Communication method and communication apparatus

Abstract

The present application provides a communication method and a communication apparatus, which are applied to the field of communications. In the technical solution of the present application, a network device configures, for a terminal device, a measurement occasion of a first beam set and / or a measurement occasion of a second beam set which is a subset of the first beam set, so that the terminal device may know the measurement occasions of the two beam sets, thereby implementing measurement for the two beam sets. Measurement of the second beam set can implement prediction of a beam measurement result while reducing power consumption of both a UE and the network device, and measurement of the first beam set can implement monitoring of AI performance.

Description

Communication methods and communication devices

[0001] This application claims priority to Chinese Patent Application No. 202410949410.4, filed on July 15, 2024, entitled "Communication Method and Communication Device", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of communications, and more particularly to communication methods and communication devices. Background Technology

[0003] In the field of communications, artificial intelligence (AI) has been applied to spatial domain prediction to improve network performance. For example, after a user equipment (UE) obtains measurements of a beam set, the AI ​​capabilities of the UE or network equipment can be used to infer the measurement results of another beam set based on those results, thus saving measurement overhead for the UE. Further research is needed to apply AI to spatial domain prediction. Summary of the Invention

[0004] This application proposes a communication method and communication device to solve at least one of the following problems: high measurement overhead of UE, high power consumption of network equipment, or how to achieve performance monitoring of AI, and has at least one of the following technical effects: saving measurement overhead of UE, reducing or saving power consumption of network equipment, or enabling performance monitoring of AI.

[0005] In a first aspect, this application provides a communication method, the method comprising: receiving first information from a network device, the first information indicating a first timing, the first timing being used to measure a first beam set, the first timing being different from a second timing, the second timing being used to measure a second beam set, the second beam set being a subset of the first beam set, and the measurement result of the second beam set being used to predict the measurement result of the first beam set.

[0006] The method of this application can be executed by a terminal device, or by a processor, processor system, chip, chip system, circuit unit or circuit system applied to a terminal device.

[0007] In this application, because the second beam set is a subset of the first beam set, the measurement overhead of the UE can be reduced compared to the terminal device measuring the first beam set. Furthermore, since the second beam set is a subset of the first beam set, it provides demand support for the network device to transmit a smaller number of beams in the second beam set, thus reducing the network device's energy consumption compared to transmitting the first beam set. Because the first beam set is measured in addition to the second beam set, it provides data support for models that detect measurement results based on the first beam set and predict measurement results based on the second beam set, which is beneficial for performance monitoring of AI models. Since the second beam set is a subset of the first beam set, and the first and second timings are different, the first and second beam sets will not be measured repeatedly within the first timing, thereby saving the UE's measurement power consumption.

[0008] In conjunction with the first aspect, in a first possible implementation, the first timing includes a period and / or an offset. That is, the measurement timing of the first beam set can be configured by configuring the offset of the measurement period and the measurement timing relative to the time unit (e.g., frame, subframe, or time slot) in which the measurement period is located.

[0009] This implementation makes full use of existing time units to indicate the timing of measurements for the first beam set, which helps to reduce transmission overhead and implementation complexity.

[0010] In conjunction with the first aspect or the first possible implementation, in the second possible implementation, the second timing is determined based on the first timing. For example, this method also includes: determining the second timing based on the first timing.

[0011] In combination with the first aspect or any of the aforementioned possible implementations, in the third possible implementation, the first timing includes the duration.

[0012] This implementation can reduce transmission overhead by indicating multiple measurement opportunities for the first beam set through first information.

[0013] In conjunction with the first aspect or any of the aforementioned possible implementations, in a fourth possible implementation, the method further includes: sending first prediction information to a network device, the first prediction information indicating the error between the first prediction result and the first measurement result, the first measurement result including the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment, the first prediction result including the measurement result obtained by the terminal device predicting the first beam set based on the second measurement result, and the second measurement result including the measurement result obtained by the terminal device measuring the second beam set at a first opportune moment.

[0014] In this implementation, the terminal device predicts the measurement result of the first beam set at the first timing based on the measurement result of the second beam set at the first timing, determines the error between the predicted measurement result of the first beam set and the actual measurement result of the first beam set at the first timing, and feeds back the error to the network side so that the network side can monitor the performance of the AI ​​model of the beam measurement results based on the error. Furthermore, this implementation reduces transmission overhead and network load.

[0015] In a fifth possible implementation, in conjunction with the first aspect or any of the first to third possible implementations, the method further includes: sending a first measurement result to the network device, the first measurement result including a measurement result obtained by the terminal device measuring the first beam set at a first opportune moment; sending a first prediction result to the network device, the first prediction result including a measurement result obtained by the terminal device predicting the first beam set based on the second measurement result, the second measurement result including a measurement result obtained by the terminal device measuring the second beam set at a first opportune moment.

[0016] In this implementation, the terminal device predicts the measurement result of the first beam set at the first timing point based on the measurement result of the second beam set at the first timing point, and sends the predicted measurement result and the actual measurement result of the first beam set at the first timing point to the network side. The network side determines the prediction error, so that it can monitor the performance of the AI ​​model of the beam measurement result based on the error. In addition, this implementation can reduce the power consumption of the terminal.

[0017] In a sixth possible implementation, in conjunction with the first aspect or any of the first to fifth possible implementations, the method further includes: sending second prediction information to a network device, the second prediction information instructing a terminal device to predict the measurement result of the first beam set based on a third measurement result, wherein the third measurement result includes the measurement result obtained by the terminal device measuring the second beam set at the second timing.

[0018] In this implementation, the terminal device predicts the measurement result of the first beam set at the second timing based on the measurement result of the second beam set at the second timing, and feeds back the predicted measurement result to the network side. This not only assists the network side in determining the L3 cell measurement result, but also saves network side resources.

[0019] In combination with the first aspect or any of the first to fifth possible implementations, in the seventh possible implementation, the method further includes: sending a third measurement result to the network device, the third measurement result including the measurement result obtained by the terminal device measuring the second beam set at a second time.

[0020] In this implementation, the terminal device sends the measurement results of the second beam set at the second timing to the network side, so that the network side can predict the measurement results of the first beam set at the second timing based on these measurement results, thereby determining the L3 cell measurement results. This implementation can save terminal device resources.

[0021] In an eighth possible implementation, combining the first aspect or any of the first to seventh possible implementations, the method further includes: sending a first measurement result to the network device, the first measurement result including the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment. This allows the network side to know the measurement result of the first beam set at the first opportune moment, thereby enabling it to determine the L3 cell-level measurement result based on the measurement result.

[0022] In the ninth possible implementation, combining the first aspect or any of the first to eighth possible implementations, the measurement period of the first beam set is less than or equal to the AI ​​monitoring period. This ensures that the performance monitoring of the predictive model based on the beam set measurement results can be achieved within each AI monitoring period, thereby improving prediction performance.

[0023] Secondly, this application provides a communication method, which includes: sending first information to a terminal device, the first information indicating a first timing, the first timing being used to measure a first beam set, the first timing being different from a second timing, the second timing being used to measure a second beam set, the second beam set being a subset of the first beam set, and the measurement result of the second beam set being used to predict the measurement result of the first beam set.

[0024] This method can be executed by a network device, or by a processor, processor system, chip, chip system, circuit unit, or circuit system applied in a network device.

[0025] In conjunction with the second aspect, in the first possible implementation, the first timing includes a period and / or a bias.

[0026] In conjunction with the second aspect or the first possible implementation, in the second possible implementation, the first timing includes the duration.

[0027] In conjunction with the second aspect or any of the aforementioned possible implementations, in a third possible implementation, the method further includes: receiving first prediction information from a terminal device, the first prediction information indicating the error between a first prediction result and a first measurement result, the first measurement result including a measurement result obtained by the terminal device measuring a first beam set at a first opportune moment, the first prediction result including a measurement result obtained by the terminal device predicting the first beam set based on a second measurement result, and the second measurement result including a measurement result obtained by the terminal device measuring a second beam set at a first opportune moment.

[0028] In a fourth possible implementation, in conjunction with the second aspect or any of the first to second possible implementations, the method further includes: receiving a first measurement result from a terminal device, the first measurement result including a measurement result obtained by the terminal device measuring the first beam set at a first opportune moment; receiving a first prediction result from a terminal device, the first prediction result including a measurement result obtained by the terminal device predicting the first beam set based on the second measurement result, the second measurement result including a measurement result obtained by the terminal device measuring the second beam set at a first opportune moment.

[0029] In a fifth possible implementation, in conjunction with the second aspect or any of the first to fourth possible implementations, the method further includes: receiving second prediction information from a terminal device, the second prediction information indicating that the terminal device predicts the measurement result of the first beam set based on the third measurement result, the third measurement result including the measurement result obtained by the terminal device measuring the second beam set at a second opportune time.

[0030] In a sixth possible implementation, in combination with the second aspect or any of the first to fourth possible implementations, the method further includes: receiving a third measurement result transmitted from a terminal device, the third measurement result including the measurement result obtained by the terminal device measuring the second beam set at the second timing.

[0031] In a seventh possible implementation, in conjunction with the second aspect or any of the first to sixth possible implementations, the method further includes: receiving a first measurement result from the terminal device, the first measurement result including the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment.

[0032] In combination with the second aspect or any of the first to seventh possible implementations, in the eighth possible implementation, the measurement period of the first beam set is less than or equal to the AI ​​monitoring period.

[0033] The technical effects of the above content can be referred to the technical effects of the corresponding content in the first aspect, and will not be repeated here.

