Communication method and communication device

Abstract

The embodiment of the invention provides a communication method and a communication device. The method comprises the steps that N anchor points are obtained, one anchor point in the N anchor points comprises one or more pieces of reference data, and N is larger than or equal to 1; and transmitting at least one index of M anchor points with the smallest difference from the first data, wherein M is less than or equal to N and M is greater than or equal to 1. In the method, a BS or a UE may transmit the index of the M anchor points with a minimum difference from the first data to the receiver, which instructs the BS or the UE to implement an active switching of a model or mode.

Description

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 507,854, filed on June 13, 2023, entitled “AI MODEL SWITCH BY DATA ANCHOR”.

[0002] The entire disclosure of the above application is incorporated herein by reference. TECHNICAL FIELD

[0003] Embodiments of the present application relate to the field of communication, and more particularly to a communication method and a communication apparatus. BACKGROUND

[0004] AI-based algorithms have been introduced into modern wireless communications to solve wireless problems such as channel estimation, scheduling, channel state information (CSI) compression (from user equipment to base station), Multiple-In Multiple-Out (MIMO) beamforming, positioning, etc. As a data-driven method, AI-based algorithms inevitably have the problem of low generalization ability. The performance of an artificial intelligence (AI) model depends only on the goodness of the data that trains them. Even if an AI model is trained on a large dataset, it can not have the necessary knowledge to perform effectively in other environments, especially in wireless communications where channel information changes rapidly.

[0005] Due to the generalization problem, a user equipment (UE) or a base station (BS) needs to detect its generalization performance and then switch to a suitable AI model. For example, there are multiple AI models for multiple scenarios, including urban indoor, urban outdoor, rural, high-speed rail, etc. When the surrounding environment changes, the BS or UE needs to switch to another model. In the prior art, when the inference performance drops, the UE or BS will switch its AI model or fall back to a non-AI mode. This is a passive solution.

[0006] Therefore, how to perform AI model switching in advance is a technical problem to be solved. SUMMARY

[0007] Embodiments of the present application provide a communication method and a communication apparatus. In the technical solution of the present application, the BS or UE can actively perform model or mode switching after the surrounding environment changes.

[0008] According to a first aspect, the embodiments of the present application provide a communication method, comprising: obtaining N anchor points, one anchor point in the N anchor points comprising one or more reference data, N≥1; and sending at least one index of M anchor points with the smallest difference from first data, M≤N and M≥1.

[0009] In the communication method provided by the present application, the BS or the UE can send the index of the M anchor points with the smallest difference from the first data to the receiver, and the receiver indicates the BS or the UE to actively switch the model or the mode.

[0010] The first data comprises monitoring data or measurement data of the user equipment or the network equipment. Further, the first data is monitoring data or measurement data related to the AI model. The network equipment in the embodiment can be a base station (BS). If the first data is data sent by the UE to the BS in uplink, the data is monitoring or measurement data of the UE. If the first data is data sent by the BS to the UE in downlink, the data is monitoring or measurement data of the BS.

[0011] The M anchor points are M anchor points with the smallest difference from the first data in the N anchor points. Each anchor point in the N anchor points corresponds to a configured AI model. M and N are integers greater than or equal to 1.

[0012] Exemplarily, the UE can report the index of the anchor point with the smallest difference from the first data to the BS, for example, the first smallest, the second smallest, and the Mth smallest. M can be configured by the BS. Exemplarily, the BS can indicate the index of the anchor point with the smallest difference from the first data to the UE, for example, the first smallest, the second smallest, and the Mth smallest. M can be reported by the UE.

[0013] The anchor point with the smallest difference from the first data in the N anchor points is the nearest anchor point to the BS or the UE. The UE or the BS reports the index of the nearest anchor point, which can be periodic, semi-static, or aperiodic. The reporting can be performed on a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH).

