Information transmission method and communication apparatus

Abstract

The present application relates to the technical field of communications. Provided are an information transmission method and a communication apparatus. In the method, a first apparatus can determine, on the basis of the distribution condition of samples among a plurality of acquired samples, samples requiring to be reported, and sends to a second apparatus indication information for indicating the samples requiring to be reported; and on the basis of the indication information, the second apparatus sends to the first apparatus the samples requiring to be reported. Therefore, it is beneficial for the first apparatus to construct a sample set featuring more uniform sample distribution, and the sample set can be configured for AI model training. In a training process, an AI model can learn the features of most or all of the samples, so that an improvement in the accuracy of the AI model can be supported, and thus a relatively objective inference result can be output.

Description

Information transmission method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on November 28, 2023, with application number 202311613227.9 and application name “Method and Communication Device for Information Transmission”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and more specifically, to an information transmission method and a communication device. Background Art

[0003] During the beam management process, both the network device and the terminal device need to traverse the candidate beams, and determine their respective optimal beams based on the comparison of the beam measurement results, and establish beam pairs between the network device and the terminal device, such as the optimal transmit beam and the optimal receive beam.

[0004] To reduce beam scanning overhead, artificial intelligence (AI) technology has been introduced. For example, a terminal device first performs a first round of beam scanning on some beams and inputs the acquired data into an AI model. The AI ​​model then outputs the identifiers of one or more beams based on inference. The terminal device then performs a second round of beam scanning on these one or more beams to ultimately determine the optimal beam.

[0005] To ensure the accuracy of AI models, large amounts of data can be input for model training. However, the accuracy of the AI ​​models obtained based on these solutions may still not meet the requirements. Therefore, how to improve the accuracy of AI models is a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The present application provides a method and a communication device for information transmission to support improving the accuracy of AI models.

[0007] In a first aspect, a method for information transmission is provided, including: sending indication information, the indication information being used to indicate a rule for sample reporting, the rule for sample reporting being used by a terminal device to determine samples that need to be reported, the samples that need to be reported including at least one of beam identification information and beam measurement report; receiving a first sample, the first sample belonging to the sample that needs to be reported.

[0008] The execution entity of the solution described in the first aspect may be the first device, a module within the first device (such as a chip system), or a logical node, logic module, or software that implements all or part of the functions of the first device, without limitation. For ease of description, the following description uses the first device as an example.

[0009] In the above scheme, the first device sends instruction information for indicating the sample reporting rules to the second device. The second device determines the samples that need to be reported based on the sample reporting rules, and selectively reports the samples that need to be reported, rather than reporting all the samples obtained by the second device to the first device. This can support the construction of a sample set with a more even sample distribution, which can be used for AI model training.

[0010] Compared with the existing AI model, which regards certain samples as noise during training due to their small proportion and ignores the learning of the characteristics of the samples, and then outputs less objective reasoning results, the above solution can support the AI ​​model to learn the characteristics of most or all samples during training (compared with the existing AI model training, the above solution can support the AI ​​model to learn the characteristics of more samples), which can support improving the accuracy of the AI ​​model and thus output more objective reasoning results.

[0011] In the first aspect, the method further comprises: receiving a plurality of samples.

[0012] Optionally, the sample that needs to be reported does not belong to the multiple samples.

[0013] For example, after receiving the plurality of samples, the first device determines that some samples are missing from the plurality of samples, and then the samples are the samples that need to be reported. In this way, it can support the construction of samples to form a richer sample set.

[0014] Optionally, the sample that needs to be reported belongs to the multiple samples.

[0015] For example, after receiving the multiple samples, the first device determines that some of the samples account for a small proportion of the samples, and then these samples are the samples that need to be reported. This can support the construction of a sample set with a more even distribution of samples. In addition, when the samples that need to be reported are among the multiple samples, the second device can selectively report the samples instead of reporting all the samples obtained, which can effectively reduce the resource overhead and power consumption of the second device.

[0016] After the first device receives multiple samples, it can detect the sample distribution of the multiple samples and determine the samples that need to be reported among the multiple samples, so as to construct a sample set with a more uniform sample distribution, thereby supporting the AI ​​model to learn the features of most or all samples (compared with the existing AI model training, the above solution can support the AI ​​model to learn the features of more samples), and then output a more objective reasoning result.

[0017] On the second aspect, a method for information transmission is provided, including: receiving indication information, the indication information is used to indicate a rule for sample reporting, the rule for sample reporting is used by a terminal device to determine samples that need to be reported, the samples that need to be reported include at least one of the identification information of the beam and the measurement report of the beam; according to the indication information, sending a first sample, the first sample belongs to the sample that needs to be reported.

[0018] The execution entity of the solution described in the second aspect can be the second device, a module within the second device (such as a chip system), or a logical node, logic module, or software that implements all or part of the functions of the second device, without limitation. For ease of description, the following description uses the second device as an example.

[0019] In the above scheme, the second device can determine the samples that need to be reported according to the sample reporting rules, and selectively report the samples that need to be reported, rather than reporting all the samples obtained by the second device to the first device. This can support the construction of a sample set or multiple samples with a more uniform sample distribution, and the sample set or multiple samples can be used for AI model training.

[0020] Compared with the existing AI model, which regards certain samples as noise during training due to their small proportion and ignores learning the characteristics of the samples, thereby outputting less objective reasoning results, the above solution can support the AI ​​model to learn the characteristics of each sample during training, which can support improving the accuracy of the AI ​​model and output more objective reasoning results.

[0021] By selectively reporting samples to the first device, resource overhead and power consumption of the second device can be effectively reduced.

[0022] In a second aspect, the method further comprises: sending at least one sample.

[0023] By reporting at least one sample to the first device, the first device can detect the distribution of the collected samples and determine the samples that need to be reported.

[0024] In combination with the solution described in any one of the first and second aspects, the indication information includes identification information corresponding to the first sample.

[0025] When the sample to be reported includes beam identification information, the indication information includes the identification information corresponding to the first sample. This can be understood as: the indication information includes the beam identification information; it can also be understood as: the indication information includes an index used to indicate or determine the beam identification information, etc. In other words, the second device can determine that the first sample needs to be reported based on the identification information corresponding to the first sample.

[0026] The association between the index of the beam identification information and the beam identification information may be indicated by the first device to the second device, or the association between the index of the beam identification information and the beam identification information may be predefined. In this way, the second device can determine the corresponding beam identification information based on the association between the index of the beam identification information and the beam identification information and the index of the beam identification information.

[0027] When the sample to be reported includes a beam measurement report, the indication information includes identification information corresponding to the first sample. This can be understood as: the indication information includes identification information of the beam measurement report, such as one or more of the following: an identifier of the beam, an index of the beam measurement report, or identification information of a resource corresponding to the measurement report. In this way, the second device can determine that it needs to report the first sample based on this.

[0028] When the sample that needs to be reported includes the identification information of the beam and the measurement report of the beam, the indication information includes the identification information corresponding to the first sample, which can be understood as: the indication information includes the identification of the beam or the identification of the measurement report of the beam, etc., and the second device can determine that the first sample needs to be reported based on this.

[0029] It is understandable that whether the samples that need to be reported specifically include beam identification information or beam measurement reports can be predefined based on the protocol, or based on preconfiguration or indication.

[0030] Optionally, there may be a correspondence between the beam identifier, the index of the beam measurement report, or the identification information of the resource corresponding to the measurement report, and their correspondence may be predefined by the protocol or based on the configuration.

[0031] Optionally, at least two of the beam identifier, the index of the beam measurement report, or the identification information of the resource corresponding to the measurement report are the same identifier.

[0032] The second device can determine that the first sample needs to be reported based on the identification information corresponding to the first sample. In this way, the resource overhead and power consumption of the second device can be effectively reduced.

[0033] In combination with the solution described in any one of the first and second aspects, the number of samples in the multiple samples is greater than a first threshold.

[0034] In this way, the first device does not need to detect the sample distribution of the multiple samples when the sample quantity of the multiple samples does not reach the threshold, which can effectively reduce the power consumption of the first device.

[0035] In combination with the scheme described in any one of the first and second aspects, the first sample belongs to the multiple samples, and the first sample satisfies at least one of the following: the difference between the proportion of the second sample in the multiple samples and the proportion of the first sample in the multiple samples is greater than or equal to the second threshold, the proportion of the second sample in the multiple samples is higher than the proportion of the first sample in the multiple samples, and the second sample belongs to the multiple samples; or, the proportion of the first sample in the multiple samples is less than or equal to the third threshold.

[0036] In this way, the first device can determine the samples that need to be reported based on any of the above items.

[0037] Optionally, the first sample may not belong to the plurality of samples, so as to support the construction of samples to form a richer sample set.

[0038] In combination with the solution described in any one of the first aspect and the second aspect, the beam is a beam whose received signal quality is greater than a fourth threshold.

[0039] By feeding back relevant information about beams whose received signal quality is greater than a threshold (such as beam identification and beam measurement reports), the AI ​​model can output the identification of one or more beams with better received signal quality when performing inference, so as to more effectively reduce beam scanning overhead.

[0040] In combination with the scheme described in any one of the first and second aspects, the beam is a beam of a channel state information reference signal, the first sample includes identification information of the channel state information reference signal, and the first sample is associated with the synchronization signal block corresponding to the terminal device.

[0041] The beam is a beam of a channel state information reference signal, which can be understood as: the beam is used to carry the channel state information reference signal. Exemplarily, the channel state information reference signal can be sent through the beam.

[0042] A terminal device in a specific area (which may be within the coverage range of the beam corresponding to the SSB (a wide beam)) feeds back information about the beam corresponding to the specific area (such as one or more narrow beams, which correspond to the wide beam corresponding to the SSB), without having to feed back information about beams not related to the specific area (one or more narrow beams not corresponding to the SSB). This is beneficial for better quality samples acquired by the second device, and thus is beneficial for AI model training.

[0043] In combination with the solution described in any one of the first aspect and the second aspect, the indication information is determined based on the sample distribution among the multiple samples.

[0044] The above sample distribution can be understood as: the proportion of different samples, for example, the proportion of sample 1, the proportion of sample 2, the proportion of sample 3, the proportion of sample 4 and the proportion of sample 5, etc.

[0045] The above sample distribution can also be understood as: the difference between the proportions of different samples, for example, the difference between the proportion of sample 1 and the proportion of sample 2, the difference between the proportion of sample 1 and the proportion of sample 3, the difference between the proportion of sample 2 and the proportion of sample 5, etc.

[0046] The first device can determine the samples that need to be reported based on the sample distribution among the multiple samples. The first device can construct a sample set with a more uniform sample distribution based on the samples that need to be reported, and can perform AI model training based on the sample set.