[0034] In a ninth possible implementation, combining the second aspect or any of the first to eighth possible implementations, the method further includes: sending first information to one or more other network devices. This allows other network devices to send the first information to terminal devices within their own cell range, facilitating beam measurement and AI monitoring in neighboring cells.

[0035] In a tenth possible implementation, combining the second aspect or any of the first to ninth possible implementations, this method further includes: receiving first information from other network devices. This implementation can realize the measurement of neighboring cell beams and AI monitoring.

[0036] In conjunction with the second aspect or any of the first to tenth possible implementations, in the eleventh possible implementation, this method further includes: sending at least one of the following information to other network devices: the number of beams in the first beam set, the number of beams in the second beam set, the relationship between beams in the first beam set, the relationship between beams in the second beam set, or antenna array information. This implementation can assist other network devices in managing the beams of this network device.

[0037] In a twelfth possible implementation, combining the second aspect or any of the first to eleventh possible implementations, the method further includes: receiving at least one of the following information from other network devices: AI monitoring cycle indication information, AI function activation indication information, input format of the activated AI function, and output format of the activated AI function. In this implementation, the network device can better implement the AI ​​function with the assistance or control of other network devices.

[0038] Thirdly, this application provides a communication method, which includes: receiving second information from a network device, the second information indicating a second timing, the second timing being used to measure a second beam set, the second timing being different from a first timing, the first timing being used to measure a first beam set, the second beam set being a subset of the first beam set, and the measurement result of the second beam set being used to predict the measurement result of the first beam set.

[0039] The method of this application can be executed by a terminal device, or by a processor, processor system, chip, chip system, circuit unit or circuit system applied to a terminal device.

[0040] In this application, because the second beam set is a subset of the first beam set, the measurement overhead of the UE can be reduced compared to the terminal device measuring the first beam set. Furthermore, since the second beam set is a subset of the first beam set, it provides demand support for the network device to transmit a smaller number of beams in the second beam set, thus reducing the network device's energy consumption compared to transmitting the first beam set. Because the first beam set is measured in addition to the second beam set, it provides data support for models that detect measurement results based on the first beam set and predict measurement results based on the second beam set, which is beneficial for performance monitoring of AI models. Since the second beam set is a subset of the first beam set, and the first and second timings are different, the first and second beam sets will not be measured repeatedly within the first timing, thereby saving the UE's measurement power consumption.

[0041] In conjunction with the third aspect, in the first possible implementation, the second timing includes a period and / or an offset. That is, the measurement timing of the second beam set can be configured by configuring the offset of the measurement period and the measurement timing relative to the time unit (e.g., frame, subframe, or time slot) in which the measurement period is located.

[0042] This implementation makes full use of existing time units to indicate the timing of measurements for the second beam set, which helps to reduce transmission overhead and implementation complexity.

[0043] In conjunction with the third aspect or the first possible implementation, in the second possible implementation, the first timing is determined based on the second timing. For example, this method also includes: determining the first timing based on the second timing.

[0044] In combination with the third aspect or any of the aforementioned possible implementations, in the third possible implementation, the second timing includes the duration.

[0045] This implementation can reduce transmission overhead by indicating multiple measurement opportunities for the first beam set through first information.

[0046] The method of this application may also include any one of the fourth to eighth possible implementations of the first aspect.

[0047] Fourthly, this application provides a communication method, which includes: sending second information to a terminal device, the second information indicating a second timing, the second timing being used to measure a second beam set, the second timing being different from a first timing, the first timing being used to measure a first beam set, the second beam set being a subset of the first beam set, and the measurement result of the second beam set being used to predict the measurement result of the first beam set.

[0048] This method can be executed by a network device, or by a processor, processor system, chip, chip system, circuit unit, or circuit system applied in a network device.

[0049] In conjunction with the fourth aspect, in the first possible implementation, the second timing includes a period and / or a bias.

[0050] In conjunction with the fourth aspect or the first possible implementation, in the second possible implementation, the second timing includes the duration.

[0051] In some implementations, the first timing is determined based on the second timing.

[0052] The technical effects of the fourth aspect or any of its implementation methods can be referenced from the technical effects of the corresponding content in the third aspect.

[0053] The method of this application may also include any one of the third to twelfth possible implementations of the second aspect.

[0054] Fifthly, this application provides a communication method, which includes: receiving first information and second information from a network device, wherein the first information indicates a first timing, the second information indicates a second timing, the first timing is used to measure a first beam set, the second timing is used to measure a second beam set, the second timing is different from the first timing, the second beam set is a subset of the first beam set, and the measurement result of the second beam set is used to predict the measurement result of the first beam set.

[0055] The method of this application can be executed by a terminal device, or by a processor, processor system, chip, chip system, circuit unit or circuit system applied to a terminal device.

[0056] In this application, because the second beam set is a subset of the first beam set, the measurement overhead of the UE can be reduced compared to the terminal device measuring the first beam set. Furthermore, since the second beam set is a subset of the first beam set, it provides demand support for the network device to transmit a smaller number of beams in the second beam set, thus reducing the network device's energy consumption compared to transmitting the first beam set. Because the first beam set is measured in addition to the second beam set, it provides data support for models that detect measurement results based on the first beam set and predict measurement results based on the second beam set, which is beneficial for performance monitoring of AI models. Since the second beam set is a subset of the first beam set, and the first and second timings are different, the first and second beam sets will not be measured repeatedly within the first timing, thereby saving the UE's measurement power consumption.

[0057] In a first possible implementation, the first timing includes a period and / or an offset, and / or the second timing includes a period and / or an offset. That is, the measurement timing of the second beam set can be configured by configuring the offset of the measurement period and measurement timing of the second beam set relative to the time unit (e.g., frame, subframe, or time slot) in which the measurement period is located; and / or, the measurement timing of the first beam set can be configured by configuring the offset of the measurement period and measurement timing of the first beam set relative to the offset of the time unit (e.g., frame, subframe, or time slot) in which the measurement period is located.

[0058] This implementation makes full use of existing time units to indicate the timing of beam set measurements, which helps to reduce transmission overhead and implementation complexity.

[0059] In conjunction with the third aspect or the first possible implementation, in the second possible implementation, the first timing includes the duration, and the second timing includes the duration.

[0060] This implementation can reduce transmission overhead by using the same information to indicate multiple measurement opportunities for the same beam set.

[0061] The method of this application may also include any one of the fourth to eighth possible implementations of the first aspect.

[0062] In a sixth aspect, this application provides a communication method, which includes: sending first information and second information to a terminal device, wherein the first information indicates a first timing, the second information indicates a second timing, the second timing is used to measure a second beam set, the second timing is different from the first timing, the first timing is used to measure a first beam set, the second beam set is a subset of the first beam set, and the measurement result of the second beam set is used to predict the measurement result of the first beam set.

[0063] This method can be executed by a network device, or by a processor, processor system, chip, chip system, circuit unit, or circuit system applied in a network device.

[0064] In a first possible implementation, the first timing includes a period and / or a bias, and / or the second timing includes a period and / or a bias.

[0065] In conjunction with the sixth aspect or the first possible implementation, in the second possible implementation, the first timing includes the duration, and the second timing includes the duration.

[0066] The technical effects of the sixth aspect or any of its implementation methods can be referenced from the technical effects of the corresponding content in the fifth aspect.

[0067] The method of this application may also include any one of the third to twelfth possible implementations of the second aspect.

[0068] In a seventh aspect, this application provides a communication device. This communication device may include modules corresponding to the methods / operations / steps / actions described in the first aspect or any possible implementation thereof, or modules corresponding to the methods / operations / steps / actions described in the third aspect or any possible implementation thereof, or modules corresponding to the methods / operations / steps / actions described in the fifth aspect or any possible implementation thereof. The modules may be hardware circuits, software, or a combination of hardware circuits and software implementation.

[0069] In one design, the device may include a processing module and a communication module. The communication module is used to perform the sending and receiving actions in the method described by the first aspect or any possible implementation thereof, while the processing module is used to perform processing actions in the method described by the first aspect or any possible implementation thereof; or, the communication module is used to perform the sending and receiving actions in the method described by the third aspect or any possible implementation thereof, while the processing module is used to perform processing actions in the method described by the third aspect or any possible implementation thereof; or, the communication module is used to perform the sending and receiving actions in the method described by the fifth aspect or any possible implementation thereof, while the processing module is used to perform processing actions in the method described by the fifth aspect or any possible implementation thereof.

[0070] In one design, the device can be a terminal device, or a device, module, circuit, or chip configured in the terminal device, or a device that can be used in conjunction with the terminal device.

[0071] Eighthly, this application provides a communication device. This communication device may include modules corresponding to the methods / operations / steps / actions described in the second aspect or any possible implementation thereof, or modules corresponding to the methods / operations / steps / actions described in the fourth aspect or any possible implementation thereof, or modules corresponding to the methods / operations / steps / actions described in the sixth aspect or any possible implementation thereof. The modules may be hardware circuits, software, or a combination of hardware circuits and software implementation.

[0072] In one design, the device may include a processing module and a communication module. The communication module is used to perform the sending and receiving actions in the method described in the second aspect or any possible implementation thereof, while the processing module is used to perform processing actions in the method described in the second aspect or any possible implementation thereof; or the communication module is used to perform the sending and receiving actions in the method described in the fourth aspect or any possible implementation thereof, while the processing module is used to perform processing actions in the method described in the fourth aspect or any possible implementation thereof; or the communication module is used to perform the sending and receiving actions in the method described in the sixth aspect or any possible implementation thereof, while the processing module is used to perform processing actions in the method described in the sixth aspect or any possible implementation thereof.