[0014] In a possible implementation scenario, if the BS assists the UE in model switching, the BS can configure N anchor points for the UE. In a possible implementation scenario, if the UE assists the BS in model switching, the UE can report N anchor points to the BS. The configuration signal or the reporting signal can be a radio resource control (RRC), a medium access control-control element (MAC-CE), or a downlink control information (DCI), and can be broadcast, groupcast, or unicast.

[0015] According to the report of the UE, the BS can assist the UE in model switching. When there are multiple AI / ML models on the UE side, the BS assists the UE in model switching or switching to a non-AI mode. When there is only one AI model on the UE side, the BS assists the UE in switching between the AI mode and the non-AI mode.

[0016] In a possible implementation, the M first difference values corresponding to the M anchor points are the smallest M of the N first difference values, and the n th first difference value in the N first difference values is a difference value between the first data and an n th anchor point in the N anchor points, 1≤n≤N.

[0017] In the communication method provided in the present application, the BS or the UE can send the indexes of the M anchor points with the smallest difference values from the first data to the receiver, and the receiver instructs the BS or the UE to implement active switching of the model or mode.

[0018] In a possible implementation, the first difference value corresponding to the n th anchor point is a minimum value or an average value of K second difference values corresponding to the n th anchor point, the n th anchor point includes K reference data, and the j th second difference value in the K second difference values is a difference value between the first data and the j th reference data in the K reference data, K≥1, 1≤j≤K and 1≤n≤N.

[0019] The n th anchor point is a set of reference data, for example, reference coefficients (f n = (f n,1, f n,2, …, f n,K) T, n = 1, 2, …, N) The reference data (f n = (f n,1, f n,2, …, f n,K) T, n = 1, 2, …, N) ) can be a vector, for example, a one-dimensional array, where the size of the vector is r, and r is predefined or configured. The size of the set is K (f n = (f n,1, f n,2, …, f n,K) T, n = 1, 2, …, N) , j = 1, 2, …, K), where K is predefined or configured. The n th anchor point includes K reference coefficients.

[0020] The second difference value can be calculated by one of the following equations: 、 and . is the difference between the first data and the reference data in the nth anchor point. is the first data. is the jth reference data in the nth anchor point. Denotes inner product. denotes norm, which is a method of measuring the size of a vector, matrix, tensor or function. denotes other custom functions, , The difference between the first data and the reference data can also be calculated by dot product, Euclidean distance or DNN-based algorithm, etc. The specific calculation should not be understood as a limitation of the present application.

[0021] The first difference can be calculated by one of the following equations: and . is the difference between the first data and the nth anchor. may be the minimum value of the difference between the first data and the K reference data in the nth anchor point may be the average value of the difference between the first data and the K reference data in the nth anchor point .

[0022] Alternatively, the difference between the first data and the nth anchor point can also be obtained by mutual information, Hilbert-Schmidt independence criterion (HSIC) metric, Kullback-Leibler (KL) divergence, graph edit distance, Wasserstein distance, Jensen-Shannon divergence (JSD) distance, DNN-based algorithm, etc.

[0023] In the communication method provided by the present application, the BS or UE can send the index of the M anchor points with the smallest difference from the first data to the receiver, and the receiver instructs the BS or UE to actively switch the model or mode.

[0024] In a possible implementation, the method further includes: sending a third difference, the third difference being the difference between the first anchor point and the first data, and the first anchor point being the anchor point with the smallest difference from the first data among the N anchor points.

[0025] ​When the difference between the first data and the nearest anchor point is greater than the threshold value, the optimal AI / ML model can not work, so the UE can report the third difference to the BS. According to the report of the UE, the BS can instruct the UE to switch to other models or fallback to a non-AI mode. Similarly, the BS can report the third difference to the UE. According to the report of the BS, the UE can instruct the BS to switch to other models or fallback to a non-AI mode. In the embodiments of the present application, the BS is allowed to provide additional assistance information to the UE for active switching.

[0026] In a possible implementation, the method further includes: sending a first message or a second message, the first message being used to indicate that the anchor point with the minimum difference from the first data has not changed in a time period, and the second message being used to indicate that the anchor point with the minimum difference from the first data has changed in a time period.

