be part of other devices. For example, the near real-time RIC can be set in the RAN node (e.g., in CU, DU), while the non-real-time RIC can be set in the OAM, cloud server, core network device, or other network device.

[0148] It should be understood that Figures 1 to 4 This is a simplified illustration for ease of understanding only. The communication system may also include other network devices, other terminal devices, or other AI nodes, and this application does not limit this.

[0149] To facilitate understanding of the embodiments of this application, some basic concepts involved in this application will be briefly explained.

[0150] 1. Artificial Intelligence (AI): This refers to enabling machines to learn, accumulate experience, and solve problems that humans can solve through experience, such as natural language understanding, image recognition, and chess. AI can be understood as the intelligence exhibited by machines created by humans. Generally, AI refers to the technology of using computer programs to represent human intelligence. The goals of AI include understanding intelligence by constructing computer programs that demonstrate symbolic reasoning or logical reasoning.

[0151] 2. Machine Learning (ML): This is an implementation method of artificial intelligence. Machine learning is a method that endows machines with the ability to perform functions that cannot be done directly by programming. In practical terms, machine learning is a method of training a model using data and then using the model to make predictions. There are many methods of machine learning, such as neural networks (NN), decision trees, and support vector machines. Machine learning theory mainly involves designing and analyzing algorithms that enable computers to learn automatically. Machine learning algorithms are a class of algorithms that automatically analyze data to obtain patterns and use these patterns to predict unknown data.

[0152] 3. AI Model: An AI model is an algorithm or computer program that enables AI functionality. It represents the mapping relationship between the model's input and output; in other words, it's a function model that maps a certain dimension of input to a certain dimension of output. The parameters of this function model can be obtained through machine learning training. For example, f(x) = ax 2 +b is a quadratic function model, which can be viewed as an AI model, where a and b are the parameters of this AI model, and a and b can be obtained through machine learning training. An AI model can also be called an ML model.

[0153] It is understood that AI models can be implemented as hardware circuits, software, or a combination of both; there are no restrictions. Non-restrictive examples of software include: program code, program, subroutine, instructions, instruction sets, code, code segments, software modules, applications, or software applications, etc. AI models can be neural networks, linear regression models, decision tree models, support vector machines (SVM), Bayesian networks, Q-learning models, or other machine learning models.

[0154] 4. Model Monitoring: This is used to observe the running AI model and ensure its performance and reliability. Before model monitoring, the performance of the AI ​​model is judged by observing its performance on a pre-provided dataset (i.e., data collected from non-real-world environments). After training on a static dataset (i.e., training data), the AI ​​model is put into inference tasks in constantly changing real-world scenarios. This difference between the static dataset during training and the dynamically changing data in actual use can cause the performance of the AI ​​model to degrade over time, necessitating model monitoring during operation.

[0155] 5. Pilot: Also known as a reference signal, it is mainly used for channel estimation, data demodulation, beam training, and time-frequency parameter tracking. Commonly used pilots include synchronization signal, demodulation reference signal (DMRS), channel state information-reference signal (CSI-RS), sounding reference signal (SRS), phase tracking reference signal (PTRS), positioning reference signal (PRS), and tracking reference signal (TRS). Among them, CSI-RS and PRS are mainly used for downlink, SRS and TRS are mainly used for uplink, and DMRS and PTRS are applicable to both uplink and downlink.

[0156] Currently, there are two main structures for pilots and data within time-frequency resources. One is a frame structure where data and pilots are orthogonal, meaning data and pilots are mapped to different REs, such as... Figure 5 As shown in (a), another type is the frame structure where data and pilot signals are superimposed, i.e., data and pilot signals are mapped to the same RE, such as... Figure 5 As shown in (b).

[0157] exist Figure 5 In this diagram, each small square represents a RE (Resource Array), and each RE has a time domain width of one symbol and a frequency domain width of one subcarrier. For example, a time slot in the time domain may include 14 symbols with symbol indices from 0 to 13, and a resource block (RB) in the frequency domain may include 12 subcarriers with subcarrier indices from 0 to 11.

[0158] Depend on Figure 5 As can be seen, mapping data and pilots to the same RE can reduce the restrictions on the length and time-frequency position of the pilots, giving the pilots greater freedom, and can also improve the resource utilization rate of data and increase throughput.

[0159] To address the vision of a future of intelligent and inclusive accessibility, intelligence will further evolve at the wireless network architecture level, and AI will be more deeply integrated with wireless networks. Taking channel estimation as an example, by deploying AI models in communication devices, AI-based channel estimation can be achieved, thereby improving the efficiency of channel estimation and enhancing communication performance.

[0160] Taking a downlink transmission scenario as an example, when data and pilot signals are mapped to the same RE, the base station transmits a signal superimposed on the data and pilot signals on the PDSCH. This signal is received by the UE after passing through the channel. The UE uses the received signal as input to an AI model, which performs channel estimation; that is, the output of the AI ​​model includes estimated channel information. It should be understood that in addition to the received signal, the input to the AI ​​model may also include other known signals, such as DMRS symbols known to the UE.

[0161] To monitor the performance of the AI ​​model, one approach is for the base station to add resources with orthogonal frame structures (hereinafter referred to as orthogonal resources) to the continuously transmitted data and pilot superimposed frame structure resources (hereinafter referred to as superimposed resources). The base station then instructs the UE to perform channel estimation on the signals received from these orthogonal resources using a traditional method (i.e., non-AI model). Alternatively, the UE will perform channel estimation on the signals received from the superimposed resources using an AI model-based method. Considering the correlation of the channel in the time dimension, the UE compares the channel estimation results obtained by these two methods to evaluate the performance of the AI ​​model and feeds it back to the base station, thus completing the entire monitoring process.