[0073] In one design, the device can be a network device, or a device, module, circuit, or chip configured in the network device, or a device that can be used in conjunction with the network device.

[0074] A ninth aspect provides an apparatus comprising a processor, wherein instructions, when executed by the processor, cause the method of the first aspect or any possible implementation thereof to be implemented, or cause the method of the second aspect or any possible implementation thereof to be implemented, or cause the method of the third aspect or any possible implementation thereof to be implemented, or cause the method of the fourth aspect or any possible implementation thereof to be implemented, or cause the method of the fifth aspect or any possible implementation thereof to be implemented, or cause the method of the sixth aspect or any possible implementation thereof to be implemented.

[0075] Optionally, the device may further include a storage medium that stores the instructions executed by the processor.

[0076] Tenthly, a chip is provided, including processing circuitry, the processing circuitry being configured to execute a program or instructions to cause the method as described in the first aspect or any possible implementation thereof to be implemented, or to cause the method as described in the second aspect or any possible implementation thereof to be implemented, or to cause the method as described in the third aspect or any possible implementation thereof to be implemented, or to cause the method as described in the fourth aspect or any possible implementation thereof to be implemented, or to cause the method as described in the fifth aspect or any possible implementation thereof to be implemented, or to cause the method as described in the sixth aspect or any possible implementation thereof to be implemented.

[0077] Optionally, the chip may further include a memory for storing programs or instructions.

[0078] Optionally, the chip may also include the transceiver circuit, or an input / output interface.

[0079] Eleventhly, a computer-readable storage medium is provided, the computer-readable storage medium comprising instructions that, when executed by a processor, cause the method of the first aspect or any possible implementation thereof to be implemented, or cause the method of the second aspect or any possible implementation thereof to be implemented, or cause the method of the third aspect or any possible implementation thereof to be implemented, or cause the method of the fourth aspect or any possible implementation thereof to be implemented, or cause the method of the fifth aspect or any possible implementation thereof to be implemented, or cause the method of the sixth aspect or any possible implementation thereof to be implemented.

[0080] In a twelfth aspect, a computer program product is provided, the computer program product comprising computer program code or instructions that, when executed, cause the method of the first aspect or any possible implementation thereof to be implemented, or cause the method of the second aspect or any possible implementation thereof to be implemented, or cause the method of the third aspect or any possible implementation thereof to be implemented, or cause the method of the fourth aspect or any possible implementation thereof to be implemented, or cause the method of the fifth aspect or any possible implementation thereof to be implemented, or cause the method of the sixth aspect or any possible implementation thereof to be implemented.

[0081] In a thirteenth aspect, a communication system is provided, comprising: means for performing the first aspect or any possible implementation thereof, and means for performing the second aspect or any possible implementation thereof;

[0082] Or it may include: means for performing the third aspect or any possible implementation of the third aspect, and means for performing the fourth aspect or any possible implementation of the fourth aspect;

[0083] Alternatively, it may include: means for performing the fifth aspect or any possible implementation thereof, and means for performing the sixth aspect or any possible implementation thereof. Attached Figure Description

[0084] Figure 1 is a schematic diagram of a communication system applicable to the communication method of this application embodiment;

[0085] Figure 2 is a schematic diagram of another communication system applicable to the communication method of this application embodiment;

[0086] Figure 3 is a schematic diagram of an application framework in a communication system according to an embodiment of this application;

[0087] Figure 4 is a schematic diagram of the application framework of a communication system according to an embodiment of this application;

[0088] Figure 5 is an exemplary structural diagram of a system according to an embodiment of this application;

[0089] Figure 6 is a schematic diagram of the AI ​​model LCM flow according to an embodiment of this application;

[0090] Figure 7 is an example diagram of the measurement section SSB of one embodiment of this application;

[0091] Figure 8 is a comparative example diagram of beam transmission modes according to an embodiment of this application;

[0092] Figure 9 is an exemplary flowchart of a communication method according to an embodiment of this application;

[0093] Figure 10 is an example diagram of beam measurement timing according to an embodiment of this application;

[0094] Figures 11 to 19 are exemplary flowcharts of communication methods according to various embodiments of this application;

[0095] Figure 20 is an exemplary structural diagram of a communication device according to an embodiment of this application;

[0096] Figure 21 is an exemplary structural diagram of a communication device according to an embodiment of this application. Detailed Implementation

[0097] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0098] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0099] It should be noted that, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0100] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and / or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0101] The technical solution of this application is applicable to wireless communication systems with AI training / inference capabilities, 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 mobile communication systems, or integrated systems of multiple systems, etc.

[0102] 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.

[0103] In a communication system, one network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. The term "network element" can also be replaced by an entity, network entity, device, communication equipment, communication module, node, communication node, etc. This application uses a device as an example. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device.

[0104] 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.

[0105] 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 vehicles, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, 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.

[0106] 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.

[0107] 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.

[0108] The network device in this application embodiment can be a device for communicating with a terminal device. This network device can also be called an access network device or a wireless access network device, such as a base station. In this application embodiment, the network device can 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 equipment in future communication networks, or 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.

[0109] One or more AI modules can be configured in a network device.

[0110] Network equipment 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 that mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0111] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, DU, or CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0112] 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.

[0113] RAN nodes can support one or more types of fronthaul interfaces, each corresponding to a DU and RU 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 another type of interface, relative to CPRI, some downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix addition (CP), are moved from the DU to the RU; and for uplink, digital beamforming (BF), or one or more of fast Fourier transform (FFT) / cyclic prefix removal (CP), are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0114] Taking eCPRI Cat A as an example, for downlink transmission, the DU is configured to implement one or more functions before and after layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions after layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more functions of inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) are moved to the RU. For uplink transmission, the DU is configured to implement one or more functions before and after demapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and demapping), while other functions after demapping (e.g., digital BF or one or more functions of fast Fourier transform (FFT) / removing CP) are moved to the 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, thus requiring increasingly diverse demands. 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, 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 demands, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence (AI) technology can be introduced into wireless communication networks to achieve network intelligence. To support AI technology in wireless networks, AI nodes may also be introduced.

[0120] AI nodes can be deployed in one or more of the following locations within the communication system: network devices, terminal devices, or core network devices, etc. Alternatively, AI nodes can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. AI nodes can communicate with other devices in the communication system, which can be one or more of the following: network devices, terminal devices, or core network elements, etc.

[0121] This application does not limit the number of AI nodes. For example, when there are multiple AI nodes, they can be divided based on function, such as different AI nodes being responsible for different functions.

[0122] AI nodes can be independent devices, integrated into the same device to implement different functions, or they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the AI ​​nodes described above. AI nodes can be AI network elements or AI modules.

[0123] Figure 1 is a schematic diagram of a communication system applicable to the communication method of this application embodiment. As shown in Figure 1, the communication system 100 may include at least one network device, such as network device 110 shown in Figure 1; the communication system 100 may also include at least one terminal device, such as terminal device 120 shown in Figure 1. Network device 110 and terminal device 120 can communicate via a wireless link. The communication devices in this communication system, for example, network device 110 and terminal device 120, can communicate via multi-antenna technology.

[0124] In practical applications, this communication system may include multiple network devices or multiple terminal devices. This application does not limit the number of network devices and terminal devices included in the communication system.

[0125] Figure 2 is a schematic diagram of another communication system applicable to the communication method of this application embodiment. Compared with the communication system 100 shown in Figure 1, the communication system 200 shown in Figure 2 further 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.

[0126] In some implementations, 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 terminal device 120. AI network element 140 can send the results of operations related to the AI ​​model to network device 110, which then forwards them to terminal device 120. 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 terminal device 120. Alternatively, the trained AI model may be deployed on network device 110. Or, the trained AI model may be deployed on terminal device 120.

[0127] It is understood that Figure 2 is only used as an example of the AI ​​network element 140 being directly connected to the network device 110. In other scenarios, the AI ​​network element 140 can also be connected to the terminal device 120. Alternatively, the AI ​​network element 140 can be connected to both the network device 110 and the terminal device 120 simultaneously. Alternatively, the AI ​​network element 140 can also be connected to the network device 110 through a third-party network element. This application embodiment does not limit the connection relationship between the AI ​​network element and other network elements.

[0128] In some implementations, the AI ​​network element 140 can be set as a module in network devices and / or terminal devices, for example, in network device 110 or terminal device 120 shown in Figure 1.

[0129] It should be noted that Figures 1 and 2 are simplified schematic diagrams for ease of understanding only. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, as well as core network devices, which are not shown in Figures 1 and 2.

[0130] Figure 3 is a schematic diagram of an application framework in a communication system according to an embodiment of this application. As shown in Figure 3, network elements in the communication system are connected through interfaces (e.g., NG, Xn) or air interfaces. These network element nodes, such as core network equipment, access network node (RAN node) network equipment, terminals, or one or more devices in operation administration and maintenance (OAM), are equipped with one or more AI modules (only one is shown in Figure 3 for clarity).

[0131] Network devices can function as a single RAN node or comprise multiple RAN nodes, such as CUs and DUs. The CU and / or DU can also be configured with one or more AI modules. Optionally, the CU can be further divided into CU-CP and CU-UP. One or more AI models are configured within the CU-CP and / or CU-UP.