[0027] If the nearest anchor point index has no change compared with the previous value, the UE reports to the BS that it is the same as the previous one, for example, using 1 bit to indicate whether there is a change, and value 1 represents a change and value 0 represents the same. If the nearest anchor point index changes, the UE can also report the nearest anchor point index to the BS. Another reporting scheme is event-triggered reporting. Only when the nearest anchor point index changes, the UE will report the nearest anchor point index to the BS. The BS sends the first message and the second message to the UE in the same way, which will not be described herein.

[0028] In the communication method provided by the present application, by reporting the first message or the second message, the air interface overhead is reduced.

[0029] In a possible implementation, sending at least one index of the M anchor points with the minimum difference from the first data includes: when it is determined that the anchor point with the minimum difference from the first data has changed in the time period, sending at least one index of the M anchor points with the minimum difference from the first data.

[0030] In the communication method provided by the present application, the index value of the M anchor points is event-triggered reporting, and the air interface overhead is reduced.

[0031] In a possible implementation, the method further includes: receiving a third message; and switching to other AI models or a non-AI mode according to the third message.

[0032] When the UE sends the index of the M anchor points with the minimum difference from the first data to the BS, the BS can send a third message to the UE, instructing the UE to switch to other AI models or a non-AI mode. When the BS sends the index of the M anchor points with the minimum difference from the first data to the UE, the UE can send a third message to the BS, instructing the BS to switch to other AI models or a non-AI mode.

[0033] In the communication method provided in the present application, the BS or the UE can send the index of the M anchor points with the smallest difference from the first data to the receiver, and the receiver instructs the BS or the UE to actively switch to a model or a mode.

[0034] In a possible implementation, switching to other AI models or a non-AI mode according to the third message includes: switching to a first AI model corresponding to a first anchor point according to the third message, the first anchor point being an anchor point with the smallest difference from the first data among the N anchor points.

[0035] In a possible application scenario, the BS sends the third message to the UE, instructing the UE to switch the model. The UE receives the third message and switches the model to a first AI model corresponding to an anchor point with the smallest difference from the first data.

[0036] In the communication method provided in the present application, the BS or the UE can send a third message to instruct the receiver to switch the model, thereby realizing active model switching.

[0037] In a possible implementation, the third message includes an index of a second AI model, and switching to other AI models or a non-AI mode according to the third message includes: switching to the second AI model according to the index of the second AI model.

[0038] In a possible application scenario, the BS can instruct the UE to switch to a specified AI model. The third message includes an index of a second AI model, and the UE receives the third message and switches the model to the second AI model according to the third message.

[0039] In the communication method provided in the present application, the BS or the UE can send a third message to instruct the receiver to switch the model, thereby realizing active model switching.

[0040] In a possible implementation, the method further includes: sending a fourth message, the fourth message being used to indicate that a third difference is greater than a predetermined threshold, the third difference being a difference between the first data and a first anchor point, the first anchor point being an anchor point with the smallest difference from the first data among the N anchor points.

[0041] In a possible application scenario, if the fourth difference is greater than the predetermined threshold, the UE can not send the index of the anchor point to the BS, but send a fourth message to the BS, indicating that the fourth difference is greater than the predetermined threshold.

[0042] In the communication method provided in the present application, the UE or the BS can send a fourth message to indicate that a difference between the first data and a nearest anchor point is greater than a predetermined threshold, and then switch to a non-AI mode.

[0043] In a possible implementation, the fourth message includes an invalid anchor point index.

[0044] For example, the effective anchor point index is 0 to N-1. The index value of the anchor point in the fourth message sent by the UE is N, which means that the difference between any anchor point and the first data is less than a predetermined threshold.

[0045] In one possible implementation, the fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

[0046] For example, a specified bit in the fourth message can be used to indicate whether the UE has switched to non-AI mode. If this specified bit is 1, it indicates that the UE has switched to non-AI mode.

[0047] In one possible implementation, the value of M is predefined or configured.