[0162] However, this approach requires independent configuration of different resource types (i.e., overlay resources and orthogonal resources), and also requires the introduction of additional signaling to instruct users to monitor the model, resulting in significant resource waste.

[0163] In view of this, this application provides a communication method and a communication device that can not only perform performance evaluation of AI models and ensure communication reliability, but also implicitly instruct the receiving end to monitor the model, saving signaling overhead.

[0164] It should be understood that the embodiments of this application can be applied to communication between network devices and terminal devices, communication between terminal devices, and communication between network devices; this application does not limit this. The following description uses communication between a network device and a terminal device as an example, where the network device can be considered an example of a first device, and the terminal device can be considered an example of a second device.

[0165] Figure 6 This is a schematic flowchart of a communication method 600 provided in this application. Figure 6 As shown, the method 600 includes the following steps.

[0166] S610, the network device sends the first information to the terminal device, and the terminal device receives the first information accordingly.

[0167] The first information is used to indicate the power allocation coefficient of the preset information and / or the first data.

[0168] In this application, the preset information may also be referred to as known information, pilot signal, first signal, reference signal, etc., and the preset information may be a synchronization signal, DMRS, CSI-RS, SRS, PTRS, PRS, TRS, etc. The preset information may be the pre-configuration information of the terminal device, or the information indicated by the network device to the terminal device.

[0169] In this application, the first data can be information carried on a data channel (such as physical downlink shared channel (PDSCH), physical uplink shared channel (PUSCH), physical downlink control channel (PDCCH), physical uplink control channel (PUCCH), physical sidelink shared channel (PSSCH), physical sidelink control channel (PSCCH), etc.), or information carried on a broadcast channel (such as broadcast channel (BCH), broadcast control channel (BCCH), etc.), or information transmitted on the Xn interface between network devices.

[0170] The power allocation coefficient is used to determine the power of the preset information and / or the first data. The preset information and the first data are carried on the same RE, for example, as shown below. Figure 5 As shown in (b), the power allocation coefficient can be used to determine the power of preset information and / or first data.

[0171] In this application, the power allocation coefficient can also be referred to as the power superposition coefficient, power division coefficient, pilot power superposition coefficient, or pilot power division coefficient.

[0172] Specifically, during resource mapping, both preset information and first data are mapped to the same RE. Power allocated to the first data and / or preset information can be scheduled, and the power allocation coefficient can be the proportion of the preset information's transmission power in the total transmission power of that RE. The total transmission power of that RE is the sum of the transmission power of the preset information and the transmission power of the first data. For example, a power allocation coefficient of 0.5 indicates that the preset information's transmission power accounts for 0.5% of the total transmission power of that RE. Optionally, the power allocation coefficient can also be the proportion of the first data's transmission power in the total transmission power of that RE, or the ratio of the preset information's transmission power to the first data's transmission power, as long as it represents the magnitude relationship between the preset information's and the first data's transmission power. As an example, when the power allocation coefficient is the ratio of the preset information's transmission power to the first data's transmission power, the first value can be a value approximately equal to 1, such as 1.01 or 0.9, and the second value can be a value much larger than the first value, such as 10 or 50. For ease of explanation, the following description uses the power allocation coefficient as the proportion of the preset information's transmission power in the total transmission power of that RE as an example.

[0173] For example, when mapped to the same RE, if the power allocated to the preset information is 0.5 and the power allocated to the first data is 0.5, then the final symbol X is:

[0174]

[0175] In formula (1), "pilot" refers to the symbol of the preset information on a specific RE, and "data symbol" refers to the symbol of the first data on the same RE. 0.5 is an example of the power allocation coefficient. In this application, the power allocation coefficient is a value greater than or equal to 0 and less than or equal to 1, that is, the range of the first and second values ​​is both 0 to 1.

[0176] In this application, the first data and preset information can be generated based on a constellation mapping method. For example, the preset information can be generated based on quadrature phase shift keying (QPSK), and the first data can be generated based on QPSK, quadrature amplitude modulation (QAM), 64QAM, 256QAM, or 1024QAM. Optionally, the first data can also be generated based on a constellation modulation method trained by AI. It should be understood that the embodiments of this application do not limit the constellation mapping method of the preset information and / or the first data. As long as the constellation mapping meets the requirements (e.g., energy normalization), it can be implemented to map both onto the same RE.

[0177] Different values ​​of the power allocation coefficient can implicitly indicate different channel estimation methods, and thus can be used to indicate model monitoring. For example, a first value of the power allocation coefficient corresponds to a first channel estimation based on an AI model, while a second value corresponds to a second channel estimation based on a non-AI model. The results of the first and second channel estimations are used to monitor the AI ​​model. Optionally, the first data corresponding to the first value of the power allocation coefficient and the first data corresponding to the second value of the power allocation coefficient may be of different channel types or the same channel type. The channel type may be, for example, a physical channel type. For instance, the first data corresponding to the first value of the power allocation coefficient may be a physical downlink shared channel, while the first data corresponding to the second value of the power allocation coefficient may be a physical downlink control channel. Optionally, the preset information corresponding to the first value of the power allocation coefficient and the preset information corresponding to the second value of the power allocation coefficient may be different preset information, such as different reference signals or different reference signal types, or the same preset information, such as the same reference signal type or the same reference signal.

[0178] In other words, with the power factor at a first value, the terminal device or network device can perform a first channel estimation based on the AI ​​model and obtain the result of the first channel estimation; with the power allocation factor at a second value, the terminal device or network device can perform a second channel estimation based on a non-AI model and obtain the result of the second channel estimation. Furthermore, by comparing the results of the first channel estimation and the results of the second channel estimation, the performance of the AI ​​model can be monitored.