[0132] AI modules are 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 biases in the activation function), input parameters (e.g., the type and / or dimension of the input parameters), or output parameters (e.g., the type and / or dimension of the output parameters). The biases in the activation function can also be referred to as the neural network biases.

[0133] 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.

[0134] Figure 4 is a schematic diagram of the application framework of a communication system according to an embodiment of this application. As shown in Figure 4, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​module shown in Figure 3, used to implement AI-related functions.

[0135] RICs include near-real-time RICs (near-RT RICs) and non-real-time RICs (non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency on the order of seconds. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency on the order of tens of milliseconds.

[0136] Near real-time (NRT) RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. NRT RICs can obtain network-side and / or terminal-side information from network devices (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data. Optionally, the NRT RIC can deliver inference results to network devices and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the NRT RIC delivers inference results to the DU, and the DU sends them to the RU.

[0137] Non-real-time RICs are also used for model training and inference. For example, they can be used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from network devices (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to the network devices and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers the inference result to a DU, which then forwards it to an RU.

[0138] For example, near real-time RICs are set up in network devices (e.g., CU, DU), while non-real-time RICs are set up in OAM, cloud servers, core network devices, or other network devices. RICs can be trained by obtaining subsets from multiple end devices from network devices (e.g., CU, CU-CP, CU-UP, DU, and / or RU), recombining them into a training dataset #2, and training on the training dataset #2.

[0139] For example, near real-time RIC and non-real-time RIC can also be set up separately as a network element, and the network device can be a near real-time RIC or a non-real-time RIC.

[0140] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Optionally, near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be set in network devices (e.g., CU, DU), while non-real-time RICs can be set in OAM, cloud servers, core network devices, or other network devices.

[0141] Figure 5 is an exemplary structural diagram of a system according to an embodiment of this application. This system may include a data collection device, a model training device, a model storage device, a model inference device, and a model management device.

[0142] The system comprises the following components: a data collection device that stores data input from gNB, gNB-CU, gNB-DU, UE, or other devices, serving as a database for AI model training and data analysis inference; a model training device that analyzes the training data provided by the data collection device to generate the optimal AI model; a model storage device that stores the trained or updated model and, based on the model management device's control of model transmission or distribution requests, transmits or distributes the model to the model inference device; and a model inference device that, under the management of selection, activation, switching, or feedback instructions issued by the model management device, uses the AI ​​model and, based on the inference data provided by the data collection device, provides reasonable AI-based predictions of network operation and / or guides the network to make policy adjustments. Finally, a model management device that can send performance feedback or retraining requests to the model training device based on management data collected by the data collection device and model data sent by the model inference device.

[0143] In some implementations, the data collection device can be a gNB, gNB-CU, gNB-DU, UE, or other device; the model inference device can be a gNB, gNB-CU, gNB-DU, UE, or other device; and the model management device and model training device can be OAM devices.

[0144] Figure 6 is a schematic diagram of an AI model lifecycle management (LCM) process according to an embodiment of this application. This embodiment includes steps S610, S620, S630, and S640.

[0145] S610, the terminal device reports capabilities and / or auxiliary information to the network device.

[0146] Among them, capability information indicates the features supported by the UE, such as the use cases supported by the UE and the AI ​​functions supported by the UE; auxiliary information indicates the AI / machine learning (ML) models supported by the UE and related information about AI / ML functions, such as the conditions under which the model / function is applicable / suitable, or whether the model / function is applicable in the current scenario.

[0147] It should be noted that the following content of this application will use AI as an example for introduction, and AI in the following content can be replaced with ML.

[0148] S620: Network devices send management commands to terminal devices.

[0149] For example, network devices make decisions based on information reported by the UE and send management commands to the UE, such as activation, deactivation, handover, and rollback commands.

[0150] For example, if the information reported by the UE indicates that it has an applicable model or function, the network device sends a model activation command or function activation command to the UE.

[0151] S630: The terminal device reports relevant information based on the measurement results to the network device.

[0152] For example, terminal devices can report actual measurement results, predicted measurement results, or monitored performance. This information can be used to assist the network side in measurement management, mobility management, cell handover management, or monitoring AI performance.

[0153] S640: Network devices send management commands to terminal devices.

[0154] For example, network devices perform AI monitoring based on performance or test results reported by the UE, thereby enabling subsequent management. For instance, if the NW detects that the current UE's model / function performance is too poor, it sends a model / function deactivation or switching command to the UE.

[0155] In the field of communications, AI has been applied to spatial domain prediction to improve network performance. For example, after a user equipment (UE) measures a beam set, the AI ​​capabilities of the UE or network equipment can be used to infer the measurement results of another beam set based on those results, thus saving measurement overhead for the UE. Further research is needed to apply AI to spatial domain prediction.

[0156] Spatial prediction is accomplished by using a subset of the measured beam configuration as input to the model to predict L3 cell-level measurement results for the same cell.

[0157] For example, the full set of SSB measurement results can be predicted by measuring a subset of the synchronization signal block (SSB), and then the L3 cell-level measurement results can be derived; or the L3 cell-level measurement results can be directly predicted by measuring a subset of the SSB.

[0158] For example, the network device configures the UE with the number of beams N for averaging and an absolute threshold "Threshold" for merging SSB measurement results, where N is a positive integer. The UE measures M SSBs, sorts the measurement results of these M SSBs in descending order of quality, takes the top N measurement results above the "Threshold" and calculates their average, which is used as the L1 cell measurement result. If the number of measurement results above the "Threshold" is less than N, the average of these SSB measurement results is calculated, and this average is used as the L1 cell measurement result. If no SSB measurement result is above the "Threshold," the measurement result of the SSB with the highest measurement result is used as the L1 cell measurement result. This L1 cell measurement result is then filtered to obtain the L3 cell measurement result. This process can be called cell-level measurement, such as inter-cell beam-level L3 mobility measurement. The cell measurement results can be used for UE mobility management.

[0159] Currently, for SSB measurement, base stations exchange configuration information, indicating the SSB frequency, SS / PBCH block measurement time configuration (SMTC), and the SSBs to be measured. The serving base station configures the UE with the SSB frequency, SMTC measurement duration, the SSBs to be measured, and reporting configurations, including periodic reporting and event-triggered reporting. Accordingly, the UE measures the SSBs indicated by the base station within each SMTC measurement duration and reports the measurement results to the base station according to the reporting configuration. The SSBs to be measured indicated by the base station are the aforementioned M SSBs, which are referred to as all SSBs.

[0160] In this process, the base station transmits all SSBs during each SMTC measurement duration, and the UE measures all SSBs during each SMTC measurement duration.

[0161] In the spatial domain prediction scenario, the following technical solution is proposed: the UE only measures a portion of the SSBs to infer the measurement results of all SSBs, thereby saving the UE's measurement overhead. Here, "all SSBs" can be understood as the aforementioned M SSBs, and "a portion of the SSBs" can be understood as a subset of the M SSBs. In this case, the base station transmits all SSBs, while the UE measures a subset of the SSBs.

[0162] Figure 7 is an example diagram of the measurement section SSB according to an embodiment of this application. In Figure 7, the dashed lines represent beams that are not measured, and the dashed lines represent beams that are measured.

[0163] As shown in Figure 7, for cell a, the UE only needs to measure two beams a1 and a3, and then predict the measurement results of a2 and a4 to obtain the measurement results of four beams; for cell b, the UE only needs to measure two beams b1 and b3, and then predict the measurement results of b2 and b4 to obtain the measurement results of four beams.

[0164] Research and analysis revealed that, considering the AI ​​capabilities of UEs in future mobile communication systems, base stations can save energy by transmitting sparse SSBs. In other words, base stations can transmit only a portion of the beams; for example, if there are M beams in total, the base station can transmit only a portion of those M beams.

[0165] In this scenario, considering AI performance monitoring, the base station still needs to transmit all SSBs during certain SMTC measurement durations so that the UE can detect all beams and thus monitor AI performance. Therefore, the base station's transmission mode can become: transmitting all SSBs during some SMTC measurement durations and transmitting partial SSBs during other SMTC measurement durations.

[0166] Figure 8 is a comparative example diagram of beam transmission modes according to an embodiment of this application. In Figure 8, dashed lines indicate no beam, while solid lines indicate that a beam has been transmitted. Set B represents a set of some SSBs, and set A represents a set of all SSBs. In the first mode, the base station transmits set B during each SMTC measurement duration; in the second mode, the base station transmits set B during the two SMTC measurement durations on the left and transmits set A during the SMTC measurement duration on the right.

[0167] In the second mode, for the two SMTC measurement durations on the left, the measurement result of setB within each SMTC measurement duration can be predicted to obtain the corresponding measurement result of setA, which is used to acquire L3 cell-level measurement results. For the SMTC measurement duration on the right, one implementation is to use the measured result of setA to acquire L3 cell-level measurement results; another implementation is to obtain the measured result of setB from the measured result of setA and predict the measurement result of setA based on the measured result of setB, with the predicted result of setA used to acquire L3 cell-level measurement results. Furthermore, the measured result and predicted result of setA for the SMTC measurement duration on the right can be used to monitor the performance of the AI ​​model for beam measurement result prediction.

[0168] However, in existing technologies, the measurement configuration sent by the base station to the UE cannot indicate the second mode of the SSB mentioned above, thus failing to meet the energy-saving requirements of the base station and the performance monitoring of AI. To address these issues, this application proposes a new technical solution.