[0047] Compared with the existing AI model, which regards the sample as noise due to its small proportion and thus ignores the learning of the characteristics of the sample, the embodiment of the present application can support the AI ​​model to learn the characteristics of most or all samples (compared with the existing AI model training, the above solution can support the AI ​​model to learn the characteristics of more samples). In this way, this can support improving the accuracy of the AI ​​model.

[0048] In combination with the solution described in any one of the first and second aspects, the types of samples that need to be reported are more than or equal to the types of samples obtained by the terminal device.

[0049] When the types of samples that need to be reported are more than the types of samples obtained by the terminal device (which can be the second device), multiple terminal devices can jointly report the samples that need to be reported, which is conducive to completing the collection of the samples that need to be reported more quickly.

[0050] When the type of sample that needs to be reported is the same as the type of sample obtained by the terminal device (which can be a second device), the second device can report the sample that needs to be reported. In this way, there is no need for multiple terminal devices to jointly report the sample that needs to be reported, which is conducive to reducing the overall power consumption of the terminal device.

[0051] In combination with the solutions described in any one of the first and second aspects, the samples that need to be reported are used for training the artificial intelligence model, and the artificial intelligence model obtained after training is used for beam management.

[0052] In this way, the accuracy of the AI ​​model used for beam management can be effectively improved.

[0053] In a third aspect, a communication device is provided. The communication device may be a first device, or a device or module for executing the function of the first device.

[0054] In one possible implementation, the communication device may include a module or unit corresponding to each of the methods / operations / steps / actions described in the first aspect. The module or unit may be a hardware circuit, software, or a combination of hardware circuit and software.

[0055] The first device mentioned above may be a terminal device or a network device, which is not limited.

[0056] In a fourth aspect, a communication device is provided. The communication device may be a second device, or a device or module for executing the function of the second device.

[0057] In one possible implementation, the communication device may include a module or unit corresponding to each of the methods / operations / steps / actions described in the second aspect. The module or unit may be a hardware circuit, software, or a combination of hardware circuit and software.

[0058] The aforementioned second device may be a terminal device.

[0059] In a fifth aspect, a communication device is provided, comprising a processor, wherein the processor is configured to, by executing a computer program or instruction, or by a logic circuit, enable the communication device to execute the method described in the first aspect and any possible manner of the first aspect; or enable the communication device to execute the method described in the second aspect and any possible manner of the second aspect.

[0060] In a possible implementation, the communication device further includes a memory for storing the computer program or instruction.

[0061] In a possible implementation, the communication device further includes a communication interface, which is used to input and / or output signals.

[0062] In the sixth aspect, a communication device is provided, comprising a logic circuit and an input / output interface, the input / output interface being used to input and / or output signals, the logic circuit being used to execute the method described in the first aspect and any possible manner of the first aspect; or the logic circuit being used to execute the method described in the second aspect and any possible manner of the second aspect.

[0063] In the seventh aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or the instruction is run on a computer, the method described in the first aspect and any possible method of the first aspect is executed; or, the method described in the second aspect and any possible method of the second aspect is executed.

[0064] In an eighth aspect, a computer program product is provided, comprising instructions, which, when executed on a computer, cause the method described in the first aspect and any possible manner of the first aspect to be executed; or cause the method described in the second aspect and any possible manner of the second aspect to be executed.

[0065] In the ninth aspect, a chip system is provided, comprising: a processor, which is used to execute the computer program or instructions in the memory, so that the chip system implements the method in the first aspect and any possible implementation of the first aspect; or, enables the chip system to implement the method in the second aspect and any possible implementation of the second aspect.

[0066] For the description of the beneficial effects of any aspect from the third aspect to the ninth aspect, reference can be made to the description of the beneficial effects of the first aspect and the second aspect, and no further details will be given. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] FIG1 is a schematic diagram of an application framework 100 applicable to an embodiment of the present application.

[0068] FIG2 is a schematic diagram of an application framework 200 applicable to an embodiment of the present application.

[0069] FIG3 is a schematic diagram of a communication system 300 to which an embodiment of the present application is applicable.

[0070] FIG4 is a schematic diagram of a communication system 400 to which an embodiment of the present application is applicable.

[0071] FIG5 is a schematic diagram of beam management 500 .

[0072] FIG6 is a schematic diagram of an interaction flow of a communication method 600 according to an embodiment of the present application.

[0073] FIG7 is a schematic diagram of a correspondence 700 between a terminal device and an SSB.

[0074] FIG8 is a schematic block diagram of a communication device 800 according to an embodiment of the present application.

[0075] FIG9 is a schematic block diagram of a communication device 900 according to an embodiment of the present application. DETAILED DESCRIPTION

[0076] The technical solution in this application will be described below with reference to the accompanying drawings.

[0077] In order to facilitate understanding of the embodiments of the present application, the following points are first explained.

[0078] 1. In this application, unless otherwise specified, "plurality" means two or more.

[0079] 2. In each embodiment of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their internal logical relationships.

[0080] 3. The various numerical numbers involved in this application are only used for the convenience of description and are not used to limit the scope of protection of this application. The size of the serial numbers involved in this application does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic. For example, the terms "first", "second", "third", "fourth" and other various terminology labels (if any) in the specification and claims and drawings of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. Among them, the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than what is illustrated or described here.

[0081] At the same time, any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0082] 4. The terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product or apparatus.

[0083] 5. In this application, "used to indicate" can be understood as "enabling," and "enabling" can include direct enabling and indirect enabling. When describing that certain information is used to enable A, it can include that the information directly enables A or indirectly enables A, and does not necessarily mean that the information contains A.

[0084] The information enabled by the information is called information to be enabled. In the specific implementation process, there are many ways to enable the enabled information, such as but not limited to, directly enabling the information to be enabled, such as the information to be enabled itself or the index of the information to be enabled. The information to be enabled can also be indirectly enabled by enabling other information, wherein there is an association between the other information and the information to be enabled. It is also possible to enable only a part of the information to be enabled, while the other parts of the information to be enabled are known or agreed in advance. For example, it is also possible to enable specific information with the help of the arrangement order of each piece of information agreed in advance (such as specified in the protocol), thereby reducing the enabling overhead to a certain extent. At the same time, it is also possible to identify the common parts of each piece of information and enable them uniformly to reduce the enabling overhead caused by enabling the same information separately.

[0085] 6. In this application, "pre-configuration" may include pre-definition, such as protocol definition. "Pre-definition" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., including each network element). This application does not limit the specific implementation method.

[0086] 7. "Storage" or "saving" as used in this application may refer to storage in one or more memories. The one or more memories may be provided separately or integrated into an encoder or decoder, a processor, or a communication device. The one or more memories may also be provided in part separately and in part integrated into a decoder, processor, or communication device. The type of memory may be any form of storage medium and is not limited thereto.

[0087] 8. The “protocol” referred to in this application may refer to a standard protocol in the field of communications, such as the fourth generation (4G) network, the fifth generation (5G) network protocol, the new radio (NR) protocol, the 5.5G network protocol, the sixth generation (6 th generation, 6G) network protocols and related protocols used in future communication systems, which are not limited in this application.

[0088] 9. The arrows or boxes indicated by dotted lines in the schematic diagrams in the accompanying drawings of this application specification represent optional steps or optional modules.

[0089] 10. In this application, unless otherwise specified, “ / ” indicates that the objects associated with each other are in an “or” relationship. For example, A / B can mean A or B. “And / or” in this application is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural.

[0090] 11. In this application, indication includes direct indication (also called explicit indication) and implicit indication. Direct indication of information A means including information A. Implicit indication of information A means indicating information A through the correspondence between information A and information B and the direct indication of information B. The correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured.

[0091] 12. In this application, the use of information C to determine information D includes both situations where information D is determined solely based on information C and situations where information D is determined based on information C and other information. Furthermore, information C can also be used to determine information D indirectly, for example, where information D is determined based on information E, and information E is determined based on information C.

[0092] 13. In this application, "device A sends information A to device B" can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the device B, which may include sending information to device B directly or indirectly.

[0093] 14. In this application, the phrase "Device B receives information A from Device A" should be understood to mean that the source of information A or an intermediate network element in the transmission path between the source and the device A is Device A, and may include directly or indirectly receiving the information from Device A. Information may undergo necessary processing between the source and destination, such as formatting changes, but the destination can still understand the valid information from the source. Similar expressions in this application should be understood similarly and are not elaborated on here.

[0094] First, a communication system to which the embodiments of the present application are applicable is described.

[0095] The technical solutions provided in this application can be applied to various communication systems, such as 5G or NR systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area networks (WLAN) systems, satellite communication systems, future communication systems, such as 6G mobile communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0096] A device in a communication system can send signals to or receive signals from another device. Signals can include information, signaling, or data. The term "device" can also be replaced by an entity, network entity, device, communication device, communication module, node, or communication node. This application uses devices as an example for description. For example, a communication system can include at least one terminal device and at least one network device. A network device can send downlink signals to a terminal device, and / or a terminal device can send uplink signals to a network device.

[0097] In an embodiment of the present 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 device.

[0098] The terminal device may be a device that provides voice / data, such as a handheld device or vehicle-mounted device with a wireless connection function. At present, some examples of terminals are: mobile phones, tablet computers, laptop computers, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, wearable devices, terminal devices in 5G networks or future evolved public land mobile communication networks (PLMNs). The terminal equipment in the network (PLMN), etc., is not limited to this in the embodiments of the present application.

[0099] As an example and not a limitation, the terminal device can also be a wearable device. Wearable devices can also be called wearable smart devices, which are a general term for wearable devices that use wearable technology to intelligently design and develop wearable devices for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-sized, and independent of smartphones to achieve complete or partial functions, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0100] In the embodiments of the present application, the device for realizing the function of the terminal device can be a terminal device, or a device capable of supporting the terminal device to realize the function, such as a chip system, which can be installed in the terminal device or used in combination with the terminal device. In the embodiments of the present application, the chip system can be composed of a chip, or it can include a chip and other discrete devices. In the embodiments of the present application, only the terminal device is used as an example for description, and the embodiments of the present application are not limited to the solutions of the embodiments of the present application.

[0101] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network.

[0102] The base station can broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmitting point (TP), master station, auxiliary station, multi-standard radio (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, RAN intelligent controller (RIC), etc.

[0103] A base station may also be a macro base station, micro base station, relay node, donor node, or the like, or a combination thereof. A base station may also refer to a communication module, modem, or chip used to be installed in the aforementioned devices or apparatuses. A base station may also be a mobile switching center, a device that performs base station functions in D2D, V2X, and M2M communications, a network-side device in a 6G network, or a device that performs base station functions in future communication systems. A base station may support networks with the same or different access technologies.

[0104] Optionally, the RAN node may also be a server, a wearable device, a vehicle, or an onboard device. For example, the access network device in vehicle-to-everything (V2X) technology may be a roadside unit (RSU). The embodiments of this application do not limit the specific technology and device form used by the network device.