[0048] In one possible implementation, one of the N anchor points corresponds to an AI model and an index.

[0049] The association between an anchor point and an AI model can be determined implicitly or configured explicitly. For example, the association between an anchor point and an AI / ML model is implicitly determined by giving the anchor point index the same value as the model index, i.e., {model index k, anchor index k}. For example, the association between an anchor point and an AI model is explicitly configured, i.e., {model index k, anchor index j}.

[0050] In the communication method provided in this application, the BS or UE can send the index of the M anchor points with the smallest difference between the first data and the receiver, and the receiver instructs the BS or UE to perform active switching of the model or mode.

[0051] In one possible implementation, the first data includes monitoring or measurement data from user equipment or network equipment.

[0052] In one possible implementation, the first data includes filtered measurement data.

[0053] The first data can be the raw measurement data or the measured quantity (measurement data) filtered by a Layer 3 filter. For Layer 3 filtering, the UE first uses the equation for each measurement quantity... The measurements are filtered before being used to evaluate reporting criteria or measurement reports. This applies to the Measurement Objects (MeasObjectNR) for Generation V New Radio. Where ki is the filter coefficient for the corresponding measurement of the i-th QuantityConfigNR in the quantityConfigNR-List, and i is indicated by QuantityConfigIndex. For other measurements, , where k is the filter coefficient of the corresponding measurement received by quantityConfig.

[0054] In one possible implementation, the indices of the M anchor points are sent via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0055] In one possible implementation, the N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

[0056] In one possible implementation, the method is executed by a user device or a network device.

[0057] The above describes the method provided in this application for the way the UE sends information to the BS. The process of the BS sending information to the UE is similar to the above process, and will not be described again in this application.

[0058] According to a second aspect, embodiments of this application provide a communication method, comprising: receiving at least one index of M anchor points, wherein the M anchor points are the M anchor points with the smallest difference from first data among N anchor points, and one of the M anchor points includes one or more reference data, N≥1, M≤N and M≥1; and sending a third message, wherein the third message is used to instruct the receiver to switch an AI model or mode.

[0059] In one possible implementation, the M first differences corresponding to the M anchor points are the M smallest of the N first differences, and the nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, 1≤n≤N.

[0060] In one possible implementation, the first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The j-th second difference among the K second differences is the difference between the first data and the j-th reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

[0061] In one possible implementation, the communication method further includes receiving a third difference, which is the difference between a first anchor point and first data, wherein the first anchor point is the anchor point with the smallest difference between itself and the first data among N anchor points.

[0062] In one possible implementation, the communication method further includes receiving a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

[0063] In one possible implementation, the third message is used to instruct the receiver to switch to the first AI model corresponding to the first anchor point, which is the anchor point with the smallest difference from the first data among the M anchor points.

[0064] In one possible implementation, the third message includes an index of the second AI model, and the third message is used to instruct the receiver to switch to the second AI model.

[0065] In one possible implementation, the communication method further includes receiving a fourth message, the fourth message indicating that a third difference is greater than a predetermined threshold, the third difference being the difference between the first data and the first anchor point, and the first anchor point being the anchor point with the smallest difference between the first data and the N anchor points.

[0066] In one possible implementation, the fourth message includes an invalid anchor index.

[0067] In one possible implementation, the fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

[0068] In one possible implementation, the value of M is predefined or configured.

[0069] In one possible implementation, one of the N anchor points corresponds to an AI model and an index.

[0070] In one possible implementation, the first data includes monitoring or measurement data from user equipment or network equipment.

[0071] In one possible implementation, the first data includes filtered measurement data.

[0072] In one possible implementation, the indices of the M anchor points are sent via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0073] In one possible implementation, the N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

[0074] In one possible implementation, the method is executed by a user device or a network device.

[0075] The beneficial effects of the second aspect are explained in the first aspect. They will not be repeated here.