[0169] Figure 9 is an exemplary flowchart of a communication method according to an embodiment of this application. The embodiment shown in Figure 9 is described using a terminal device and a network device as examples of the execution subjects. It can be understood that the terminal device in the method shown in Figure 9 can be replaced by a processor, processor system, chip, chip system, circuit unit, or circuit system applied to the terminal device, and the network device in the method shown in Figure 9 can be replaced by a processor, processor system, chip, chip system, circuit unit, or circuit system applied to the network device.

[0170] S910, the network device sends first information to the terminal device. The first information indicates a first timing, which is used to measure a first beam set. The first timing is different from a second timing. The second timing is used to measure a second beam set, which is a subset of the first beam set. The measurement result of the second beam set is used to predict the measurement result of the first beam set. Accordingly, the terminal device receives the first information.

[0171] In this embodiment, the first beam set can be denoted as setA, and the second beam set can be denoted as setB.

[0172] In this embodiment, in some implementations, the timing of beam set measurement can be replaced by the timing of beam set transmission. That is, the transmission timing is consistent with the measurement timing, or the beam set is measured only when the beam set is transmitted, or the beam set is transmitted only when the beam set is measured.

[0173] In this embodiment, the network device can send the first information via radio resource control (RRC) signaling.

[0174] In this embodiment, the first timing is used to measure the first beam set, which can be understood as: the first timing is the timing for measuring the first beam set; the second timing is used to measure the second beam set, which can be understood as: the second timing is the timing for measuring the second beam set.

[0175] In this embodiment, the measurement results of the second beam set are used to predict the measurement results of the first beam set. This can be understood as: predicting the measurement results of the first beam set based on the measurement results of the second beam set.

[0176] When predicting the measurement results of the first beam set based on the measurement results of the second beam set, the measurement results of the first beam set can be predicted using AI capabilities or AI models based on the measurement results of the second beam set.

[0177] The number of measurement results in the first beam set is denoted as M, where M is a positive integer. After predicting the M measurement results of the first beam set, in some implementations, these M measurement results are sorted in descending order of quality. Among these M measurement results that are higher than the absolute threshold "Threshold" configured by the network device, the top N measurement results are taken and their average value is calculated. This average value is used as the L1 cell measurement result. If the number of measurement results higher than "Threshold" among these M measurement results is less than N, then the average value of these measurement results that are higher than "Threshold" is calculated, and this average value is used as the L1 cell measurement result. If no measurement result among these M measurement results is higher than "Threshold", then the highest measurement result among these M measurement results is taken as the L1 cell measurement result. The L1 cell measurement result is then filtered to obtain the L3 cell measurement result. Based on the L3 cell measurement result, mobility management or cell handover management of terminal devices is performed.

[0178] In this embodiment, the beam may include an SSB beam or a channel-state information reference signal (CSI-RS) beam.

[0179] In this embodiment, the measurement result may include the reference signal receiving power (RSRP).

[0180] In some implementations, the first timing includes a period and / or an offset. The period here can be understood as the period of the measurement timing of the first beam set, and the offset is used to determine the start time of the measurement timing.

[0181] In some implementations, the period and / or offset can be characterized by measurement windows. In this implementation, the period refers to the number of measurement windows contained in the measurement period of the first beam set; the offset refers to the offset of the first measurement window of the first beam set relative to the first measurement window within the measurement period.

[0182] In some implementations, the measurement window can be understood as the measurement period of the beam. For example, when the beam is an SSB, the measurement window is the SSB measurement period. As an example, the SSB measurement period can be the SMTC measurement duration.

[0183] Taking the measurement window as the SMTC measurement duration as an example, a period of 3 SMTC measurement durations and an offset of 2 SMTC measurement durations means that the measurement period of the first beam set is 3 SMTC measurement durations and the offset is 2 SMTC measurement durations. That is, the first beam set is remeasured every 3 SMTC measurement durations, and the first beam set is measured within the third SMTC measurement duration of these 3 SMTC measurement durations.

[0184] As an example, the first information can include the following information: setA: periodicityAndOffsetFullset{smtc3 INTEGER(0 / 1 / 2)}, where "periodicityAndOffsetFullset" indicates that the following information is the measurement timing information of setA, "smtc3" indicates that the measurement period is 3 SMTC measurement durations, and "INTEGER(0 / 1 / 2)" indicates the value of the offset, which can be one of 0, 1, and 2.

[0185] In this embodiment, the second timing can be determined based on the first timing. The relationship between the first and second timings can be pre-defined.

[0186] An example of the relationship between the first timing and the second timing includes: during the measurement period of the first beam set, the measurement timings of the other beams, except for the measurement timing of the first beam set, can all be used to measure the second beam set.

[0187] As an example, the measurement period of the first beam set is 3 SMTC measurement durations. When the offset is 2 SMTC measurement durations, the first beam set is measured during the third SMTC measurement duration of these 3 SMTC measurement durations, and the second beam set is measured during the first SMTC measurement duration and the second SMTC measurement duration.

[0188] Figure 10 is an example diagram of beam measurement timing according to an embodiment of this application. In Figure 10, dashed beams represent beams that are not transmitted or measured, and solid beams represent beams that are transmitted and measured. In this embodiment, the period of the first beam set is 3 SMTC measurement durations, and the offset is 2 SMTC measurement durations.

[0189] As shown in Figure 10, during the two SMTC measurement durations represented by the solid boxes, the network device sends the second beam set, and the terminal device measures the second beam set; during the SMTC measurement durations represented by the dashed boxes, the network device sends the first beam set, and the terminal device measures the first beam set.

[0190] In some implementations of this embodiment, the first timing includes a duration.

[0191] As an example, this duration can be characterized by the number of measurement windows, which can represent the number of measurement windows that measure the first beam set within one cycle.

[0192] Taking the measurement period of the first beam set as 5 SMTC measurement durations and the offset as 0 SMTC measurement durations as an example, if the duration is 2 SMTC measurement durations, it can be said that in these 5 SMTC measurement durations, the first beam set is measured in each SMTC measurement duration within the 2 SMTC measurement durations starting from the first SMTC measurement duration.

[0193] In some implementations of this embodiment, the first information also includes information about the first beam set, which is used to indicate which beams the first beam set contains.

[0194] One way to implement the information of the first beam set is as follows: the information of the first beam set can be a list, which includes the index of the beam.

[0195] One way to implement the information of the first beam set is as follows: The information of the first beam set can be a binary bit sequence, where each bit is 0 to indicate that the corresponding beam does not need to be measured, and 1 to indicate that the corresponding beam needs to be measured. The length of the bit sequence can be equal to the number of beams in the cell's beam set, and multiple bits in the bit sequence correspond one-to-one with multiple beams in the cell's beam set.

[0196] In some implementations of this embodiment, the first information also includes information about the second beam set, which is used to indicate which beams the second beam set contains.

[0197] One way to implement the information of the second beam set is as follows: the information of the second beam set can be a list, which includes the index of the beam.

[0198] One way to implement the information of the second beam set is as follows: The information of the second beam set can be a binary bit sequence, where each bit is 0 to indicate that the corresponding beam does not need to be measured, and 1 to indicate that the corresponding beam needs to be measured. The length of the bit sequence can be equal to the number of beams in the first beam set, and multiple bits in the bit sequence correspond one-to-one with multiple beams in the first beam set.

[0199] In some implementations of this embodiment, the first information may further include: beam frequency, and / or, time unit.

[0200] Taking the SSB beam as an example in this embodiment, and the measurement period of the first beam set as three SMTC measurement durations, in some implementations, the first information may include the following:

[0201] {Existing configuration {SSB frequency point, SMTC, ssb-ToMeasure, etc.}}

[0202] SetB information: ssb-ToMeasuresubset subsetInfo,

[0203] setA period: periodicityAndOffsetFullset{smtc3 INTEGER(0 / 1 / 2)}.

[0204] Here, subsetInfo represents the information of setB.

[0205] In this embodiment, after receiving the first information, the terminal device measures the first beam set at a first opportune moment and measures the second beam set at a second opportune moment, as instructed by the first information.

[0206] Taking Figure 10 as an example, the terminal device measures setB during the SMTC measurement duration corresponding to the implementation box, and measures setB during the SMTC measurement duration corresponding to the dashed box.

[0207] In some implementations of this embodiment, the measurement period of the first beam set is less than or equal to the AI ​​monitoring period.

[0208] S920, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0209] The prediction results of the first beam set include: the prediction results of the first beam set at the first timing, and / or, the prediction results of the first beam at the second timing.

[0210] The prediction results of the first beam set at the first timing include the measurement results predicted based on the measurement results of the second beam set at the first timing; the prediction results of the first beam set at the second timing include the measurement results predicted based on the measurement results of the second beam set at the second timing.

[0211] Since the second beam set is a subset of the first beam set, the measurement results of the second beam set at the first timing can be obtained from the measurement results of the first beam set at the first timing.

[0212] In some implementations of this embodiment, the information that the terminal device sends to the network device in this step depends on whether the model used to predict the measurement results is a UE-side model or a network-side model. The model used to predict the measurement results can be called a prediction model.

[0213] If the prediction model is a UE-side model, the operation of predicting the measurement results based on the actual measurement results can be performed on the terminal device, and the terminal device reports the predicted measurement results and / or related information obtained based on the predicted measurement results; if the prediction model is a network-side model or the UE-side AI function is not activated, the terminal device can send the actual measurement results to the network device, and the network device will perform the prediction operation and subsequent operations.