[0179] It should be understood that channel estimation can also be called channel measurement, without any restriction.

[0180] In this application, the AI ​​model can be understood as an AI algorithm. The AI ​​model can be used to complete channel estimation. Optionally, the AI ​​model can also be used to implement one or more functions such as channel equalization, data demodulation, and decoding.

[0181] In this application, the non-AI model can be understood as a non-AI algorithm or a traditional mathematical algorithm that can be used for channel estimation. Examples include least squares (LS), linear minimum mean square error (LMMESE), and compressed sensing (CS) algorithms.

[0182] For example, in this application, the difference between the first value and the second value is greater than a threshold, for example, the first value is 0.5 and the second value is 1, and the threshold can be 0.5. Optionally, the first value is less than the second value.

[0183] When the power allocation coefficient is 1, it means that all the transmission power is concentrated on the preset information. From the receiver's perspective, this can be understood as only the preset information was transmitted and the first data was not transmitted, or in other words, there is no useful first data on the RE.

[0184] Furthermore, both the first and second channel estimations are performed based on preset information. In other words, when the power factor is a first value, the terminal device or network device can process the preset information based on an AI model to obtain the result of the first channel estimation; when the power allocation factor is a second value, the terminal device or network device can process the preset information based on a non-AI model to obtain the result of the second channel estimation.

[0185] Optionally, when the power allocation coefficient takes a first value, the preset information is also used for demodulation of the first data. Specifically, after the terminal device or network device processes the preset information to obtain the result of the first channel estimation, it can also perform channel equalization, data demodulation, and decoding based on the result of the first channel estimation to obtain the first data.

[0186] In this application, the first information is used to indicate the power allocation coefficient, and different values ​​of the power allocation coefficient can be used to implicitly indicate model monitoring of the AI ​​model. Therefore, it can be understood that when the power allocation coefficient of the preset information and / or the first data is a first value, the first information is used to indicate the power allocation coefficient, or when the power allocation coefficient of the preset information and / or the first data is a second value, the first information is used not only to indicate the power allocation coefficient, but also to indicate model monitoring.

[0187] It should be understood that network devices can send the first information to terminal devices every time the power factor changes, i.e., non-periodic transmission, or periodic transmission of the current power allocation factor.

[0188] For example, network devices can send the first information to terminal devices through higher-layer signaling, such as radio resource control (RRC) signaling or medium access control control element (MAC CE) signaling, or through physical layer signaling, such as downlink control information (DCI).

[0189] S620, network devices and terminal devices transmit preset information and / or first data.

[0190] For downlink transmission scenarios, S620 can be replaced with: S620a, where the network device sends preset information and / or first data to the terminal device, and the terminal device receives the preset information and / or first data accordingly.

[0191] Specifically, the network device can send preset information and / or first data to the terminal device according to a power allocation coefficient. For example, when the power allocation coefficient is a first value, the network device determines the power to send the preset information and / or first data based on the first value; when the power allocation coefficient is a second value, the network device determines the power to send the preset information and / or first data based on the second value. Correspondingly, the terminal device can determine the channel estimation method according to the power allocation coefficient. For example, when the power allocation coefficient is a first value, the terminal device can input the received signal into an AI model, complete the first channel estimation based on the AI ​​model, and obtain the result of the first channel estimation. Optionally, when completing the first channel estimation based on the AI ​​model, the terminal device can input the received signal into the AI ​​model, and also input the corresponding preset information into the AI ​​model. It should be understood that the preset information is known information to the terminal device; it can be information indicated by the network device to the terminal device before S620a, or it can be pre-configured information of the terminal device. When the power allocation coefficient is a second value, the terminal device can process the received signal based on a non-AI model to complete the second channel estimation and obtain the result of the second channel estimation. Furthermore, when the power allocation coefficient is the second value, the terminal device can also evaluate the performance of the AI ​​model based on the results of the second channel estimation. For example, by comparing the results of the first channel estimation and the results of the second channel estimation, the monitoring result information of the AI ​​model can be determined.

[0192] Based on the above scheme, different values ​​of the power allocation coefficient can correspond to channel estimation based on the AI ​​model and channel estimation without the AI ​​model. The channel estimation results obtained by the two methods can be used to monitor the AI ​​model. In this way, not only can the performance of the AI ​​model be evaluated and the reliability of communication be guaranteed, but the terminal device can also be implicitly instructed to perform model monitoring, saving signaling overhead.

[0193] Optionally, in this scenario, method 600 further includes: the terminal device sending monitoring result information to the network device, the monitoring result information being used to determine whether the AI ​​model needs to be updated, the monitoring result information being determined based on the results of the first channel estimation and the second channel estimation.

[0194] Specifically, whether the AI ​​model needs to be updated can be replaced with: whether there is a need to update the AI ​​model, or whether the conditions for updating the AI ​​model are met.

[0195] The monitoring results may include one or more aspects such as the performance evaluation index of the AI ​​model, the loss function value, the model stability analysis, and anomaly detection or early warning.

[0196] As an example, the monitoring results information includes the error or similarity between the results of the first channel estimation and the results of the second channel estimation.

[0197] For example, this error can be represented by normalized mean squared error (NMSE), variance, standard deviation, etc., and similarity can be represented by cosine similarity, Pearson correlation coefficient, Jaccard coefficient, etc.

[0198] In this example, the network device can also determine whether the AI ​​model needs to be updated based on errors or similarity. In other words, the terminal device can send intermediate information from model monitoring, such as errors or similarity, to the network device, which then determines whether the model needs to be updated, for example, by retraining it.