[0105] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0106] In some deployments, the network device may include a CU or a DU, or both a CU and a DU, or a control plane CU node (central unit-control plane (CU-CP)), a user plane CU node (central unit-user plane (CU-UP)), and a DU node. For example, the network device includes a gang-CU-CP, a gNB-CU-UP, and a gNB-DU.

[0107] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes implementing portions of the base station's functionality. For example, a RAN node can be a CU, DU, CU-CP, CU-UP, or RU. The CU and DU can be separate or included in the same network element, such as the BBU. The RU can be included in a radio frequency device or radio unit, such as an RRU, AAU, or RRH.

[0108] A RAN node can support one or more types of fronthaul interfaces, and different fronthaul interfaces correspond to DUs and RUs with different functions.

[0109] If the fronthaul interface between the DU and the 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.

[0110] If the fronthaul interface between the DU and the RU is another interface, relative to CPRI, part of the downlink and / or uplink baseband functions, such as precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) for downlink, are moved from the DU to the RU for implementation; for uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / cyclic prefix (CP) removal are moved from the DU to the RU for implementation.

[0111] In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, the division between the DU and RU is different, corresponding to different types (Categories) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0112] Taking eCPRI Cat A as an example, for downlink transmission, based on layer mapping, the DU is configured to implement layer mapping and one or more functions preceding it (i.e., one or more of coding, rate matching, scrambling, modulation, and layer mapping). Other functions after layer mapping (e.g., RE mapping, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to the RU for implementation. For uplink transmission, based on RE demapping, the DU is configured to implement demapping and one or more functions preceding it (i.e., one or more of decoding, rate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and RE demapping). Other functions after demapping (e.g., one or more of digital BF or fast Fourier transform (FFT) / CP removal) are moved to the RU for implementation. It is understandable that for the functional description of DU and RU corresponding to various types of eCPRI, reference can be made to the eCPRI protocol, which will not be described in detail here.

[0113] In one possible design, the processing unit for implementing baseband functions in the BBU is called a baseband high layer (BBH) unit, and the processing unit for implementing baseband functions in the RRU / AAU / RRH is called a baseband low layer (BBL) unit.

[0114] In different communication systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open RAN (open RAN, ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0115] In the embodiments of the present application, the device for implementing the functions of the network device can be a network device; it can also be a device that can support the network device to implement the functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The device can be installed in the network device or used in conjunction with the network device. In the embodiments of the present application, only the device for implementing the functions of the network device is used as an example to illustrate, and does not constitute a limitation on the solutions of the embodiments of the present application.

[0116] The network equipment and / or terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on the water; and can also be deployed in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the scenarios in which the network equipment and terminal equipment are located.

[0117] In addition, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (for example, a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of terminal devices and network devices.

[0118] In wireless communication networks (such as mobile communication networks), the services supported by the networks are becoming increasingly diverse, and the demands they need to meet are becoming increasingly diverse. For example, the networks need to be able to support ultra-high speeds, ultra-low latency, and ultra-large connections. This makes network planning, network configuration, and resource scheduling increasingly complex. As network functionality becomes increasingly powerful, such as supporting higher spectrum bandwidths, high-order multiple input multiple output (MIMO) technology, beamforming, and / or beam management, network energy conservation has become a hot research topic. These new demands, new scenarios, and new features pose unprecedented challenges to network planning, maintenance, and efficient operations. To meet this challenge, AI technology can be introduced into wireless communication networks to achieve network intelligence.

[0119] In order to support AI technology in wireless networks, the communication system can also introduce AI nodes.

[0120] Optionally, the AI ​​node can be deployed in one or more of the following locations in the communication system: access network equipment, terminal equipment, or core network equipment. Alternatively, the AI ​​node can be deployed separately, for example, in a location other than any of the above devices, such as a host or cloud server in an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, such as one or more of the following: network equipment, terminal equipment, or network elements of the core network.

[0121] It is understood that the embodiments of the present application do not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on function, such as different AI nodes are responsible for different functions.

[0122] It is also understood that AI nodes can be independent devices, or integrated into the same device to implement different functions, or can be network elements in hardware devices, or can be software functions running on dedicated hardware, or can be virtualized functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of AI nodes. Among them, AI nodes can be AI network elements or AI modules.

[0123] Figure 1 is a schematic diagram of an application framework 100 applicable to an embodiment of the present application. As shown in Figure 1, the devices are connected through interfaces (such as NG, Xn) or air interfaces. These device nodes, such as core network equipment, access network nodes (RAN nodes), terminals or one or more devices in operation administration and maintenance (OAM) are provided with one or more AI modules (for clarity, only one is shown in Figure 1). The access network node can be a separate RAN node or can include multiple RAN nodes, for example, including CU and DU. The CU and / or DU can also be provided with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are provided in the CU-CP and / or CU-UP.

[0124] The AI ​​module is used to implement the corresponding AI function. The AI ​​modules deployed in different devices may be the same or different. The model of the AI ​​module can implement different functions according to different parameter configurations. The model of the AI ​​module can be configured based on one or more of the following parameters: structural parameters (for example, the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or at least one of the bias in the activation function), input parameters (for example, the type of input parameters and / or the dimension of the input parameters), or output parameters (for example, the type of output parameters and / or the dimension of the output parameters). The bias in the activation function can also be called the bias of the neural network.

[0125] 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 on the same node or device.

[0126] Figure 2 is a schematic diagram of an application framework 200 applicable to an embodiment of the present application. As shown in Figure 2, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​modules 117 and 118 shown in Figure 1, which are used to implement AI-related functions. The RIC includes a near-real-time RIC (near-real time RIC, near-RT RIC) and a non-real-time RIC (non-real time RIC, Non-RT RIC). The non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to latency, and the latency of the data can be in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, and the latency of the data is in the order of tens of milliseconds.

[0127] Near-real-time RIC is used for model training and inference. For example, it is used to train AI models and use them for inference. Near-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data.

[0128] Optionally, the near real-time RIC may deliver the inference results to the RAN node and / or the terminal.

[0129] Optionally, the inference results can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the near-real-time RIC delivers the inference results to the DU, which then sends them to the RU.

[0130] Non-real-time RIC is also used for model training and inference. For example, it is used to train AI models and use them for inference. Non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to the RAN nodes and / or terminals.

[0131] Optionally, the inference results may be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the non-real-time RIC submits the inference results to the DU, which then sends them to the RU.

[0132] The near-real-time RIC and the non-real-time RIC may also be provided as separate devices. Alternatively, the near-real-time RIC and the non-real-time RIC may also be provided as part of other devices. For example, the near-real-time RIC may be provided in a RAN node (e.g., a CU or DU), while the non-real-time RIC may be provided in an OAM, a cloud server, a core network device, or other network device.

[0133] Figure 3 is a schematic diagram of a communication system 300 applicable to an embodiment of the present application. As shown in Figure 3, communication system 300 may include at least one network device, such as network device 110; communication system 100 may also include at least one terminal device, such as terminal device 120 and terminal device 130. Network device 110 and terminal devices (such as terminal device 120 and terminal device 130) may communicate via wireless links. Communication devices in the communication system, such as network device 110 and terminal device 120, may communicate using multi-antenna technology.

[0134] FIG4 is a schematic diagram of a communication system 400 applicable to an embodiment of the present application. Compared to the communication system 300, the communication system 400 further includes an AI device 140. The AI ​​device 140 is used to perform AI-related operations, such as constructing a training data set or training an AI model.

[0135] In one possible implementation, the network device 110 sends data related to the training of the AI ​​model to the AI ​​device 140, which constructs a training data set and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. The AI ​​device 140 sends the results of the operations related to the AI ​​model to the network device 110, and forwards them to the terminal device through the network device 110. For example, the results of the operations related to the AI ​​model include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a part of the trained AI model is deployed on the network device 110, and the other part is deployed on the terminal device. Alternatively, the trained AI model is deployed on the network device 110. Alternatively, the trained AI model is deployed on the terminal device.

[0136] It should be understood that FIG4 illustrates only an example of a direct connection between AI device 140 and network device 110. In other scenarios, AI device 140 may also be connected to a terminal device; AI device 140 may also be connected to both network device 110 and a terminal device simultaneously; AI device 140 may also be connected to network device 110 through a third-party device, etc. Therefore, this application does not limit the connection relationship between the AI ​​device and other devices.

[0137] The AI ​​device 140 may also be provided as a module in a network device and / or a terminal device, for example, in the network device 110 or the terminal device shown in FIG. 3 .

[0138] It should be noted that Figures 3 and 4 are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 3 and 4. In actual applications, the communication system may include multiple network devices (such as network device 110 and network device 150 (not shown in Figure 3)) and may also include multiple terminal devices. Therefore, this application does not limit the number of network devices and terminal devices included in the communication system.

[0139] Next, some technical concepts involved in this application are briefly described.

[0140] Machine learning (ML): ML is an important technical approach to achieving AI. ML can be categorized into supervised learning, unsupervised learning, and reinforcement learning.

[0141] Supervised learning uses an ML algorithm to learn the mapping relationship between sample values ​​and sample labels based on collected sample values ​​and sample labels. This learned mapping relationship is then expressed using an ML model. The process of training an ML model is the process of learning this mapping relationship. For example, in signal detection, a noisy received signal is a sample, and the true constellation point corresponding to this signal is the label. Through training, the ML model aims to learn the mapping relationship between samples and labels, essentially making the ML model a signal detector. During training, the model parameters are optimized by calculating the error between the model's predicted values ​​and the true labels. Once the mapping relationship is learned, the learned mapping can be used to predict the sample label for each new sample. The mapping relationship learned by supervised learning can include linear and nonlinear mappings. Learning tasks can be categorized into classification and regression tasks based on the type of label.

[0142] In supervised learning, there is a class of tasks where ML algorithms learn to assign labels to examples. For example, classifying an email as "spam" or "not spam." In ML, classification refers to predictive modeling problems in which a class label is predicted for a given example in input data. Examples include: given an example, classify it as spam or not spam; given a handwritten character, classify it as a known character; and based on recent user behavior, classify a user as a churner or a non-churner. From a modeling perspective, classification requires a training dataset containing many input and output examples to learn from. The model uses the training dataset and calculates how to best map the input data to a specific class label. Therefore, the training dataset must be representative and contain many examples for each class. Class labels are typically strings, such as "spam" and "not spam." Class labels must first be mapped to numeric values ​​before they can be used in a modeling algorithm. This process is often called label encoding, which assigns a unique integer to each class label, such as "spam" = 0 and "not spam" = 1. Many different types of classification algorithms are available for modeling classification predictive modeling problems.