[0214] When the prediction model is a UE-side model, in some implementations, for the measurement results used for AI performance monitoring, the terminal device sends a first measurement result and a first prediction result to the network device. The first measurement result includes the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment. The first prediction result includes the measurement result obtained by the terminal device predicting the first beam set based on the second measurement result. The second measurement result includes the measurement result obtained by the terminal device measuring the second beam set at the first opportune moment. The second measurement result can also be understood as the measurement result of the second beam set at the first opportune moment.

[0215] In this implementation, after the network device receives the first measurement result and the first prediction result, it can calculate the difference between the first measurement result and the first prediction result as the error of the first prediction result relative to the first measurement result. This error can be used to monitor the performance of the measurement result prediction model, update the prediction model, and improve the performance of the prediction model.

[0216] When the prediction model is a UE-side model, in some implementations, for the measurement results used for AI performance monitoring, the terminal device sends first prediction information to the network device. The first prediction information indicates the error between the first prediction result and the first measurement result. The first measurement result includes the measurement result obtained by the terminal device measuring the first beam set at the first time. The first prediction result includes the measurement result obtained by the terminal device predicting the first beam set based on the second measurement result. The second measurement result includes the measurement result obtained by the terminal device measuring the second beam set at the first time.

[0217] The difference between this implementation and the previous one is that in the previous implementation, the network device calculates the error between the first prediction result and the first measurement result, while in this implementation, the terminal device calculates the error between the first prediction result and the first measurement result and then reports it to the network device.

[0218] In some implementations, the terminal device can record multiple errors (e.g., multiple errors corresponding to multiple dashed boxes in 10), and put these multiple errors in a list for reporting.

[0219] In some implementations, the terminal device can calculate the average of these multiple errors and report the average.

[0220] Taking RSRP as an example, in some implementations, the cell RSRP obtained by the UE based on the measurement result of the first beam set at the first timing is RSRP#1. After the measurement result of the first beam set is predicted based on the measurement result of the second beam set at the first timing, the cell RSRP calculated based on the predicted measurement result of the first beam set is RSRP#2. Then the UE records the prediction error as |RSRP#1-RSRP#2|.

[0221] In other implementations, the L3 cell measurement result is obtained based on the actual measurement result of the first beam set at the first timing point, and this L3 cell measurement result is denoted as RSRP#1; after predicting the measurement result of the first beam set based on the measurement result of the second beam set at the first timing point, the L3 cell measurement result is obtained based on the predicted measurement result of the first beam set, and this L3 cell measurement result is denoted as RSRP#2; the error between RSRP#1 and RSRP#2, |RSRP#1-RSRP#2|, is calculated as the prediction error.

[0222] Before the terminal reports the prediction error to the network device, the UE can record multiple prediction errors. When reporting, the UE can put these multiple errors in a list and report them; or it can calculate the average of these multiple errors and then report the average.

[0223] When the prediction model is a UE-side model, for prediction results used for network management, such as measurement results used for mobility management or cell handover management, in some implementations, the terminal device sends second prediction information to the network device. The second prediction information instructs the terminal device to predict the measurement results of the first beam set based on the third measurement results. The third measurement results include the measurement results obtained by the terminal device measuring the second beam set at a second time.

[0224] In this implementation, after receiving the second prediction information, the network device can determine the L3 cell-level measurement results based on the measurement results indicated by the second prediction information, so as to perform mobility management or cell handover management, etc.

[0225] When the prediction model is a network-side model, in some implementations, for measurement results used for AI performance monitoring, the terminal device sends a first measurement result to the network device. This first measurement result includes the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment. Upon receiving the first prediction result, the network device can obtain the opportune moment measurement result of the second beam set at the first opportune moment from the first prediction result. Then, based on the opportune moment measurement result of the second beam set at the first opportune moment, the network device predicts the measurement result of the first beam set at the first opportune moment using the prediction model. Furthermore, it calculates the error between the predicted measurement result and the actual measurement result of the first beam set at the first opportune moment, and performs subsequent operations accordingly.

[0226] When the prediction model is a network-side model, for the measurement results used for AI performance monitoring, in some implementations, the terminal device sends a first measurement result and the measurement result of the second beam set within the first measurement result to the network device. The first measurement result includes the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment. The difference between this implementation and the previous one is that in the previous implementation, the network device needs to obtain the measurement result of the second beam set at the first opportune moment from the first prediction result, while in this implementation, the network device directly obtains the measurement result of the second beam set at the first opportune moment from the terminal device.

[0227] When the prediction model is a UE-side model, in some implementations, for the prediction results used for network management, the terminal device sends a third measurement result to the network device. The third measurement result includes the measurement result obtained by the terminal device measuring the second beam set at a second opportune time. In this way, after receiving the third measurement result, the network device can predict the measurement result of the first beam set at the second opportune time based on the third measurement result using the prediction model, and determine the L3 cell-level measurement result based on the predicted measurement result of the first beam set at the second opportune time for mobility management or cell handover management, etc.

[0228] In some implementations, regardless of whether the prediction model is a UE-side model or a network-side model, for the prediction results used for network management, the terminal device sends a first measurement result to the network device. The first measurement result includes the measurement result obtained by the terminal device measuring the first beam set at a first opportune moment. In this way, the network device can determine the L3 cell-level measurement result based on the first measurement result for mobility management or cell handover management, etc.

[0229] In some implementations, the terminal device has already entered the connected state and activated the AI ​​function before executing S910. In this implementation, if the terminal device enters the disconnected state, it performs actual measurements and predictions according to the configuration of the first information, records the obtained actual measurement and / or prediction results, and reports them to the base station the next time it enters the connected state.

[0230] If the network device does not configure the non-connected state measurement configuration for the UE before the terminal device enters the non-connected state, the terminal device can perform blind detection on all beams according to the smallest time unit, that is, measure all beams in each time unit, and report the measurement results to the base station when it enters the connected state again.

[0231] Figure 11 is an exemplary flowchart of a communication method according to an embodiment of this application. One difference between the method shown in Figure 11 and the method shown in Figure 10 is that the method shown in Figure 11 describes the relationship between the step of exchanging first information between the network device and the terminal device and other steps.

[0232] S1110, the terminal device reports capability and / or auxiliary information to the network device. Accordingly, the network device receives the capability and / or auxiliary information.

[0233] S1120, the network device sends a management command to the terminal device. Correspondingly, the terminal device receives the management command.

[0234] S1130, the network device sends the first information to the terminal device. Correspondingly, the terminal device receives the first information.

[0235] S1140, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0236] In this embodiment, S1110 and S1120 can be referred to as S610 and S620 respectively, and S1130 and S1140 can be referred to as S910 and S920 respectively, which will not be described again here.

[0237] Some applicable scenarios for S1110 and S1120 include: the terminal side has all or part of the AI ​​capabilities, or in other words, it has a UE-side AI model. If the terminal side has no AI capabilities at all, then S1110 and S1120 do not need to be executed.

[0238] Figure 12 is an exemplary flowchart of a communication method according to an embodiment of this application. One difference between the method shown in Figure 12 and the method shown in Figure 10 is that the method shown in Figure 12 further includes the exchange of first information between network devices, so that other network devices can send the first information to the terminal devices served by other network devices, thereby enabling the terminal devices to measure the beams of neighboring cells, and thus improving the network management performance, such as improving mobility management performance and cell handover management performance.

[0239] S1210, the first network device sends first information to the second network device. Correspondingly, the second network device receives the first information.

[0240] In this step, the method by which the first network device sends the first information to the second network device can be referenced from the method in S910 for the network device to send the first information to the terminal device, and will not be repeated here. For example, the network device in S910 can be replaced with the first network device, and the terminal device can be replaced with the second network device.

[0241] S1220, the first network device sends first information to the terminal device. Correspondingly, the terminal device receives the first information.

[0242] S1230, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0243] In this embodiment, S1220 and S1230 can refer to S910 and S920, for example, the network device in the method shown in FIG9 can be replaced with the first network device, which will not be described in detail here.

[0244] Optionally, in this embodiment, S1212 and S1214 may be included before S1220.

[0245] S1212, the terminal device reports capability and / or auxiliary information to the network device. Accordingly, the network device receives the capability and / or auxiliary information.

[0246] S1214, the network device sends a management command to the terminal device. Correspondingly, the terminal device receives the management command.

[0247] In this embodiment, S1212 and S1214 can be referred to as S610 and S620 respectively, and will not be described again here.

[0248] Figure 13 is an exemplary flowchart of a communication method according to an embodiment of this application. The method shown in Figure 13 differs from the method shown in Figure 9 in that: in the method shown in Figure 9, the network device configures a first timing for the terminal device, that is, configures the measurement timing for the first beam set; while in the method shown in Figure 13, the network device configures a second timing for the terminal device, that is, configures the measurement timing for the first beam set.

[0249] S1310, the network device sends second information to the terminal device. The second information indicates a second timing, which is used to measure a second beam set. The second timing is different from the first timing. The first timing is used to measure a first beam set. The second beam set is a subset of the first beam set. The measurement result of the second beam set is used to predict the measurement result of the first beam set. Accordingly, the terminal device receives the second information.

[0250] In this embodiment, S1310 can refer to S910, but the difference is that in S910, the network device configures first information for the terminal device to indicate a first timing, and the terminal device determines a second timing based on the first timing; while in this embodiment, the network device configures second information for the terminal device to indicate a second timing, and determines a first timing based on the second timing.

[0251] The implementation method of the second information indicating the second timing can refer to the relevant content of the first information indicating the first timing, and the implementation method of determining the first timing based on the second timing can refer to the implementation method of determining the second timing based on the first timing.