[0199] As another example, the monitoring results include indications of whether the AI ​​model needs to be updated. In other words, the terminal device can determine whether the AI ​​model needs to be updated and then instruct the network device accordingly.

[0200] For example, the terminal device can first determine the error or similarity between the results of the first channel estimation and the results of the second channel estimation, and then determine whether the AI ​​model needs to be updated based on the error or similarity.

[0201] For example, when the error is greater than a first threshold, it means that the AI ​​model needs to be updated; or when the similarity is less than a second threshold, it means that the AI ​​model needs to be updated.

[0202] Based on the above scheme, the terminal device sends monitoring result information to the network device so that the network device can know in a timely manner whether the AI ​​model needs to be updated. This helps the model monitoring to proceed smoothly and ensures the performance of the AI ​​model.

[0203] Optionally, in this scenario, method 600 further includes: the network device sending information about the AI ​​model to the terminal device, and correspondingly, the terminal device receiving the information about the AI ​​model.

[0204] Specifically, in downlink transmission scenarios, AI models can be deployed on the terminal device side, such as on the terminal device itself, or on an OTT device on the terminal device side. The information of the AI ​​model on the terminal device can be received from network devices or the cloud, such as OTT devices or servers. For example, the network device can train the AI ​​model and send the parameters of the AI ​​model, such as weights and biases, to the terminal device so that the terminal device can use the AI ​​model to complete the first channel estimation.

[0205] For example, when the AI ​​model is deployed on an OTT device on the terminal device side, the first channel estimation can be implemented by the OTT device, and the second channel estimation and / or model monitoring can be implemented by the terminal device or by the OTT device.

[0206] For uplink transmission scenarios, S620 can be replaced with S620b, where the terminal device sends preset information and / or first data to the network device, and the network device receives the preset information and / or first data accordingly.

[0207] Specifically, the terminal device can send preset information and / or first data to the network device according to a power allocation coefficient. For example, when the power allocation coefficient is a first value, the terminal device determines the power to send the preset information and / or first data based on the first value; when the power allocation coefficient is a second value, the terminal device determines the power to send the preset information and / or first data based on the second value. Correspondingly, the network device can determine the channel estimation method based on the power allocation coefficient. For example, when the power allocation coefficient is a first value, the network device can input the received signal into an AI model, complete channel estimation based on the AI ​​model, and obtain the first channel estimation result. Similar to the downlink transmission scenario, in the uplink transmission scenario, when the network device completes the first channel estimation based on the AI ​​model, the network device can input the received signal into the AI ​​model, and also input the corresponding preset information into the AI ​​model. When the power allocation coefficient is a second value, the network device can process the received signal based on a non-AI model to complete channel estimation and obtain the second channel estimation result. Furthermore, when the power allocation coefficient is a second value, the network device can also evaluate the performance of the AI ​​model based on the second channel estimation result. For example, by comparing the first channel estimation result and the second channel estimation result, the monitoring result information of the AI ​​model can be determined.

[0208] Based on the above scheme, different values ​​of the power allocation coefficient can correspond to channel estimation based on the AI ​​model and channel estimation without the AI ​​model. The channel estimation results obtained by the two methods can be used to monitor the AI ​​model. In this way, not only can the performance of the AI ​​model be evaluated and the reliability of communication be guaranteed, but also the terminal equipment can be implicitly instructed to assist the network equipment in completing the model monitoring, thus saving signaling overhead.

[0209] Optionally, in this scenario, method 600 further includes: the network device determining whether the AI ​​model needs to be updated based on the results of the first channel estimation and the second channel estimation.

[0210] Based on the above solution, network devices can promptly determine whether the AI ​​model needs to be updated, ensuring the performance of the AI ​​model.

[0211] Specifically, in uplink transmission scenarios, AI models can be deployed on the network device side, such as on the network device itself, or on an OTT device on the network device side. For example, the network device can train the AI ​​model and use the AI ​​model to complete the first channel estimation.

[0212] For example, in the case where the AI ​​model is deployed on an OTT device on the network device side, the first channel estimation can be implemented by the OTT device, and the second channel estimation and / or model monitoring can be implemented by the network device or by the OTT device.

[0213] Optionally, in any of the above scenarios, the method further includes: the network device determining the first information.

[0214] It should be understood that in both downlink and uplink transmission scenarios, network devices can determine the power allocation factor and indicate the power allocation factor to the terminal device.

[0215] In this process, network devices can determine power allocation coefficients through AI training. For example, power allocation coefficients and AI models can be trained together; the network device can train the power allocation coefficients while simultaneously training the AI ​​model used for first channel estimation.

[0216] Optionally, in any of the above scenarios, the power allocation coefficient is applied to the first time-domain resource when it is a first value, and applied to the second time-domain resource when it is a second value. The RE carried by the preset information and the first data is located within the first or second time-domain resource. In other words, the power allocation coefficient corresponding to one or more time-domain units (i.e., the first time-domain resource) in the time domain takes the second value, while the power allocation coefficient corresponding to other time-domain units (i.e., the second time-domain resource) in the time domain takes the first value.