[0143] Common classification tasks include:

[0144] 1. Two-classification problem:

[0145] Binary classification tasks are typically modeled using a Bernoulli probability distribution that predicts each example. The Bernoulli distribution is a discrete probability distribution that encompasses binary outcomes, either 1 or 0. For classification problems, this model predicts the probability that an example belongs to the "1" class, or the anomaly class. Common algorithms for binary classification include: 1) Logistic Regression 2) K-Nearest Neighbors 3) Decision Trees 4) Support Vector Machines 5) Naive Bayesian.

[0146] 2. Multi-classification problem:

[0147] Multiclass classification refers to classification tasks with more than two class labels. Examples include face recognition, plant species identification, and optical character recognition. Unlike binary classification, multiclass classification does not have the concept of normal and abnormal results. Instead, samples are classified as belonging to one of a set of known classes. In some problems, the number of class labels can be very large. For example, a model might predict that a photo belongs to one of thousands of faces in a face recognition system. Problems involving predicting word sequences, such as text translation models, can also be considered a special type of multiclass classification. Each word in the sequence to be predicted involves a multiclass classification, and the vocabulary size defines the number of classes that can be predicted, which can be tens of thousands of words. Multiclass classification tasks are often modeled using multivariate probability distributions. A multivariate distribution is a discrete probability distribution consisting of events with a definite classification outcome, such as K in {1, 2, 3, …, K}. For this type of classification task, this means that the model predicts the probability that a sample belongs to each class label. Many binary classification algorithms can also be used for multiclass classification.

[0148] 3. Multi-label classification:

[0149] Multi-label classification refers to classification tasks with two or more class labels, where each example can be predicted to belong to one or more classes. Consider the example of photo classification, where a given photo may have multiple objects in the scene, and the model can predict the presence of multiple known objects in the photo, such as "bicycle," "apple," "person," and so on. This differs from binary and multi-class classification, where each example is predicted to have only a single class label. Multi-label classification tasks are typically modeled using a model that predicts multiple outputs, each of which is predicted as a Bernoulli probability distribution. Essentially, this is a model that makes multiple binary class predictions for each example. Classification algorithms designed for binary or multi-class classification cannot be directly used for multi-label classification. Instead, specialized versions of standard classification algorithms, known as multi-label versions, can be used. These include: 1) multi-label decision trees 2) multi-label random forests 3) multi-label gradient boosting.

[0150] 4. Impact of imbalanced samples on classification task models:

[0151] General classification learning methods assume a relatively balanced number of training samples for different categories. For example, in a two-category classification system, there are approximately 1,000 positive and negative examples. A ratio of 1,200:800 is acceptable. However, if it is 1,900:100, some measures must be taken to address the imbalance. Otherwise, the final training result will likely ignore negative examples and classify all samples as positive. For a three-category classification system, assuming the sample ratios for the three categories are 1,000:800:600, this is acceptable. However, a ratio of 1,000:300:100 is considered imbalanced. This results in the classifier overfitting the first category and underfitting the other two categories, resulting in poor test results.

[0152] Unsupervised learning relies solely on collected sample values, using algorithms to discover inherent patterns within them. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0153] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal decision-making actions. However, because the labels for "correct actions" are not available in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

[0154] Deep neural networks (DNNs) are a specific implementation of machine learning (ML). According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, DNN-based deep learning communication systems can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0155] According to the network construction method, DNN can be divided into feedforward neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN).

[0156] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0157] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0158] The above-mentioned FNN, CNN, and RNN are common neural network structures, which are all constructed based on neurons. As mentioned above, each neuron performs a weighted sum operation on its input values, and the weighted summation result generates an output through a nonlinear function. We call the weights of the weighted summation operation of neurons in the neural network and the nonlinear function the parameters of the neural network. Taking the neuron with max{0,x} as the nonlinear function as an example, The parameters of the neuron to be operated are weights w=[w0,…,w n The weighted sum is biased by b, and the nonlinear function max{0,x}. The parameters of all neurons in a neural network constitute the parameters of the neural network.

[0159] AI Model: An AI model is an algorithm or computer program that implements AI functionality. It represents the mapping between the model's input and output. AI models can be neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q-learning models, or other ML models.

[0160] AI model application or reasoning: using trained AI models to solve practical problems.

[0161] It is understood that the AI ​​model can be implemented as a hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application, or software application.

[0162] Beam management: This function is used to establish and maintain appropriate beam pairs between network devices and terminal devices. For example, for downlink transmission, the network device needs to select an appropriate transmit beam, and the terminal device needs to select an appropriate receive beam. These two beams form a beam pair to maintain a good wireless connection between the network device and the terminal device.

[0163] For further description of beam management, please refer to Figure 5.

[0164] FIG5 is a schematic diagram of beam management 500. As shown in FIG5, the network device sends synchronization signal block 1 (i.e., synchronization signal / physical broadcast channel block, SSB) in the direction of beam 1 (corresponding to beam 1), sends SSB2 in the direction of beam 2 (corresponding to beam 2), and sends SSB3 in the direction of beam 3 (corresponding to beam 3). The receiving end receives three SSBs, namely, SSB1, SSB2, and SSB3, and measures the three SSBs to obtain measurement results of the three SSBs and reports the measurement results of the three SSBs to the network device. The network device determines that the measurement result of SSB2 is better than the measurement results of SSB1 and SSB3 based on the measurement results of the three SSBs reported by the terminal device.

[0165] The network device then divides beam 2 into three sub-beams, namely beam a1, beam a2, and beam a3, and sends channel state information-reference signal (CSI-RS) a1 (corresponding to beam a1), CSI-RS a2 (corresponding to beam a2), and CSI-RS a3 (corresponding to beam a3) respectively. The terminal device receives three CSI-RSs, namely CSI-RS a1, CSI-RS a2, and CSI-RS a3, and measures these three CSI-RSs to obtain the measurement results of the three CSI-RSs and report the measurement results of the three CSI-RSs to the network device. The network device determines that the measurement result of CSI-RS a2 is better than the measurement results of CSI-RS a1 and CSI-RS a3 based on the measurement results of the three CSI-RSs reported by the terminal device. The network device can determine that the sub-beam corresponding to CSI-RS a2 is the best transmitting beam.

[0166] The terminal device can also determine a suitable receiving beam based on the above process.

[0167] Optionally, the terminal device may only report the measurement result of the optimal SSB to the network device, and the network device determines that the beam corresponding to the SSB is the optimal wide beam based on the measurement result of the optimal SSB. Furthermore, the terminal device may only report the measurement result of the optimal CSI-RS to the network device, and the network device determines that the beam corresponding to the CSI-RS is the optimal narrow beam based on the measurement result of the optimal CSI-RS.

[0168] It should be noted that the selection of beams is mainly completed through reference signals and corresponding beam measurements. Reference signals mainly include SSB and CSI-RS. SSB is a cell broadcast signal, which includes the primary synchronization signal (PSS), the secondary synchronization signal (SSS), the physical broadcast channel (PBCH) and the demodulation reference signal (DMRS). SSB can be sent periodically according to the cell configuration, and its function can be used not only for beam management, but also for initial access, time-frequency synchronization, etc. SSB signals can generally be considered as wide-beam signals. CSI-RS signals are user-level signals, and the network side configures one or more groups of CSI-RS resources for users based on actual conditions. CSI-RS signals can also be used not only for beam management, but also for channel quality measurement, etc. The CSI-RS signal can be simply understood as a narrow-beam signal.

[0169] In order to solve the technical problems mentioned in the background technology section, the present application provides a method and a communication device for information transmission to support improving the accuracy of the AI ​​model.

[0170] The following describes the information transmission method and communication device according to the embodiments of the present application in conjunction with the accompanying drawings.

[0171] For ease of understanding and explanation, the following describes the information transmission method of the embodiment of the present application by taking the interaction between the first device and the second device as an example, but this should not constitute any limitation on the execution subject of the information transmission method of the embodiment of the present application. For example, the method performed by the first device can also be performed by a module of the first device (such as a circuit, a chip or a chip system, etc.), and can also be implemented by a logical node, a logical module or software that can realize all or part of the functions of the first device. The method performed by the second device can be performed by a module of the second device (such as a circuit, a chip or a chip system, etc.), and can also be implemented by a logical node, a logical module or software that can realize all or part of the functions of the second device.

[0172] The above-mentioned apparatus may be a communication device or device, or a component or chip system in a device. For example, the first apparatus is a first device, or a first component, or a first chip, etc.; for example, the second apparatus is a second device, or a second component, or a second chip, etc.

[0173] In one possible implementation, the first device may be a terminal device or a network device, and the second device may be a terminal device. For example, the first device is a terminal device and the second device is a terminal device; for example, the first device is a network device and the second device is a terminal device.

[0174] When the first device and the second device are both terminal devices, the communication between the first device and the second device is side link communication; when the first device is a network device and the second device is a terminal device, the communication between the first device and the second device is air interface communication or Uu interface communication.

[0175] The second device may represent one or more devices (using a terminal device as an example). For example, the second device may represent a single terminal device, or the second device may represent multiple terminal devices. For ease of description, the following description uses the example of the second device representing a single terminal device, but does not limit the scenario to the second device representing multiple terminal devices.

[0176] It should be noted that the following description uses the example of a first device having an AI model training function. The first device may also be a device without an AI model training function. When the first device is a device with an AI model training function, it can perform AI model training based on the acquired samples. When the first device is a device without an AI model training function, it can send the acquired samples to a device with an AI model training function, and the device with the model training function can perform AI model training based on the samples.

[0177] FIG6 is a schematic diagram of an interactive process of a method 600 for information transmission according to an embodiment of the present application. As shown in FIG6 , the method includes:

[0178] Optionally, S601a, the second device sends at least one sample to the first device.

[0179] Accordingly, the first device receives at least one sample from the second device.

[0180] Optionally, S601b: the first device receives a plurality of samples, wherein the plurality of samples include the at least one sample, that is, the at least one sample sent by the second device belongs to the plurality of samples.

[0181] By receiving multiple samples, the first device can detect the distribution of these samples and determine the samples that need to be reported by one or more second devices (for ease of description, the following description uses one second device as an example). As an example, the multiple samples mentioned above can be called a sample set.

[0182] For example, the first device receives (or obtains) a sample set, where the sample set includes multiple samples (the multiple samples may come from multiple devices or a single device, without limitation). For example, the first device may obtain multiple samples from multiple terminal devices (such as the second device and the third device).

[0183] Exemplarily, the first device obtains sample 1 and sample 2 from terminal device 1 (eg, the second device), and the first device obtains sample 1 and sample 3 from terminal device 2 (eg, the third device).

[0184] For another example, the first apparatus may obtain multiple samples from a terminal device #M ("#M" is used to represent a certain terminal device).

[0185] For example, terminal device 1 sends sample 1 and sample 2 to terminal device #M, terminal device 2 sends sample 1 and sample 3 to terminal device #M, and terminal device #M (which can be a second device) sends two samples 1, one sample 2, and one sample 3 to the first device. For a description of multiple samples, see Table 1. The content shown in Table 1 is for example only and is not intended to be definitive.