[0252] S1320, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0253] In this embodiment, S1320 can be referred to as S920, and will not be described again here.

[0254] The technical effects of this embodiment can be referred to the technical effects of the embodiment shown in Figure 9, and will not be repeated here.

[0255] Figure 14 is an exemplary flowchart of a communication method according to an embodiment of this application. One difference between the method shown in Figure 14 and the method shown in Figure 13 is that the method shown in Figure 14 describes the relationship between the step of exchanging first information between the network device and the terminal device and other steps.

[0256] S1410, the terminal device reports capability and / or auxiliary information to the network device. Accordingly, the network device receives the capability and / or auxiliary information.

[0257] S1420, the network device sends a management command to the terminal device. Correspondingly, the terminal device receives the management command.

[0258] S1430, the network device sends the second information to the terminal device. Accordingly, the terminal device receives the second information.

[0259] S1440, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0260] In this embodiment, S1410 and S1420 can be referred to as S610 and S620 respectively, and S1430 and S1440 can be referred to as S1310 and S1320 respectively, which will not be described again here.

[0261] Figure 15 is an exemplary flowchart of a communication method according to an embodiment of this application. One difference between the method shown in Figure 15 and the method shown in Figure 13 is that the method shown in Figure 15 further includes the exchange of second information between network devices, so that other network devices can send second information to terminal devices served by other network devices, thereby enabling terminal devices to measure the beam of neighboring cells, and thus improving network management performance, such as improving mobility management performance, cell handover management performance, etc.

[0262] S1510, the first network device sends second information to the second network device. Correspondingly, the second network device receives the second information.

[0263] In this step, the method by which the first network device sends the second information to the second network device can be referenced from the method in S1310 for the network device to send the second information to the terminal device, and will not be repeated here. For example, the network device in S1310 can be replaced with the first network device, and the terminal device can be replaced with the second network device.

[0264] S1520, the first network device sends the second information to the terminal device. Accordingly, the terminal device receives the second information.

[0265] S1530, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0266] In this embodiment, S1520 and S1530 can refer to S1310 and S1320, for example, replacing the network device in the method shown in FIG13 with the first network device, which will not be described in detail here.

[0267] Optionally, in this embodiment, S1512 and S1514 may be included before S1520.

[0268] S1512, the terminal device reports capability and / or auxiliary information to the network device. Accordingly, the network device receives the capability and / or auxiliary information.

[0269] S1514, the network device sends a management command to the terminal device. The terminal device then receives the management command.

[0270] In this embodiment, S1512 and S1514 can be referred to as S610 and S620 respectively, and will not be described again here.

[0271] Figure 16 is an exemplary flowchart of a communication method according to an embodiment of this application. The method shown in Figure 16 differs from the method shown in Figure 9 in that: in the method shown in Figure 9, the network device configures a first timing for the terminal device, that is, configures a measurement timing for the first beam set; while in the method shown in Figure 16, the network device configures a first timing and a second timing for the terminal device, that is, configures a measurement timing for the first beam set and a measurement timing for the second beam set.

[0272] S1610, the network device sends first information and second information to the terminal device. The first information indicates a first timing, and the second information indicates a second timing. The second timing is used to measure a second beam set. The first timing is different from the second timing. The first timing is used to measure a first beam set, and the second beam set is a subset of the first beam set. The measurement result of the second beam set is used to predict the measurement result of the first beam set. Accordingly, the terminal device receives the second information.

[0273] In this embodiment, S1610 can refer to S910 and S1310. The difference is that in this embodiment, the first timing is indicated by the first information, and the second timing is indicated by the second information. The terminal device may not need to determine the second timing based on the first timing, and may not need to determine the first timing based on the second timing.

[0274] S1620, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0275] In this embodiment, S1620 can be referred to as S920, and will not be described again here.

[0276] The technical effects of this embodiment can be referred to the technical effects of the embodiment shown in Figure 9, and will not be repeated here. Furthermore, in this embodiment, the second timing is also configured by the network device, which can improve the configuration flexibility of the second timing.

[0277] Figure 17 is an exemplary flowchart of a communication method according to an embodiment of this application. One difference between the method shown in Figure 17 and the method shown in Figure 16 is that the method shown in Figure 17 illustrates the relationship between the steps of exchanging first and second information between the network device and the terminal device and other steps.

[0278] S1710, the terminal device reports capability and / or auxiliary information to the network device. Accordingly, the network device receives the capability and / or auxiliary information.

[0279] S1720, the network device sends a management command to the terminal device. Correspondingly, the terminal device receives the management command.

[0280] S1730, the network device sends first information and second information to the terminal device. Correspondingly, the terminal device receives the first information and second information.

[0281] S1740, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0282] In this embodiment, S1710 and S1720 can be referred to as S610 and S620 respectively, and S1730 and S1740 can be referred to as S1610 and S1620 respectively, which will not be described again here.

[0283] Figure 18 is an exemplary flowchart of a communication method according to an embodiment of this application. One difference between the method shown in Figure 18 and the method shown in Figure 16 is that the method shown in Figure 18 further includes the exchange of first and second information between network devices, enabling other network devices to send the first and second information to terminal devices served by other network devices. This allows terminal devices to measure the beam of neighboring cells, thereby improving network management performance, such as mobility management performance and cell handover management performance.

[0284] S1810, the first network device sends first information and second information to the second network device. Correspondingly, the second network device receives the first information and second information.

[0285] In this step, the method by which the first network device sends the first and second information to the second network device can be referenced from the method in S1610 for the network device to send the first and second information to the terminal device, and will not be repeated here. For example, the network device in S1610 can be replaced with the first network device, and the terminal device can be replaced with the second network device.

[0286] S1820, the first network device sends first information and second information to the terminal device. Correspondingly, the terminal device receives the first information and the second information.

[0287] S1830, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0288] In this embodiment, S1820 and S1830 can refer to S1610 and S1620, for example, replacing the network device in the method shown in FIG16 with the first network device, which will not be described in detail here.

[0289] Optionally, in this embodiment, S1812 and S1814 may be included before S1820.

[0290] S1812, the terminal device reports capability and / or auxiliary information to the network device. Accordingly, the network device receives the capability and / or auxiliary information.

[0291] S1814, the network device sends a management command to the terminal device. The terminal device then receives the management command.

[0292] In this embodiment, S1812 and S1814 can be referred to as S610 and S620 respectively, and will not be described again here.

[0293] In some embodiments of this application, the network device can be divided into multiple finer-grained units. In this case, the steps performed by the network device in any of the aforementioned method embodiments can be distributed to these multiple units for execution. Accordingly, these multiple units need to exchange information, such as exchanging first information and / or second information, or even exchanging one or more of the following information: the number of beams in the first beam set, the number of beams in the second beam set, the relationship between beams in the first beam set, the relationship between beams in the second beam set, antenna array information, AI monitoring cycle indication information, AI function activation indication information, input format of the activated AI function, and output format of the activated AI function, in order to implement any of the aforementioned methods.

[0294] The following description uses a network device including a CU and sub-network nodes as an example. The sub-network nodes can be RICs or service units (SUs).

[0295] Figure 19 is an exemplary flowchart of a communication method according to an embodiment of this application. The method shown in Figure 19 can be applied to an O-RAN architecture or an architecture including standalone nodes with AI capabilities.

[0296] S1910, the first CU and the second CU exchange first information and / or second information.

[0297] This step can refer to the content of the first information and / or second information exchanged between the first network device and the second network device in the foregoing embodiments, which will not be repeated here.

[0298] S1920, the first CU sends at least one of the following information to the sub-network node: first information, second information, or third information, wherein the third information includes at least one of the following: the number of beams in the first beam set, the number of beams in the second beam set, the relationship between beams in the first beam set, the relationship between beams in the second beam set, or antenna array information. Accordingly, the sub-network node receives this at least one piece of information.

[0299] In this step, the first CU sends at least one piece of information to the sub-network node, which can be used to assist the sub-network node in managing AI functions.

[0300] S1930, the first CU sends first information and / or second information to the terminal device. Accordingly, the terminal device receives the first information and / or second information.

[0301] This step can be referred to the relevant content in the foregoing embodiments, and will not be repeated here.

[0302] S1940, the terminal device sends at least one of the following information to the network device: measurement results of a first beam set, prediction results of a first beam set, measurement results of a second beam set, or, first prediction information indicating the error between the measurement results and the prediction results of the first beam set. Accordingly, the network device receives this at least one piece of information.

[0303] This step can be referred to in S920, and will not be repeated here.

[0304] In some implementations, this embodiment may also include S1921 and S1925.

[0305] S1921, the terminal device reports capability and / or auxiliary information to the first CU. Accordingly, the first CU receives the capability and / or auxiliary information.

[0306] S1925, the first CU sends a management command to the terminal device. Correspondingly, the terminal device receives the management command.

[0307] In some implementations, this embodiment may also include S1923 and S1924.

[0308] S1923, the first CU reports capability and / or auxiliary information to the sub-network nodes. Correspondingly, the sub-network nodes receive the capability and / or auxiliary information.

[0309] S1924, the sub-network node sends a management command to the first CU. Correspondingly, the first CU receives the management command.

[0310] In this embodiment, S1921 and S1923 can be referred to as S610 and S620, and will not be described again here.

[0311] In some implementations of this embodiment, the management instructions sent by the sub-network node to the first CU may include at least one of the following: AI monitoring cycle indication information, AI function activation indication information, input format of the activated AI function, or output format of the activated AI function.