[0217] Specifically, when the power allocation coefficient is a first value, the preset information and the first data are carried in the same RE in the first time domain resource. For downlink transmission scenarios, the network device can send the preset information and / or the first data to the terminal device within the first time domain resource with the first value of the power allocation coefficient. Correspondingly, the terminal device receives the preset information and / or the first data within the first time domain resource. For uplink transmission scenarios, the terminal device can send the preset information and / or the first data to the network device within the first time domain resource with the first value of the power allocation coefficient. Correspondingly, the network device receives the preset information and / or the first data within the first time domain resource. When the power allocation coefficient is the second value, the preset information and the first data are carried in the same RE in the second time domain resource. For downlink transmission scenarios, the network device can send the preset information and / or the first data to the terminal device in the second time domain resource with the power allocation coefficient of the second value. Correspondingly, the terminal device receives the preset information and / or the first data in the second time domain resource. For uplink transmission scenarios, the terminal device can send the preset information and / or the first data to the network device in the second time domain resource with the power allocation coefficient of the second value. Correspondingly, the network device receives the preset information and / or the first data in the second time domain resource.

[0218] The first time-domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol, while the second time-domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol.

[0219] Taking the NR system as an example, a frame lasts for 10 ms and can be divided into 10 subframes, numbered 0-9. Each subframe lasts for 1 ms. The number of time slots in each subframe is related to the subcarrier spacing (SCS), as shown in Table 1. In addition, each time slot in the normal cyclic prefix (CP) includes 14 symbols.

[0220] Table 1

[0221] Subcarrier spacing (kHz) Number of time slots included in each subframe 15 1 30 2 60 4 120 8 240 16

[0222] It should be understood that the lengths of the first and second temporal resources may or may not be in the same dimension. For example, the first temporal resource may consist of multiple subframes, and the second temporal resource may also consist of multiple subframes. Alternatively, the first temporal resource may consist of multiple frames, and the second temporal resource may consist of multiple time slots or multiple symbols. Another example is that the first temporal resource may include at least one subframe and at least one time slot, while the second temporal resource may include at least one time slot and at least one symbol.

[0223] Optionally, the length of the first time domain resource is predefined, or is indicated by the network device to the terminal device, and the second time domain resource is similar.

[0224] For example, the first time-domain resource can be a time-domain resource used for transmitting a data channel. For instance, the first time-domain resource refers to multiple symbols carrying the PDSCH. The second time-domain resource can be a time-domain resource used for transmitting a control channel. For instance, the second time-domain resource refers to one or more symbols reserved for the physical downlink control channel (PDCCH).

[0225] In one implementation, at least some of the resources in the first temporal resource and the second temporal resource are located in the same frame, the same subframe, or the same time slot.

[0226] For example, when a first temporal resource occupies at least one subframe, and a second temporal resource occupies at least one subframe, at least one time slot, or at least one symbol, at least a portion of the resources in the first temporal resource and the second temporal resource reside in the same frame. When a first temporal resource occupies at least one time slot, and a second temporal resource occupies at least one time slot or at least one symbol, at least a portion of the resources in the first temporal resource and the second temporal resource reside in the same subframe. When a first temporal resource occupies at least one symbol, and a second temporal resource occupies at least one symbol, at least a portion of the resources in the first temporal resource and the second temporal resource reside in the same time slot.

[0227] The following example uses downstream transmission, combined with... Figures 7 to 9 Please provide an explanation.

[0228] Figure 7 This is a schematic flowchart of a communication method 700 provided in this application. Method 700 can be considered as a specific implementation of method 600. Figure 7 As shown, the method includes the following steps.

[0229] S701, the power allocation coefficient A = A* (an example of the first value) of the pilot and data (i.e., preset information and first data) sent by the base station (an example of a network device) to the UE (an example of a terminal device).

[0230] Where, 0≤A * <1. Furthermore, the pilot and data are mapped to the same RE.

[0231] Optionally, as in Example 1, such as Figure 8 As shown, the power allocation coefficient can be used for subframes 1-6 and subframes 9-12 (an example of the first time-domain resource), etc.

[0232] Alternatively, as in Example 2, such as Figure 9 As shown, the power allocation coefficient can be used for symbols 1-2 and 5-17 (another example of the first time-domain resource), etc.

[0233] Among them, Figure 8 In the text, subframes 1-10 belong to the same frame. Figure 9 In the text, symbols 3-16 refer to the same time slot.

[0234] S702, the base station sends information about the AI ​​channel estimation model (an example of an AI model) to the UE.

[0235] Specifically, the base station can pre-train an AI channel estimation model and determine the coefficients A*. This AI channel estimation model and A* are used in scenarios where pilots and data are mapped to the same RE, so the base station can execute S701 and S702.

[0236] It should be understood that S702 is an optional step. For example, if the information of the AI ​​channel estimation model is determined by the UE itself, then method 700 may not include S702.

[0237] S703, the base station transmits pilot signals and data on the same RE based on A=A*, and the UE receives the pilot signals and data.

[0238] In Example 1, the base station can send pilot signals and data to the UE in the REs corresponding to subframes 1-6 and 9-12 with a power allocation factor A = A*.

[0239] In Example 2, the base station can send pilot signals and data to the UE in the REs corresponding to symbols 1-2 and 5-17 with a power allocation factor A = A*.

[0240] S704, the UE performs channel estimation based on the AI ​​channel estimation model (an example of the first channel estimation) and obtains result #1 (an example of the result of the first channel estimation).

[0241] Specifically, the UE can input the signal received in S703 into the AI ​​channel estimation model, and the output of the AI ​​channel estimation model is result #1.

[0242] S705, the base station sends a power allocation coefficient A=1 to the UE (an example of the second value).

[0243] Optionally, as in Example 1, such as Figure 8 As shown, this power allocation coefficient can be used for subframes 7-8 (an example of the second time-domain resource), etc.

[0244] Alternatively, as in Example 2, such as Figure 9 As shown, this power allocation coefficient can be used for symbols 3-4 (another example of the second time-domain resource), etc.

[0245] The power allocation coefficient A = 1 can be used for implicit triggering (i.e. implicit indication) to monitor the model's performance within the second time domain resources.