[0186] Table 1

[0187] As shown in Table 1, the first device received a total of 16 samples, namely: 8 samples 1, 4 samples 2, 3 samples 3, 1 sample 4, and 1 sample 5. The proportion of sample 1-sample 2-sample 3-sample 4-sample 5 in the multiple samples is: 8:4:2:1:1.

[0188] In one possible implementation, the sample to be reported includes beam identification information or a beam measurement report, and the beam measurement report includes at least one of a beam measurement result and a resource identifier (the beam measurement result can be characterized by one or more of reference signal received power (RSRP), a complex received signal, or phase information). The resource identifier and the beam identification information may have a configured, indicated, or predefined correspondence, or the resource identifier and the beam identification information may be the same identifier, that is, the resource identifier is the beam identification information.

[0189] For example, the sample to be reported includes identification information of the beam;

[0190] For example, the sample to be reported includes a measurement report of a beam (the beam and the beam indicated by the beam identification information are the same beam);

[0191] For example, the sample to be reported includes beam identification information and a beam measurement report.

[0192] Taking the example that the samples that need to be reported include the identification information of the beam, for example, the first device receives 8 pieces of identification information of beam 1, the first device receives 4 pieces of identification information of beam 2, the first device receives 2 pieces of identification information of beam 3, the first device receives one piece of identification information of beam 4, and the first device receives one piece of identification information of beam 5.

[0193] In one possible implementation, the identification information of the beam may be the identifier of the beam, or may be related information for identifying the beam, such as an index, etc., without limitation.

[0194] Taking the example that the samples that need to be reported include measurement reports of beams, for example, the first device receives 8 measurement reports of beam 1, the first device receives 4 measurement reports of beam 2, the first device receives 2 measurement reports of beam 3, the first device receives one measurement report of beam 4, and the first device receives one measurement report of beam 5.

[0195] It should be noted that the measurement report of the beam includes the measurement result of the beam and a resource identifier (the resource identifier is similar to information such as the identifier of the beam). The resource identifier is used to indicate the resource corresponding to the measurement result of the beam. The resource is associated with the beam. The first device can determine the beam corresponding to the measurement result of the beam based on the association between the resource and the beam (this association relationship can be indicated by the first device to the second device, or it can be predefined. The second device can also obtain the association relationship and can determine the measurement report of the beam that needs to be reported accordingly) and the measurement result of the beam. There may be numerical differences between multiple measurement results corresponding to the same beam (for example, the same beam is measured at different times or locations to obtain multiple different measurement results), but the multiple measurement results corresponding to the same beam can be considered to correspond to the same beam. For example, the first device sends reference signal 1 through resource 1, reference signal 1 corresponds to beam 1 (or reference signal 1 is used to measure the channel quality of beam 1), the second device measures reference signal 1, obtains the measurement result of reference signal 1, and reports the measurement result of reference signal 1 and the identifier of resource 1 to the second device (which can form a measurement report of beam 1). The second device can determine that the measurement result of reference signal 1 corresponds to reference signal 1, thereby determining the measurement result of beam 1.

[0196] Taking the example that the samples that need to be reported include the identification information of the beam and the measurement report of the beam, for example, the first device receives 8 identification information of beam 1 and the measurement report of beam 1, the first device receives 4 identification information of beam 2 and the measurement report of beam 2, the first device receives 2 identification information of beam 3 and the measurement report of beam 3, the first device receives one identification information of beam 4 and the measurement report of beam 4, and the first device receives one identification information of beam 5 and the measurement report of beam 5.

[0197] Optionally, when the first device indicates to the second device that a measurement report of a beam needs to be reported, resource identification information may be carried in the indication information, where the resource identification information is used to indicate the resource that needs to be beam scanned. The second device determines the corresponding resource based on the resource identification information, scans the beam transmitted on the resource, and obtains the measurement report of the beam corresponding to the resource.

[0198] Optionally, the identification information of the resource may also be replaced by the identification information of the measurement report, and the identification information of the measurement report may correspond to the identification information of one or more resources.

[0199] In one possible implementation, the number of samples in the plurality of samples is greater than threshold #1 (i.e., an example of the first threshold). In this way, the first device does not need to detect the sample distribution of the plurality of samples when the number of samples in the plurality of samples does not reach the threshold, which can effectively reduce the power consumption of the first device.

[0200] S601: The first device sends instruction information.

[0201] Correspondingly, the second device receives the instruction information, which is used to indicate a sample reporting rule, and the sample reporting rule is used by the second device to determine the sample that the second device needs to report.

[0202] After the first device receives multiple samples, it can detect the sample distribution and determine the samples that need to be reported, so that a sample set with a more even sample distribution can be constructed or formed, thereby supporting the AI ​​model to learn the features of most or all samples (compared to the existing AI model training, the above solution can support the AI ​​model to learn the features of more samples), and then output more objective reasoning results.

[0203] In a possible implementation, the sample that needs to be reported does not belong to the multiple samples. For example, after receiving the multiple samples, the first device determines that some samples are missing from the multiple samples, and the samples are the samples that need to be reported.

[0204] Taking Table 1 as an example, the first device received multiple samples 1, 2, 3-5, but did not receive sample 6, which is the sample that needs to be reported. In this way, it can support the construction of samples to form a richer sample set.

[0205] In one possible implementation, the sample that needs to be reported belongs to the multiple samples. For example, after receiving the multiple samples, the first device determines that some of the samples account for a small proportion, and then these samples are the samples that need to be reported. For a detailed description, please refer to Table 1. In this way, it is possible to support the construction of a sample set with a more even sample distribution.

[0206] It should be noted that there may or may not be an intersection between the sample that needs to be reported and the at least one sample already reported by the second device. The second device can selectively report samples from newly acquired samples (the newly acquired samples may be different from the at least one sample already reported) based on the instruction of the first device, rather than reporting all newly acquired samples. This can effectively reduce the resource overhead and power consumption of the second device.

[0207] For example, the second device obtains a first round of samples and reports all of the first round of samples to the first device, where the first round of samples is the at least one sample described above. After the second device obtains a second round of samples, the second device may selectively report samples based on the instruction information sent by the first device, rather than reporting all of the obtained samples. The second round of samples may be unrelated to the first round of samples.

[0208] It should also be noted that the samples reported by the second device to the first device may be part or all of the samples required to be reported. For example, if the second device determines that the samples required to be reported are sample 3, sample 4, and sample 5 based on the sample reporting rules, the samples actually reported by the second device to the first device may be one or more of sample 3, sample 4, or sample 5.

[0209] In S601, the first device may broadcast the indication information, may multicast the indication information, or may unicast the indication information.

[0210] When the first device broadcasts the indication information, the devices that receive the indication information may be multiple devices (including the second device), and each of the multiple devices determines the samples that need to be reported based on the sample reporting rules.

[0211] For a description of the first device broadcasting the instruction information, please refer to Table 2. The content shown in Table 2 is only an example and is not a final limitation.

[0212] Table 2

[0213] As shown in Table 2:

[0214] For terminal device 1, the instruction information indicates that the samples it needs to report are sample 3, sample 4, and sample 5;

[0215] For terminal device 2, the instruction information indicates that the samples it needs to report are sample 3, sample 4, and sample 5;

[0216] For terminal device 3, the indication information indicates that the samples it needs to report are sample 3, sample 4 and sample 5.

[0217] In this way, the first device can obtain the samples that need to be reported in a shorter time.

[0218] In an embodiment of the present application, when the first device transmits indication information via broadcast, multiple devices receiving the indication information may determine the samples that need to be reported based on the indication information. Furthermore, each device receiving the indication information may report a portion of the samples that need to be reported, rather than all of the samples, based on its corresponding SSB. For a detailed description, please refer to the description of unicast transmission of indication information below.

[0219] When the first device multicasts the indication information, there may be multiple devices (including the second device) that receive the indication information. The multiple devices may be grouped into a device group, and each device in the device group determines the samples that need to be reported according to the sample reporting rules.

[0220] For a description of the multicast sending instruction information by the first device, please refer to Table 3. The content shown in Table 3 is only an example and is not a final limitation.

[0221] Table 3

[0222] As shown in Table 3:

[0223] For terminal device 1, the instruction information indicates that the samples it needs to report are sample 3, sample 4, and sample 5;

[0224] For terminal device 2, the instruction information indicates that the samples it needs to report are sample 3, sample 4, and sample 5;

[0225] For terminal device 3, the indication information indicates that the sample it needs to report is sample 3.

[0226] Exemplarily, terminal device 1 and terminal device 2 belong to the same terminal device group. The first apparatus can send indication information to the terminal device group, which is used to instruct terminal device 1 and terminal device 2 to report sample 3, sample 4, and sample 5. Terminal device 3 belongs to another terminal device group. The first apparatus can send another indication information to the terminal device group, which is used to instruct terminal device 3 to report sample 3. In this way, different terminal device groups can report the samples that need to be reported to the first apparatus based on the indication information they receive. For example, terminal device 1 or terminal device 2 determines that the samples it needs to report are sample 3, sample 4, and sample 5 based on the indication information; terminal device 3 determines that the sample it needs to report is sample 3 based on the indication information.

[0227] When different samples are reported respectively by different terminal devices or terminal device groups, this can enable the terminal devices in a specific area (which can be in the coverage range of the beam corresponding to the SSB (a wide beam)) to feedback information about the beam corresponding to the specific area (such as one or more narrow beams, the one or more narrow beams corresponding to the wide beam corresponding to the SSB), without having to feedback information about beams not related to the specific area (one or more narrow beams not corresponding to the SSB), which is beneficial for better quality of samples acquired by the second device, and thus beneficial for AI model training.

[0228] The above-mentioned terminal device groups can be terminal devices located within the coverage of the same SSB. For example, the terminal devices in terminal device group 1 are all located within the coverage of SSB1, and the terminal devices in terminal device group 1 can determine the samples that need to be reported based on the received indication information. The terminal devices in terminal device group 2 are all located within the coverage of SSB2, and the terminal devices in terminal device group 2 can determine the samples that need to be reported based on the received indication information. The indication information received by the terminal devices in terminal device group 1 can be different from the indication information received by the terminal devices in terminal device group 2, or can be the same, and this is not limited.

[0229] When the first device unicasts the indication information, the device that receives the indication information is one (or multiple devices, all of which are located within the coverage of the same SSB) (taking the second device as an example), and the device determines the samples that need to be reported according to the sample reporting rules.

[0230] For a description of the first device unicasting the indication information, see Table 4. The content shown in Table 4 is only an example and is not a final limitation.

[0231] Table 4

[0232] As shown in Table 4:

[0233] For terminal device 1, the indication information indicates that the sample it needs to report is sample 3, and the SSB corresponding to terminal device 1 is SSB1;

[0234] For terminal device 2, the indication information indicates that the sample it needs to report is sample 4, and the SSB corresponding to terminal device 2 is SSB2;

[0235] For terminal device 3, the indication information indicates that the sample it needs to report is sample 5, and the SSB corresponding to terminal device 3 is SSB3.