[0312] In some implementations, the first CU configures the beams included in the second beam set according to the AI ​​function activated by the sub-network node. For example, the number of beams in the second beam set is set to be the same as the input dimension of the activated function. For example, if the function activated by the sub-network node is to predict the measurement results of 8 beams based on the measurement results of 4 beams, then the second beam set may include 4 beams.

[0313] When a sub-network node sends a monitoring period, the first CU can configure the period of the first beam set according to the monitoring period. For example, the period of the first beam set can be set to be the same as the monitoring period, or smaller than the monitoring period, such as 1 / 2 or 1 / 3 of the monitoring period.

[0314] Figure 20 is a schematic diagram of the structure of a communication device according to an embodiment of this application. As shown in Figure 20, the communication device 2000 may include a processing module 2001 and a communication module 2002.

[0315] As a first example, the communication device 2000 can be used to implement the steps performed by the terminal device in any of the foregoing method embodiments. For example, the processing module 2001 is used to implement the processing-related steps performed by the terminal device in any of the foregoing method embodiments, and the communication module 2002 is used to implement the sending and / or receiving steps performed by the terminal device in any of the foregoing method embodiments.

[0316] As a second example, the communication device 2000 can be used to implement the steps performed by the network device (or CU, or sub-network node) in any of the foregoing method embodiments. For example, the processing module 2001 is used to implement the processing-related steps performed by the network device (or CU, or sub-network node) in any of the foregoing method embodiments, and the communication module 2002 is used to implement the sending and / or receiving steps performed by the network device (or CU, or sub-network node) in any of the foregoing method embodiments.

[0317] Figure 21 is a schematic diagram of a communication device provided in another embodiment of this application. As shown in Figure 21, the communication device 2100 includes a processor 2101 and a communication circuit 2102. The processor 2101 and the communication circuit 2102 are coupled to each other. It is understood that the communication circuit 2102 can be a transceiver or an input / output interface. Optionally, the communication device 2100 may also include a memory 2103 for storing instructions executed by the processor 2101, or storing input data required by the processor 2101 to execute instructions, or storing data generated after the processor 2101 executes instructions. It is understood that the memory 2103 can be located outside the processor 2101, or inside the processor 2101.

[0318] As an example, processor 2101 is used to implement the functions of the processing module 2001 described above, and communication circuit 2102 is used to implement the functions of the communication module 2002 described above.

[0319] The communication device 2100 can be a terminal device or a chip used in a terminal device.

[0320] The communication device 2100 may be a network device (or CU, or sub-network node) or a chip applied in a network device (or CU, or sub-network node).

[0321] It is understandable that when the communication device 2100 is a terminal device or a network device, the communication circuit 2102 can be a transceiver. When the communication device 2100 is a chip, the communication circuit 2102 can be an input / output interface.

[0322] In some embodiments of this application, a computer program product is also provided. When the computer program product is run on a processor, it can implement the method implemented by the terminal device in any of the above embodiments, or it can implement the method implemented by the network device (or CU, or sub-network node) in any of the above method embodiments.

[0323] In some embodiments of this application, a computer-readable storage medium is also provided, which contains computer instructions that, when executed on a processor, can implement the methods implemented by the terminal device in any of the above embodiments, or can implement the methods implemented by the network device (or CU, or sub-network node) in any of the above method embodiments.

[0324] In some embodiments of this application, a communication system is also provided, which can implement the methods implemented by terminal devices and network devices (or CUs, or sub-network nodes) in any of the above embodiments.

[0325] It is understood that the processor in the embodiments of this application may be any of the following devices or all or part of the circuitry used for processing functions: a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0326] The method steps or functions in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. 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. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Additionally, the ASIC can reside in a network device or terminal device. Alternatively, the processor and storage medium can exist as discrete components in the network device or terminal device.

[0327] The steps or functions in the above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer 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 executed, in whole or in part. 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.

[0328] 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. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0329] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. A communication method characterized by comprising: The method comprises: receiving first information from a network device, the first information indicating a first occasion, the first occasion being for measuring a first set of beams, the first occasion being different from a second occasion, the second occasion being for measuring a second set of beams, the second set of beams being a subset of the first set of beams, a measurement result of the second set of beams being used for predicting a measurement result of the first set of beams.

2. The method of claim 1, wherein, The first occasion comprises a periodicity and / or an offset.

3. The method according to claim 1 or 2, characterized in that, The first occasion comprises a duration.

4. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: sending first prediction information to the network device, the first prediction information indicating an error between a first prediction result and a first measurement result, the first measurement result comprising a measurement result of the first set of beams measured by a terminal device at the first occasion, the first prediction result comprising a measurement result of the first set of beams predicted by the terminal device based on a second measurement result, the second measurement result comprising a measurement result of the second set of beams measured by the terminal device at the first occasion.

5. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: sending a first measurement result to the network device, the first measurement result comprising a measurement result of the first set of beams measured by a terminal device at the first occasion; sending a first prediction result to the network device, the first prediction result comprising a measurement result of the first set of beams predicted by the terminal device based on a second measurement result, the second measurement result comprising a measurement result of the second set of beams measured by the terminal device at the first occasion.

6. The method according to any one of claims 1 to 5, characterized in that, The method further comprises: sending second prediction information to the network device, the second prediction information indicating a measurement result of the first set of beams predicted by a terminal device based on a third measurement result, the third measurement result comprising a measurement result of the second set of beams measured by the terminal device at the second occasion.

7. The method according to any one of claims 1 to 5, characterized in that, The method further comprises: sending a third measurement result to the network device, the third measurement result comprising a measurement result of the second set of beams measured by a terminal device at the second occasion.

8. The method according to any one of claims 1 to 7, characterized in that, The method further comprises: sending a first measurement result to the network device, the first measurement result comprising a measurement result of the first set of beams measured by a terminal device at the first occasion.

9. The method according to any one of claims 1 to 8, characterized in that, A measurement period of the first set of beams is less than or equal to an artificial intelligence, AI, monitoring period.

10. The method according to any one of claims 1 to 9, characterized in that, The method further comprises: determining the second occasion according to the first occasion.

11. A communication method, comprising: The method comprises: sending first information to a terminal device, the first information indicating a first occasion, the first occasion being for measuring a first set of beams, the first occasion being different from a second occasion, the second occasion being for measuring a second set of beams, the second set of beams being a subset of the first set of beams, a measurement result of the second set of beams being used for predicting a measurement result of the first set of beams.

12. The method of claim 11, wherein, The first occasion comprises a periodicity and / or an offset.

13. The method according to claim 11 or 12, characterized in that, The first occasion comprises a duration.

14. The method according to any one of claims 11 to 13, characterized in that, The method further comprises: receiving first prediction information from the terminal device, the first prediction information indicating an error between a first prediction result and a first measurement result, the first measurement result comprising a measurement result of the terminal device measuring the first beam set at the first time, the first prediction result comprising a measurement result of the terminal device predicting the first beam set based on a second measurement result, the second measurement result comprising a measurement result of the terminal device measuring the second beam set at the first time.

15. The method according to any one of claims 11 to 13, characterized in that, The method further includes: receiving first measurement information from the terminal device, the first measurement information comprising a measurement result of the terminal device measuring the first beam set at the first time; receiving first prediction information from the terminal device, the first prediction information indicating an error between a first prediction result and a first measurement result, the first measurement result comprising a measurement result of the terminal device measuring the first beam set at the first time, the first prediction result comprising a measurement result of the terminal device predicting the first beam set based on a second measurement result, the second measurement result comprising a measurement result of the terminal device measuring the second beam set at the first time.

16. The method according to any one of claims 11 to 15, characterized in that, The method further includes: receiving second prediction information from the terminal device, the second prediction information indicating a measurement result of the terminal device predicting the first beam set based on a third measurement result, the third measurement result comprising a measurement result of the terminal device measuring the second beam set at the second time.

17. The method according to any one of claims 11 to 15, characterized in that, The method further includes: receiving third measurement information from the terminal device, the third measurement information comprising a measurement result of the terminal device measuring the second beam set at the second time.

18. The method according to any one of claims 11 to 17, characterized in that, The method further includes: receiving first measurement information from the terminal device, the first measurement information comprising a measurement result of the terminal device measuring the first beam set at the first time.

19. The method according to any one of claims 11 to 18, characterized in that, A measurement period of the first beam set is less than or equal to an artificial intelligence, AI, monitoring period.

20. The method of any one of claims 11 to 19, wherein, The method further includes: sending the first information to a first network device.

21. The method according to any one of claims 11 to 19, characterized in that, The method further includes: receiving the first information from a first network device.

22. The method of any one of claims 11 to 21, wherein, The method further includes: sending, to a second network device, at least one of: a number of beams of the first beam set, a number of beams of the second beam set, a relationship between beams in the first beam set, a relationship between beams in the second beam set, or, antenna array information.

23. The method of any one of claims 11 to 22, wherein, The method further includes: receiving, from a second network device, at least one of: an indication of an AI monitoring period, an indication of an AI function being activated, an input format of the activated AI function, or, an output format of the activated AI function.

24. A communications device, characterized by A device comprising a processor coupled to a memory, the memory storing program instructions, the processor configured to execute the program instructions in the memory to implement a method as claimed in any one of claims 1 to 10.

25. A communications device, characterized by comprising a processor coupled with a memory for storing program instructions, the processor for executing the program instructions in the memory to implement the method of any of claims 11 to 23.

26. A communication system, characterized by comprising the communication device of claim 24 and the communication device of claim 25.

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