[0246] Alternatively, when A≠A * When the AI-based channel estimation model fails to output reasonable channel estimation results, meaning the AI ​​channel estimation model is not working properly, the UE can use a non-AI model for channel estimation.

[0247] S706, the base station transmits pilot signals and data on the same RE based on A=1, and the UE receives the pilot signals and data.

[0248] Specifically, in Example 1, the base station can transmit pilot signals and data to the UE in the RE corresponding to subframes 7-8 with a power allocation factor A=1. In Example 2, the base station can transmit pilot signals and data to the UE in the RE corresponding to subframes 3-4 with a power allocation factor A=1.

[0249] S707, the UE performs channel estimation based on a non-AI method (i.e., a non-AI model) (an example of second channel estimation) and obtains result #2 (an example of the result of second channel estimation).

[0250] For example, in a simple single-input single-output (SISO) communication system, the channel estimation process on a single RE resource includes:

[0251] Assuming that on this RE, the data symbol S and the pilot symbol P are superimposed according to the power allocation factor A, then the transmitted symbol X can be expressed as:

[0252]

[0253] The received signal on the UE side after passing through channel H can be expressed as:

[0254]

[0255] Following the traditional (i.e., non-AI model) processing flow, the first step is to perform a coarse channel estimation. For example, the least squares method can be used to obtain the channel estimation result H. est for:

[0256]

[0257] Optionally, after obtaining the channel estimation results, the UE can also reconstruct the pilot and data portions of the received signal. For example, if iterative channel estimation is not considered, the next step is to use the obtained H... est H is obtained after noise reduction / filtering and other operations. est ′, thereby reconstructing the pilot portion Y in the received signal. pilot :

[0258]

[0259] Furthermore, by removing this portion from the received signal, the data portion Y can be obtained. data :

[0260] Y data =YY pilot

[0261] Optionally, the UE can then perform traditional equalization, demodulation, and decoding processes to obtain data.

[0262] S708, the UE compares result #1 and result #2 to determine whether the AI ​​channel estimation model needs to be updated.

[0263] S709, the UE indicates to the base station whether the AI ​​channel estimation model needs to be updated.

[0264] It should be understood that S709 is an optional step. For example, if the information of the AI ​​channel estimation model is determined by the UE itself, the UE does not need to indicate to the base station after determining whether the AI ​​channel estimation model needs to be updated. In this case, method 700 may not include S709.

[0265] In Example 1 above, both the first and second temporal resources include multiple subframes, and since subframes 1-10 belong to the same frame, at least some resources in the first temporal resource and the second temporal resource are located in the same frame. In Example 2 above, both the first and second temporal resources include multiple symbols, and since symbols 3-16 belong to the same time slot, at least some resources in the first temporal resource and the second temporal resource are located in the same time slot.

[0266] Based on the above scheme, this application can use frames or subframes as the monitoring granularity, or time slots or symbols as the monitoring granularity, which has greater flexibility.

[0267] In addition, when monitoring is done at the time slot or symbol level, the performance of AI models can be evaluated more accurately in scenarios where the channel changes rapidly. At the same time, the smaller monitoring granularity can also reduce resource waste.

[0268] It should be understood that the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0269] It should also be understood that, in the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0270] It should also be understood that in some of the above embodiments, the examples are mainly based on devices in existing network architectures (such as network devices, terminal devices, etc.). It should be understood that the specific form of the device is not limited in the embodiments of this application. For example, any device that can achieve the same function in the future is applicable to the embodiments of this application.

[0271] It is understood that, in the above-described method embodiments, the methods and operations implemented by a device (such as a network device or a terminal device) can also be implemented by components of the device (such as a chip or circuit).

[0272] The above, combined with Figures 1 to 7 The communication method provided in the embodiments of this application is described in detail. The above communication method is mainly introduced from the perspective of interaction between terminal devices and network devices. It is understood that, in order to achieve the above functions, the terminal devices and network devices include corresponding hardware structures and / or software modules for performing each function.

[0273] It is understood that, in order to implement the functions in the above embodiments, the terminal device and network device include hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0274] Figure 10 and Figure 11 This is a schematic block diagram of a communication device provided in an embodiment of this application. These communication devices can be used to implement the functions of the terminal device or network device in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments.

[0275] Figure 10 This is a schematic block diagram of the communication device 2000 provided in an embodiment of this application. Figure 10 As shown, the communication device 2000 includes a transceiver unit (or communication unit) 2020. Optionally, the communication device 2000 further includes a processing unit 2010. The communication device 2000 is used to implement the above-mentioned... Figure 6 or Figure 7 The methods illustrated in this embodiment demonstrate the functions of the terminal device or network device.

[0276] When the communication device 2000 is used to achieve Figure 6 or Figure 7 In the illustrated method embodiment, the terminal functions as follows: The transceiver unit 2020 receives first information from a network device. This first information indicates a power allocation coefficient for preset information and / or first data. The power allocation coefficient is used to determine the power of the preset information and / or the first data. The preset information and the first data are carried on the same resource unit. The processing unit 2010 determines the power allocation coefficient based on the first information. Specifically, a first value for the power allocation coefficient corresponds to a first channel estimation based on an AI model, and a second value for the power allocation coefficient corresponds to a second channel estimation based on a non-AI model. The results of the first and second channel estimations are used for model monitoring of the AI ​​model.

[0277] When the communication device 2000 is used to achieve Figure 6 or Figure 7 In the method embodiment shown, the base station functions as follows: the transceiver unit 2020 is used to send power allocation coefficients to the terminal device.

[0278] For a more detailed description of the aforementioned processing unit 2010 and transceiver unit 2020, please refer to [link / reference needed]. Figure 6 The relevant descriptions in the method embodiments shown.