[0236] For terminal devices:

[0237] The first device sends instruction information 1 to terminal device 1, and terminal device 1 determines that the sample to be reported is sample 3 according to instruction information 1;

[0238] The first device sends instruction information 2 to the terminal device 2, and the terminal device 2 determines that the sample to be reported is sample 4 according to the instruction information 2;

[0239] The first device sends indication information 3 to the terminal device 3 , and the terminal device 3 determines that the sample to be reported is sample 5 according to the indication information 3 .

[0240] In the embodiment of the present application, different terminal devices correspond to different SSBs, as shown in Figure 7.

[0241] FIG7 is a schematic diagram of a correspondence 700 between terminal devices and SSBs. As shown in FIG7 , different terminal devices correspond to different SSBs. For example, if terminal device 1 is within the coverage of the beam corresponding to SSB1, terminal device 1 corresponds to SSB1; if terminal device 2 is within the coverage of the beam corresponding to SSB2, terminal device 2 corresponds to SSB2; and if terminal device 3 is within the coverage of the beam corresponding to SSB3, terminal device 3 corresponds to SSB3.

[0242] When performing beam scanning, terminal device 1 can send the measurement results of the beam corresponding to SSB1 to the first device, where SSB1 corresponds to channel state information reference signal (CSI-RS) 3; terminal device 2 sends the measurement results of the beam corresponding to SSB2 to the first device, where SSB2 corresponds to CSI-RS 4; terminal device 3 sends the measurement results of the beam corresponding to SSB3 to the first device, where SSB3 corresponds to CSI-RS5.

[0243] When performing beam scanning, the terminal device may first obtain the measurement result of the wide beam. For example, terminal device 1 obtains the measurement result of wide beam 1 (such as SSB1) and reports the measurement result of wide beam 1; terminal device 2 obtains the measurement result of beam 2 (such as SSB2) and reports the measurement result of wide beam 2 (such as SSB2); terminal device 3 obtains the measurement result of beam 3 (such as SSB3) and reports the measurement result of wide beam 3. Accordingly, the first device may record the correspondence between the terminal device and the SSB. For example, the first device determines that terminal device 1 is associated with SSB1, determines that terminal device 2 is associated with SSB2, and determines that terminal device 3 is associated with SSB3.

[0244] In this embodiment of the present application, the beam corresponding to the SSB is a wide beam, and the beam corresponding to the CSI-RS is a narrow beam. A wide beam may include at least one narrow beam. For example, SSB1 corresponds to CSI-RS3 (or multiple CSI-RSs), SSB2 corresponds to CSI-RS4 (or multiple CSI-RSs), and SSB3 corresponds to CSI-RS5 (or multiple CSI-RSs).

[0245] In the embodiment of the present application, the aforementioned beam may be a narrow beam, and the identification information of the beam included in the aforementioned sample may be identification information of the narrow beam, such as identification information of a CSI-RS.

[0246] After the first device determines the correspondence between SSB and CSI-RS, the first device can send corresponding indication information to the corresponding terminal device according to the correspondence between SSB and CSI-RS.

[0247] For example, if it is determined that terminal device 1 corresponds to SSB1 and SSB1 corresponds to CSI-RS3, indication information 1 is sent to terminal device 1, where indication information 1 is used to indicate reporting of information of the beam corresponding to CSI-RS3 (such as identification information and measurement report);

[0248] For example, if it is determined that terminal device 2 corresponds to SSB2, and SSB2 corresponds to CSI-RS4, indication information 2 is sent to terminal device 2, where indication information 2 is used to indicate reporting of information of the beam corresponding to CSI-RS4 (such as identification information and measurement report);

[0249] For example, if it is determined that terminal device 3 corresponds to SSB3 and SSB3 corresponds to CSI-RS5, indication information 3 is sent to terminal device 3, and indication information 3 is used to indicate the information of the beam corresponding to CSI-RS5 (such as identification information and measurement report).

[0250] The first device may determine that the samples to be reported are sample 3, sample 4, and sample 5 according to the sample distribution of the multiple samples shown in Table 1, and each sample corresponds to a narrow beam, such as a sample corresponding to a CSI-RS.

[0251] When the first device determines that it is necessary to report the measurement report of the beam corresponding to CSI-RS 3, the measurement report of the beam corresponding to CSI-RS 4, and the measurement report of the beam corresponding to CSI-RS 5, the first device can determine, based on the correspondence between CSI-RS and SSB, that the terminal device within the coverage range of the beam corresponding to SSB1 needs to report the measurement report of the beam corresponding to CSI-RS 3, and then send indication information 1 to terminal device 1; determine that the terminal device within the coverage range of the beam corresponding to SSB2 needs to report the measurement report of the beam corresponding to CSI-RS4, and then send indication information 2 to terminal device 2; determine that the terminal device within the coverage range of the beam corresponding to SSB3 needs to report the measurement report of the beam corresponding to CSI-RS 5, and then send indication information 3 to terminal device 3.

[0252] In this way, a terminal device in a specific area (which may be within the coverage range of the beam corresponding to the SSB (a wide beam)) feeds back information about the beam corresponding to the specific area (such as one or more narrow beams, the one or more narrow beams corresponding to the wide beam corresponding to the SSB), without having to feed back information about beams not related to the specific area (one or more narrow beams not corresponding to the SSB). This is beneficial for better quality samples acquired by the second device, and thus is beneficial for AI model training.

[0253] It should be noted that the content shown in Table 4 is described based on the number of terminal devices within the coverage of an SSB as 1. The content shown in Table 4 is also applicable to the scenario where the number of terminal devices within the coverage of an SSB is more than one.

[0254] It should also be noted that when the first device sends indication information to multiple terminal devices in a broadcast manner, the multiple terminal devices that receive the indication information can report some samples according to the correspondence between SSB and CSI-RS. For example, the first device broadcasts indication information to terminal device 1, terminal device 2, and terminal device 3, and the indication information is used to indicate that the samples to be reported include sample 1, sample 2, and sample 3. Terminal device 1 determines to report sample 1 to the first device based on the correspondence between SSB and CSI-RS, terminal device 2 determines to report sample 2 to the first device based on the correspondence between SSB-CSI-RS, and terminal device 3 determines to report sample 3 to the first device based on the correspondence between SSB-CSI-RS. In this way, each terminal device can obtain samples of better quality and avoid interference of channel quality on the beam measurement of the terminal device. Among them, the above-mentioned correspondence between SSB-CSI-RS can be pre-configured in the terminal device, or can be indicated by the first device to the multiple terminal devices, and this is not limited.

[0255] In one possible implementation, the sample reporting rules used by the terminal device to determine the samples that need to be reported may include:

[0256] The terminal device indirectly determines the samples to be reported based on the sample reporting rules; or,

[0257] The terminal device directly determines the samples that need to be reported based on the sample reporting rules.

[0258] Taking the example of a terminal device indirectly determining the samples that need to be reported based on the sample reporting rules, for example, the indication information includes the identification information of sample 1. The terminal device may determine based on the identification information of sample 1 that sample 1 does not need to be reported, but instead needs to report samples 2, 3, 4, and 5. Accordingly, the terminal device reports one or more of sample 2, sample 3, sample 4, and sample 5 to the first apparatus.

[0259] For example, the terminal device directly determines the sample to be reported according to the sample reporting rules. For example, if the indication information includes the identification information of sample 3, the terminal device can determine that sample 3 needs to be reported according to the identification information of sample 3. Accordingly, the terminal device can report sample 3 to the first apparatus.

[0260] For example, the indication information carries identification information corresponding to the sample to be reported. Exemplarily, the indication information includes identification information corresponding to the first sample. The second device determines that the first sample needs to be reported (the number of the first samples is not limited) based on the identification information corresponding to the first sample.

[0261] For another example, the indication information carries identification information corresponding to a sample that does not need to be reported. Exemplarily, the indication information includes identification information corresponding to a second sample. The second device determines, based on the identification information corresponding to the second sample, that the second sample does not need to be reported, and determines that samples other than the second sample, such as the first sample, need to be reported.

[0262] In one possible implementation of the embodiment of the present application, the first sample belongs to the multiple samples, and the first sample satisfies at least one of the following:

[0263] The difference between the proportion of the second sample in the plurality of samples and the proportion of the first sample in the plurality of samples is greater than or equal to threshold #2

[0264] (ie, an example of the second threshold), the proportion of the second sample in the multiple samples is higher than the proportion of the first sample in the multiple samples, and the second sample belongs to the multiple samples; or

[0265] The proportion of the first sample in the plurality of samples is less than or equal to threshold #3 (ie, an example of the third threshold).

[0266] For the first item, exemplarily, the second sample is sample 1, the first sample is sample 4, the proportion of sample 1 is 8 / 16, the proportion of sample 4 is 1 / 16, and the difference in proportion between sample 1 and sample 4 is greater than threshold #2 (such as threshold #2 = 0.4) (can be flexibly set and is not limited), and the first device determines that sample 4 needs to be reported.

[0267] For the second item, illustratively, the first sample is sample 4, the proportion of sample 4 is 1 / 16, and is less than threshold #3 (such as threshold #3=0.2) (can be flexibly set and is not limited), and the first device determines that sample 4 needs to be reported.

[0268] In this way, the first device can determine the samples that need to be reported based on any of the above items.

[0269] When the first sample does not belong to the multiple samples, the proportion of the first sample in the multiple samples is 0 and is less than the threshold #3.

[0270] When the first device receives the sample that needs to be reported, it can form a sample set with a more even distribution based on the sample that needs to be reported and the multiple samples that have been obtained, thereby supporting the AI ​​model to learn the features of most or all samples (compared with the existing AI model training, the above solution can support the AI ​​model to learn the features of more samples), and then output a more objective reasoning result.

[0271] Optionally, the first sample may also be determined according to a measurement report of the beam, as shown in Table 5. The content shown in Table 5 is only an example and is not a final limitation.

[0272] Table 5

[0273] As shown in Table 5:

[0274] Terminal device 1 reports RSRP set 1, including: {RSRP1, RSRP2, RSRP3, RSRP4, RSRP5, RSRP6};

[0275] Terminal device 2 reports RSRP set 2, including: {RSRP1, RSRP2, RSRP3, RSRP4, RSRP5, RSRP6};

[0276] Terminal device 3 reports RSRP set 3, including: {RSRP1, RSRP2, RSRP3, RSRP4, RSRP5, RSRP6};

[0277] Terminal device 4 reports RSRP set 4, including: {RSRP1, RSRP2, RSRP3, RSRP4, RSRP5, RSRP6};

[0278] Terminal device 5 reports RSRP set 5, including: {RSRP1, RSRP2, RSRP3, RSRP4, RSRP5, RSRP6};

[0279] Terminal device 6 reports RSRP set 6, including: {RSRP1, RSRP2, RSRP3, RSRP4, RSRP5, RSRP6}.

[0280] Each terminal device may report part or all of the measurement reports of the multiple beams obtained by it to the first apparatus (the resource identifiers in the measurement reports are not shown in Table 5).

[0281] The first device can draw a change curve for the RSRP set reported by the terminal device. For example, the first device determines the sum of the 6 RSRPs based on the 6 RSRPs reported by the terminal device 1, and determines the ratio between each RSRP and the sum, and draws a curve graph based on the 6 ratios. The curve graph will have a highest point (the highest point can be any one of RSRP1-RSRP6, without limitation).

[0282] In combination with the above description, it can be seen that the first device can draw six curves (each curve has a highest point), and can determine the samples that need to be reported based on the six curves. For example, the highest point in the first curve is RSRP1 (corresponding to RSRP set 1), the highest point in the second curve is RSRP2 (corresponding to RSRP set 2), the highest point in the third curve is RSRP3 (corresponding to RSRP set 3), the highest point in the fourth curve is RSRP4 (corresponding to RSRP set 4), the highest point in the fifth curve is RSRP5 (corresponding to RSRP set 5), and the highest point in the sixth curve is RSRP4 (corresponding to RSRP set 6). The first device can determine that the RSRP set with RSRP6 as the highest value is the sample that needs to be reported. The first device can send indication information to some or all of the terminal devices among terminal devices 1-terminal devices 6 to indicate that RSRP6 is reported as the RSRP set with the highest value, that is, the resource corresponding to RSRP6 is a measurement report of the optimal beam. Optionally, the indication information sent by the first device to some or all of the terminal devices among terminal devices 1-terminal devices 6 indicates one or more of the resource identifier or beam identifier information corresponding to RSRP6.

[0283] Optionally, the indication information may be used to indicate that the RSRP set with the highest value of RSRP6 is reported. After receiving the indication information, the terminal device sends the RSRP set with the highest value of RSRP6 to the first apparatus.

[0284] Optionally, the indication information may indicate not only reporting the RSRP set with RSRP6 as the highest value but also reporting the RSRP set with RSRP6 as the possible highest value. After receiving the indication information, the terminal device sends the RSRP set with RSRP6 as the highest value to the first device, and sends the RSRP set with RSRP6 as the possible highest value, that is, the RSRP set does not include RSRP6, but the beam corresponding to the highest value in the RSRP set is adjacent to the beam corresponding to RSRP6 in the spatial domain. For example, the highest value in the RSRP set is RSRP5. Since the RSRP set does not include the value of RSRP6, and the beam corresponding to RSRP6 and the beam corresponding to RSRP5 are adjacent in the spatial domain, the value of RSRP6 may be higher than the value of RSRP5.

[0285] When the terminal device obtains measurement results for a larger number of beams, it may only send the measurement results for some of the beams to the first device. When the first device draws a curve based on the measurement results of the partial beams, it may determine the set of measurement results for the beams that need to be reported based on the above description of RSRP6. In summary, the embodiments of the present application do not limit the manner in which the first device determines the samples that need to be reported.

[0286] In a possible implementation, the above-mentioned indication information is determined according to the sample distribution among the multiple samples.

[0287] In this way, the first device can determine the samples that need to be reported based on the sample distribution among the multiple samples. The first device can construct a sample set with a more uniform sample distribution based on the samples that need to be reported, and can perform AI model training based on the sample set.

[0288] Compared with the existing AI model, which regards the sample as noise due to its small proportion and thus ignores the learning of the characteristics of the sample, the embodiment of the present application can support the AI ​​model to learn the characteristics of most or all samples (compared with the existing AI model training, the above solution can support the AI ​​model to learn the characteristics of more samples). In this way, this can support improving the accuracy of the AI ​​model.

[0289] The above sample distribution can be understood as: the proportion of different samples, for example, the proportion of sample 1, the proportion of sample 2, the proportion of sample 3, the proportion of sample 4 and the proportion of sample 5, etc.

[0290] The above sample distribution can also be understood as: the difference between the proportions of different samples, for example, the difference between the proportion of sample 1 and the proportion of sample 2, the difference between the proportion of sample 1 and the proportion of sample 3, the difference between the proportion of sample 2 and the proportion of sample 5, etc.

[0291] Taking Table 1 as an example, combined with the aforementioned description of the impact of unbalanced samples on classification task models, when the ratios of sample 1, sample 2, sample 3, sample 4, and sample 5 in multiple samples are 8:4:2 and 1:1, this will result in the AI ​​model trained based on the samples shown in Table 1 being unable to learn the characteristics of samples 3, 4, and 5. In subsequent AI model applications, the AI ​​model will tend to believe that the beam corresponding to sample 1 is the optimal beam and will ignore the possibility that the beam corresponding to sample 4 is also the optimal beam. This may result in the AI ​​model's accuracy failing to meet requirements. For example, the AI ​​model's inference result may indicate that the beam corresponding to sample 1 is the optimal beam, but in fact the beam corresponding to sample 4 is the optimal beam.

[0292] S602: The second device sends a first sample to the first device.

[0293] Correspondingly, the first device receives the first sample, which belongs to the sample that needs to be reported.

[0294] The first sample may include one or more samples, and the one or more samples belong to the samples that need to be reported.

[0295] Optionally, the first sample may be part or all of the samples that need to be reported, and this is not limited. The first device may train an AI model according to Table 1 and the samples that need to be reported, and the AI ​​model obtained after training can be used for beam management.

[0296] For a description of the first device training the AI ​​model according to Table 1 and the samples to be reported, please refer to Table 6. The content shown in Table 6 is only an example description and is not a final limitation.

[0297] Table 6

[0298] As shown in Table 6, the first four rows contain the content shown in Table 1, and the last three rows contain the samples that need to be reported. When the first device obtains the samples that need to be reported (the samples that need to be reported can be obtained from one or more second devices, without limitation), the ratios of samples 1 to 5 are: 8:8:5:4:3, respectively.

[0299] The first device can train the AI ​​model based on the samples shown in Table 6. Since the proportions of the above samples are evenly distributed, the first device can learn the characteristics of each sample and will not tend to assume that the beam corresponding to sample 1 is the optimal beam. Therefore, it can more accurately infer the appropriate optimal beam, thereby meeting the needs.

[0300] To sum up, the first device sends indication information for indicating the sample reporting rules to the second device. The second device can determine the samples that need to be reported based on the sample reporting rules, and selectively report the samples that need to be reported, rather than reporting all the samples obtained by the second device to the first device. This can support the construction of a sample set with a more even sample distribution, which can be used for AI model training.

[0301] Compared with the existing AI model, which regards certain samples as noise due to their small proportion during training and ignores the learning of the characteristics of the samples, and then outputs less objective reasoning results, the above solution can support the AI ​​model to learn the characteristics of each sample or most or all samples during training (compared with the existing AI model training, the above solution can support the AI ​​model to learn the characteristics of more samples), which can support improving the accuracy of the AI ​​model and thus output more objective reasoning results.

[0302] In an embodiment of the present application, the types of samples that need to be reported are more than or equal to the types of samples that can be obtained by the second device. For example, when the second device performs beam measurement, the samples that can be obtained (the samples satisfy the aforementioned related information of the beam with signal reception quality greater than threshold #4) include sample 1, sample 2, and sample 3, and the samples that need to be reported include: sample 1, sample 2, sample 3, sample 4, and sample 5. The types of samples that need to be reported are more than the types of samples that can be determined by the second device; or, the samples that need to be reported include sample 1, sample 2, and sample 3, and the types of samples that need to be reported are equal to the types of samples that can be determined by the second device.

[0303] Optionally, multiple devices may jointly report the samples that need to be reported, which is conducive to completing the collection of the samples that need to be reported more quickly. In this case, the types of samples that need to be reported may be more than the types of samples that the second device can determine. That is, when the aforementioned indication information is sent by broadcast or multicast, the types of samples that need to be reported indicated by the indication information may be more than the types of samples that can be obtained by each of the multiple devices that receive the indication information. At this time, each of the multiple devices can report part of the samples that need to be reported to the first device according to the types of samples that can be obtained.

[0304] Optionally, a single device may report the sample that needs to be reported. This can avoid multiple devices from reporting, save signaling, and reduce power consumption of terminal devices that do not report. In this case, the first device may send the indication information in a unicast manner.

[0305] In this case, the device that receives the indication information needs to report the types of samples based on the indication information and reports all the types of samples that need to be reported. Optionally, when the indication information is sent in unicast mode, the first device may first determine whether the second device to which the indication information is sent has the ability to obtain the types of samples that need to be reported. For example, the first device determines that the second device has reported the sample that needs to be reported, or has reported samples related to the sample that needs to be reported. For example, the sample that needs to be reported is beam 2, and the samples related to the sample that needs to be reported may be adjacent beams of beam 2.

[0306] In an embodiment of the present application, the above-mentioned beam can be a beam whose received signal quality (such as RSRP, or other terms used to characterize channel quality, not limited) is greater than threshold #4 (i.e., an example of the fourth threshold).

[0307] Optionally, the above beam may also be a beam with the best received signal quality.

[0308] By feeding back relevant information about the beam with the highest received signal quality (such as the beam identifier and beam measurement report), the AI ​​model can output the identifiers of one or more beams with better received signal quality when performing inference, so as to more effectively reduce beam scanning overhead.

[0309] For example, the terminal device measures multiple beams, obtains the measurement results of each beam, determines the beam whose measurement results are greater than threshold #4, and reports the information of the beam (such as the identification information of the beam or at least one item in the measurement report of the beam) to the first device.

[0310] Exemplarily, terminal device 1 measures beam 1-beam 100, obtains the measurement results of the beams (for example, expressed as RSRP values), and filters the measurement results of the 100 beams to determine the beams whose RSRP is greater than threshold #4. For example, if beam 1-beam 10 is a beam whose RSRP is greater than threshold #4, then at least one item of the identification information of each beam in beam 1-beam 10 or the measurement report of the beam is reported.

[0311] Exemplarily, terminal device 2 measures beam 1 to beam 100, obtains the measurement results of the beams (for example, expressed in RSRP values), filters the measurement results of the 100 beams, and determines the beams whose RSRP is greater than threshold #4. For example, if beam 10 to beam 20 are beams whose RSRP is greater than threshold #4, then at least one item of the identification information of each beam in beam 10 to beam 20 or the measurement report of the beam is reported.

[0312] By feeding back relevant information about beams whose received signal quality is greater than a threshold (such as beam identification and beam measurement reports), the AI ​​model can output the identification of one or more beams with better received signal quality when performing inference, so as to more effectively reduce beam scanning overhead.