[0279] The apparatus 2000 of each of the above-described schemes has the function of implementing the corresponding steps performed by the terminal device in the above-described method; or, the apparatus 1000 of each of the above-described schemes has the function of implementing the corresponding steps performed by the network device in the above-described method. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions; for example, the transceiver unit can be replaced by a transceiver (e.g., the sending unit in the transceiver unit can be replaced by a transmitter, and the receiving unit in the transceiver unit can be replaced by a receiver), and other units, such as processing units, can be replaced by processors, respectively executing the transceiver operations and related processing operations in each method embodiment.

[0280] Furthermore, the aforementioned transceiver unit can also be a transceiver circuit (e.g., it may include a receiving circuit and a transmitting circuit), and the processing unit can be a processing circuit. The processing circuit can be one or more processors, or all or part of the circuitry within one or more processors used for control or processing functions. In embodiments of this application, Figure 10 The device mentioned can be the terminal device or network device in the foregoing embodiments, or it can be a chip or a chip system, such as a system on a chip (SoC). The transceiver unit can be an input / output circuit or a communication interface; the processing unit is a processor, microprocessor, or integrated circuit integrated on the chip. No limitations are imposed here.

[0281] Figure 11 This is a schematic block diagram of a communication device 3000 provided in an embodiment of this application. The device 3000 includes a processing circuit. The device may also include a communication circuit. The processing circuit and the communication circuit communicate with each other via an internal connection path. The processing circuit executes instructions to control the communication circuit to send and / or receive signals.

[0282] Taking a processing circuit that includes one or more processors and a communication circuit that is a transceiver as an example, such as Figure 11 As shown, the communication device 3000 includes a processor 3010 and a transceiver 3020. The processor 3010 and the transceiver 3020 are coupled to each other. It is understood that the transceiver 3020 can be a transceiver or an input / output interface. Optionally, the communication device 3000 may also include a memory 3030 for storing instructions executed by the processor 3010, or storing input data required by the processor 3010 to execute instructions, or storing data generated after the processor 3010 executes instructions. Sometimes, the transceiver 3020 can also be understood as part of the processor 3010, in which case the communication device 3000 includes the processor 3010.

[0283] In one possible implementation, the apparatus 3000 is used to implement the various processes and steps corresponding to the terminal device in the above method embodiments. In another possible implementation, the apparatus 3000 is used to implement the various processes and steps corresponding to the network device in the above method embodiments.

[0284] It is understood that the device 3000 can specifically be the terminal device or network device in the above embodiments, or it can be a chip or chip system. Correspondingly, the communication circuit can be the interface circuit of the chip, or an input / output circuit, which is not limited here. Specifically, the device 3000 can be used to execute the various steps and / or processes corresponding to the terminal device or network device in the above method embodiments.

[0285] When the communication device 3000 is used to achieve Figure 6 or Figure 7 In the method shown, the processor 3010 is used to implement the functions of the processing unit 2010, and the transceiver 3020 is used to implement the functions of the transceiver unit 2020.

[0286] When the aforementioned communication device is a chip or OTT device applied to a terminal, the terminal chip or OTT device implements the functions of the terminal in the above method embodiments, such as implementing the terminal's processing functions. The terminal chip or OTT device receiving information from a base station can be understood as the information being first received by other modules in the terminal (such as a radio frequency module or antenna), and then sent to the terminal chip or OTT device by these modules. The terminal chip or OTT device sending information to the base station can be understood as the information being first sent by the terminal chip or OTT device to other modules in the terminal (such as a radio frequency module or antenna), and then sent to the base station by these modules.

[0287] When the aforementioned communication device is a chip or OTT device applied to a base station, the base station chip or OTT device implements the functions of the base station in the above method embodiments, for example, implementing the processing functions of the base station. The base station chip or OTT device receiving information from the terminal can be understood as the information being first received by other modules in the base station (such as a radio frequency module or antenna), and then sent to the base station chip or OTT device by these modules. The base station chip or OTT device sending information to the terminal can be understood as the information being first sent by the base station chip or OTT device to other modules in the base station (such as a radio frequency module or antenna), and then sent to the terminal by these modules.

[0288] In this application, entity A sends information to entity B, either directly or indirectly through other entities. Similarly, entity B receives information from entity A, either directly or indirectly through other entities. Entities A and B can be RAN nodes or terminals, or modules within RAN nodes or terminals. Information transmission and reception can be between RAN nodes and terminals, such as between a base station and a terminal; between two RAN nodes, such as between a CU and a DU; or between different modules within a single device, such as between a terminal chip and other modules of the terminal, or between a base station chip and other modules of the base station.

[0289] It is understood that, in order to implement the functions in the above embodiments, the terminal device and network device include hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0290] It should be understood that in the embodiments of this application, the time-domain symbol can be an orthogonal frequency division multiplexing (OFDM) symbol or a discrete fourier transform-spread-of-dm (DFT-s-OFDM) symbol. Unless otherwise specified, the symbols in the embodiments of this application refer to time-domain symbols.

[0291] In this document, PDSCH, PUSCH, PDCCH, PUCCH, PSSCH, PSCCH, and PUSCH are merely examples of downlink data channel, uplink data channel, downlink control channel, uplink control channel, sidelink data channel, sidelink control channel, and uplink data channel, respectively. In different systems and scenarios, data channels and control channels may have different names, and the embodiments of this application do not limit this.

[0292] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), image processors, artificial intelligence processors, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0293] The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a base station or terminal. The processor and storage medium can also exist as discrete components in a base station or terminal.