[0313] In the embodiment of the present application, multiple samples with low proportions can also be classified into the same category. As shown in Table 1, samples 3-4 and 5 are all samples with low proportions. The first device classifies samples 3, 4 and 5 into one category (represented by sample X), and the proportion of sample X is 4 / 16. The distribution of samples 1, 2 and X is relatively uniform. The first device performs AI model training based on sample 1, sample 2 and sample X. When the first device newly acquires multiple samples 3, the first device can also classify sample 4 and sample 5 into one category (represented by sample XX), and perform AI model training based on sample 1, sample 2, sample 3 and sample XX. In this way, the existing samples can be fully utilized for AI model training, which can avoid wasting samples.

[0314] In one embodiment of the present application, a first device may periodically send instruction information to one or more second devices. In this way, the first device may periodically update the sample set used for AI model training, which may enable the AI ​​model to learn the features of most or all samples and thus output more objective inference results.

[0315] Finally, the device embodiment of the embodiment of the present application is introduced.

[0316] To implement the various functions of the method provided herein, the first device and the second device may each include hardware structures and / or software modules, and implement the aforementioned functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular function is implemented in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0317] Figure 8 is a schematic block diagram of a communication device 800 according to an embodiment of the present application. The communication device 800 includes a processing circuit 810 and a transceiver circuit 820. The processing circuit 810 and the transceiver circuit 820 may be interconnected or coupled, for example, via a bus 830. The communication device 800 may be a first device or a second device.

[0318] Optionally, the communication device 800 may further include a memory 840. The memory 840 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or portable read-only memory (CD-ROM), and is used for related instructions and data.

[0319] The processing circuit 810 may be all or part of the processing circuit in one or more processors, or one or more processors. The processor may be a central processing unit (CPU). When the processing circuit 810 is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processing circuit 810 may be a signal processor, a chip, or other integrated circuit that can implement the method of the present application, or a portion of the circuit for processing functions in the aforementioned processor, chip or integrated circuit. In addition, the transceiver circuit 820 may also be a transceiver, or an input / output interface, which is used for input or output of signals or data, and may also be referred to as an input / output circuit.

[0320] When the communication device 800 is a first device, illustratively, the processing circuit 810 is configured to perform the following operations: sending indication information; receiving a first sample, etc.

[0321] When the communication device 800 is the second device, illustratively, the processing circuit 810 is configured to perform the following operations: receive indication information; send a first sample, etc.

[0322] The above contents are merely exemplary descriptions. When the communication device 800 is the first device or the second device, it will be responsible for executing the methods or steps related to the first device or the second device in the above method embodiments.

[0323] When the communication device 800 is a first device or a second device, the transceiver circuit 820 may be a transceiver. When the communication device 800 is a chip for the first device or the second device, the transceiver circuit 820 may be an input / output circuit. The above description is only an example description.

[0324] For details, please refer to the contents of the above method embodiment. The implementation of each operation in Figure 8 can also correspond to the corresponding description of the method embodiment shown in Figures 6 to 8.

[0325] Figure 9 is a schematic block diagram of a communication device 900 according to an embodiment of the present application. The communication device 900 may be a first device or a second device, configured to implement the method according to the above embodiment.

[0326] The communication device 900 includes a transceiver unit 910. The transceiver unit 910 is exemplarily introduced below.

[0327] The transceiver unit 910 may include a transmitting unit and a receiving unit. The transmitting unit is used to perform a transmitting operation of the communication device, and the receiving unit is used to perform a receiving operation of the communication device. For ease of description, this embodiment of the application combines the transmitting unit and the receiving unit into a single transceiver unit. This is described here as a unified description and will not be repeated later.

[0328] When the communication device 900 is a first device, illustratively, the transceiver unit 910 is used to send indication information; and is also used to receive a first sample, etc.

[0329] Optionally, the communication device 900 may further include a processing unit 920, which is configured to execute the content of the first device involving processing, control, etc. For example, the processing unit 920 is configured to determine samples that need to be reported.

[0330] When the communication device 900 is the second device, illustratively, the transceiver unit 910 is used to receive indication information; and is also used to send the first sample, etc.

[0331] Optionally, the communication device 900 may further include a processing unit 920, which is configured to determine samples that need to be reported according to sample reporting rules. The processing unit 920 is configured to execute the content of the second device involving processing, control, and other steps.

[0332] When the communication device 900 is the first device or the second device, it will be responsible for executing one or more of the methods or steps related to the first device or the second device in the aforementioned method embodiments.

[0333] Optionally, the communication device 900 further includes a storage unit 930, which is used to store a program or code for executing the aforementioned method.

[0334] It should be noted that the transceiver unit in FIG. 9 may correspond to the transceiver circuit in FIG. 8 , and the processing unit in FIG. 9 may correspond to the processing circuit in FIG. 8 .

[0335] The device embodiments shown in Figures 8 and 9 are used to implement the content described in Figure 6. The specific execution steps and methods of the devices shown in Figures 8 and 9 can refer to the content described in the above method embodiments.

[0336] The present application also provides a chip including a processor configured to retrieve and execute instructions stored in a memory, so that a communication device equipped with the chip executes the methods described in the above examples. The memory may be integrated within the chip or located outside the chip.

[0337] The present application also provides another chip, comprising: an input interface, an output interface, and a processing circuit, wherein the input interface, the output interface, and the processor are connected via an internal connection path, and the processing circuit is used to execute the code in the memory. When the code is executed, the processing circuit is used to execute the method in each of the above examples. Optionally, the chip also includes a memory, which is used to store computer programs or code. The input interface and the output interface can be independent of each other, or can be integrated into an input and output interface.

[0338] The processing circuit may be all or part of the processing circuits in one or more processors, or one or more processors.

[0339] The present application also provides a processor for coupling with a memory, and for executing the methods and functions involving a network device or a terminal device in any of the above embodiments.

[0340] In another embodiment of the present application, a computer program product including instructions is provided. When the computer program product is run on a computer, the method of the above embodiment is implemented.

[0341] The present application also provides a computer program. When the computer program is executed in a computer, the method of the aforementioned embodiment is implemented.

[0342] In another embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a computer, the method described in the above embodiment is implemented.

[0343] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0344] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0345] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0346] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0347] Those skilled in the art will appreciate that the various exemplary units and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented using hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application. Those skilled in the art will clearly understand that, for ease of description and brevity, the specific operating processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other divisions may be used, such as multiple units or components can be combined or integrated into another system, or some features can be omitted or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other can be through some interface, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0348] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. If the above functions are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the portion that contributes to the prior art, or the portion of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0349] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

Claims

1. A method for information transmission, characterized in that: include: Sending indication information, where the indication information is used to indicate a sample reporting rule, where the sample reporting rule is used by the terminal device to determine samples that need to be reported, where the samples that need to be reported include at least one of the identification information of the beam and the measurement report of the beam; A first sample is received, where the first sample belongs to the sample that needs to be reported.

2. The method according to claim 1, characterized in that The indication information includes identification information corresponding to the first sample.

3. The method according to claim 2, characterized in that Before sending the indication information, the method further includes: Receive multiple samples.

4. The method according to claim 3, characterized in that The indication information is determined according to sample distribution among the multiple samples.

5. The method according to claim 3 or 4, characterized in that: A number of samples in the plurality of samples is greater than a first threshold.

6. The method according to any one of claims 3 to 5, characterized in that The first sample belongs to the multiple samples, and the first sample satisfies at least one of the following: The difference between the proportion of the second sample in the multiple samples and the proportion of the first sample in the multiple samples is greater than or equal to a second threshold, the proportion of the second sample in the multiple samples is higher than the proportion of the first sample in the multiple samples, and the second sample belongs to the multiple samples; or The proportion of the first sample in the multiple samples is less than or equal to a third threshold.

7. The method according to any one of claims 1 to 6, characterized in that The beam is a beam whose received signal quality is greater than a fourth threshold.

8. The method according to any one of claims 1 to 7, characterized in that The beam is a beam of a channel state information reference signal, the first sample includes identification information of the channel state information reference signal, and the first sample is associated with a synchronization signal block corresponding to the terminal device.

9. The method according to any one of claims 1 to 8, characterized in that The types of samples that need to be reported are more than or equal to the types of samples obtained by the terminal device.

10. The method according to any one of claims 1 to 9, characterized in that The samples that need to be reported are used for training the artificial intelligence model, and the artificial intelligence model obtained after training is used for beam management.

11. A method for information transmission, characterized in that: include: Receive indication information, where the indication information is used to indicate a sample reporting rule, where the sample reporting rule is used by the terminal device to determine samples that need to be reported, where the samples that need to be reported include at least one of identification information of the beam and a measurement report of the beam; According to the indication information, a first sample is sent, where the first sample belongs to the sample that needs to be reported.

12. The method according to claim 11, characterized in that The indication information includes identification information corresponding to the first sample.

13. The method according to claim 12, characterized in that Before receiving the indication information, the method further includes: Send at least one sample.

14. The method according to any one of claims 11 to 13, characterized in that The beam is a beam whose received signal quality is greater than a threshold.

15. The method according to any one of claims 12 to 14, characterized in that The beam is a beam of a channel state information reference signal, the first sample includes identification information of the channel state information reference signal, and the first sample is associated with a synchronization signal block corresponding to the terminal device.

16. The method according to any one of claims 11 to 15, characterized in that The types of samples that need to be reported are more than or the same as the types of samples obtained by the terminal device.

17. The method according to any one of claims 11 to 16, characterized in that The samples that need to be reported are used for training the artificial intelligence model, and the artificial intelligence model obtained after training is used for beam management.

18. A communication device, characterized in that: comprising a processing circuit for, by executing a computer program or instructions, or, by hardware circuitry, The communication device is caused to perform the method according to any one of claims 1 to 10; or, The communication device is enabled to execute the method according to any one of claims 11 to 17.

19. A communication device, characterized in that: It includes a logic circuit and an input / output interface, wherein the input / output interface is used to input and / or output signals. The logic circuit is used to execute the method according to any one of claims 1 to 10; or, The logic circuit is configured to execute the method according to any one of claims 11 to 17.

20. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program or instruction. When the computer program or instruction is executed on a computer, so that the method of any one of claims 1 to 10 is performed; or, The method according to any one of claims 11 to 17 is performed.

21. A computer program product, characterized in that Contains instructions that, when executed on a computer, so that the method of any one of claims 1 to 10 is performed; or, The method according to any one of claims 11 to 17 is performed.

22. A chip system, characterized in that: include: A processor, wherein the processor is used to execute a computer program or instruction in a memory so that the chip system implements the method according to any one of claims 1 to 10; or The chip system is enabled to implement the method according to any one of claims 11 to 17.

23. A communication device, characterized in that: The method comprises a module for executing the method of any one of claims 1 to 10, or comprises a module for executing the method of any one of claims 11 to 17.

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