[0294] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless 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 medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0295] In the above embodiments, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0296] In this document, "at least one" means one or more. "More than one" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates that the related objects before and after are in an "or" relationship; in the formulas of this application, the character " / " indicates that the related objects before and after are in a "division" relationship. "Including at least one of A, B, and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B, and C.

[0297] It should be understood that in the various embodiments of this application, the terms "first," "second," and various numerical designations are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the sequence numbers of the above processes does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

[0298] 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.

[0299] 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.

[0300] In the several embodiments provided in this application, it should be understood that 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0301] 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.

[0302] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0303] 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.

[0304] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A communication method, characterized in that, include: Receive first information from a first device, the first information being used to indicate a power allocation coefficient for preset information and / or first data, the power allocation coefficient being used to determine the power of the preset information and / or the first data, and the preset information and the first data being carried on the same resource unit; The power allocation coefficient is determined based on the first information; wherein... The first value of the power allocation coefficient corresponds to the first channel estimation completed based on the AI ​​model, and the second value of the power allocation coefficient corresponds to the second channel estimation completed based on the non-AI model. The results of the first channel estimation and the second channel estimation are used to monitor the AI ​​model.

2. The method according to claim 1, characterized in that, When the power allocation coefficient is the first value, it is applied to the first time domain resource; when the power allocation coefficient is the second value, it is applied to the second time domain resource. The resource unit is located within the first time domain resource or the second time domain resource. The first time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol. The second time domain resource occupies at least one frame, or at least one subframe, or at least one time slot, or at least one symbol.

3. The method according to claim 2, characterized in that, At least some of the resources in the first temporal resource and the second temporal resource are located in the same frame, the same subframe, or the same time slot.

4. The method according to any one of claims 1 to 3, characterized in that, The power allocation coefficient is the ratio of the transmission power of the preset information or the first data to the total transmission power on the resource unit.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: The monitoring result information is sent to the first device. The monitoring result information is used to determine whether the AI ​​model needs to be updated. The monitoring result information is determined based on the results of the first channel estimation and the second channel estimation.

6. The method according to claim 5, characterized in that, The monitoring result information includes the error or similarity between the results of the first channel estimation and the results of the second channel estimation.

7. The method according to claim 5, characterized in that, The monitoring results include an indication of whether the AI ​​model needs to be updated.

8. The method according to any one of claims 5 to 7, characterized in that, The method further includes: When the power allocation coefficient is the first value, the first channel estimation is completed based on the AI ​​model to obtain the result of the first channel estimation; When the power allocation coefficient is the second value, the second channel estimation is performed based on the non-AI model to obtain the result of the second channel estimation.

9. The method according to any one of claims 1 to 4, characterized in that, The method further includes: When the power allocation coefficient is the first value, the power to transmit the preset information or the first data is determined based on the first value; When the power allocation coefficient is the second value, the power for transmitting the preset information or the first data is determined based on the second value.

10. A communication method, characterized in that, include: Send first information to the second device, the first information being used to indicate a power allocation coefficient for preset information and / or first data, the power allocation coefficient being used to determine the power of the preset information and / or the first data, wherein the preset information and the first data are carried on the same resource unit, wherein... The first value of the power allocation coefficient corresponds to the first channel estimation completed based on the AI ​​model, and the second value of the power allocation coefficient corresponds to the second channel estimation completed based on the non-AI model. The results of the first channel estimation and the second channel estimation are used to monitor the AI ​​model.

11. The method according to claim 10, characterized in that, When the power allocation coefficient is the first value, it is applied to the first time domain resource; when the power allocation coefficient is the second value, it is applied to the second time domain resource. The resource unit is located within the first time domain resource or the second time domain resource. The first time domain resource occupies at least one frame, at least one subframe, at least one time slot, or at least one symbol in the time domain. The second time domain resource occupies at least one frame, at least one subframe, at least one time slot, or at least one symbol.

12. The method according to claim 11, characterized in that, At least some of the resources in the first temporal resource and the second temporal resource are located in the same frame, the same subframe, or the same time slot.

13. The method according to any one of claims 10 to 12, characterized in that, The power allocation coefficient is the ratio of the transmission power of the preset information or the first data to the total transmission power on the resource unit.

14. The method according to any one of claims 10 to 13, characterized in that, The method further includes: The monitoring result information received from the second device is used to determine whether the AI ​​model needs to be updated. The monitoring result information is determined based on the results of the first channel estimation and the second channel estimation.

15. The method according to claim 14, characterized in that, The monitoring result information includes the error or similarity between the first channel estimation result and the second channel estimation result, and the method further includes: Whether the AI ​​model needs to be updated is determined based on the error or similarity.

16. The method according to claim 14, characterized in that, The monitoring results include an indication of whether the AI ​​model needs to be updated.

17. The method according to any one of claims 14 to 16, characterized in that, The method further includes: When the power allocation coefficient is the first value, the power to transmit the preset information or the first data is determined based on the first value; When the power allocation coefficient is the second value, the power for transmitting the preset information or the first data is determined based on the second value.

18. The method according to any one of claims 10 to 13, characterized in that, The method further includes: When the power allocation coefficient is the first value, the first channel estimation is completed based on the AI ​​model to obtain the result of the first channel estimation; When the power allocation coefficient is the second value, the second channel estimation is performed based on the non-AI model to obtain the result of the second channel estimation.

19. The method according to claim 18, characterized in that, The method further includes: Based on the results of the first channel estimation and the second channel estimation, determine whether the AI ​​model needs to be updated.

20. A communication device, characterized in that, It includes modules or units for performing the method as described in any one of claims 1 to 9, or modules or units for performing the method as described in any one of claims 10 to 19.

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