[0076] According to a third aspect, this application provides a communication device, comprising: an acquisition module for acquiring N anchor points, wherein one of the N anchor points includes one or more reference data, and N≥1; and a transmission module for transmitting at least one index of the M anchor points that differs from the first data the least, wherein M≤N and M≥1.

[0077] In one possible implementation, the M first differences corresponding to the M anchor points are the M smallest of the N first differences, and the nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, 1≤n≤N.

[0078] In one possible implementation, the first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The j-th second difference among the K second differences is the difference between the first data and the j-th reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

[0079] In one possible implementation, the sending module is also used to send a third difference, which is the difference between the first anchor point and the first data. The first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0080] In one possible implementation, the sending module is further configured to send a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

[0081] In one possible implementation, the sending module is further configured to send at least one index of the M anchor points that differ the least from the first data when it is determined that the anchor point with the smallest difference from the first data has changed during the time period.

[0082] In one possible implementation, the acquisition module is also used to receive a third message; the communication device also includes a processing module for switching to other AI models or non-AI modes based on the third message.

[0083] In one possible implementation, the processing module is also used to switch to the first AI model corresponding to the first anchor point based on the third message. The first anchor point is the anchor point with the smallest difference from the first data among N anchor points.

[0084] In one possible implementation, the third message includes an index of the second AI model, and the processing module is also used to switch to the second AI model based on the index of the second AI model.

[0085] In one possible implementation, the sending module is further configured to send a fourth message, which indicates that a third difference is greater than a predetermined threshold. The third difference is the difference between the first data and the first anchor point, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0086] In one possible implementation, the fourth message includes an invalid anchor index.

[0087] In one possible implementation, the fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

[0088] In one possible implementation, the value of M is predefined or configured.

[0089] In one possible implementation, one of the N anchor points corresponds to an AI model and an index.

[0090] In one possible implementation, the first data includes monitoring or measurement data from user equipment or network equipment.

[0091] In one possible implementation, the first data includes filtered measurement data.

[0092] In one possible implementation, the indices of the M anchor points are sent via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0093] In one possible implementation, the N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

[0094] In one possible implementation, the device is located on a user equipment or network device.

[0095] According to a fourth aspect, this application provides a communication device, comprising: a receiving module for receiving at least one index of M anchor points, wherein the M anchor points are the M anchor points with the smallest difference from first data among N anchor points, and one of the M anchor points includes one or more reference data, where N≥1, M≤N and M≥1; and a sending module for sending a third message, wherein the third message is used to instruct the receiver to switch an AI model or mode.

[0096] In one possible implementation, the M first differences corresponding to the M anchor points are the M smallest of the N first differences, and the nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, 1≤n≤N.

[0097] In one possible implementation, the first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The j-th second difference among the K second differences is the difference between the first data and the j-th reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

[0098] In one possible implementation, the receiving module is also used to receive a third difference, which is the difference between the first anchor point and the first data. The first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0099] In one possible implementation, the receiving module is further configured to receive a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

[0100] In one possible implementation, the third message is used to instruct the receiver to switch to the first AI model corresponding to the first anchor point, which is the anchor point with the smallest difference from the first data among the M anchor points.

[0101] In one possible implementation, the third message includes an index of the second AI model, and the third message is used to instruct the receiver to switch to the second AI model.

[0102] In one possible implementation, the receiving module is further configured to receive a fourth message, which indicates that a third difference is greater than a predetermined threshold. The third difference is the difference between the first data and the first anchor point, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0103] In one possible implementation, the fourth message includes an invalid anchor index.

[0104] In one possible implementation, the fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

[0105] In one possible implementation, the value of M is predefined or configured.

[0106] In one possible implementation, one of the N anchor points corresponds to an AI model and an index.

[0107] In one possible implementation, the first data includes monitoring or measurement data from user equipment or network equipment.

[0108] In one possible implementation, the first data includes filtered measurement data.

[0109] In one possible implementation, the indices of the M anchor points are sent via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0110] In one possible implementation, the N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

[0111] In one possible implementation, the device is located on a user equipment or network device.

[0112] According to a fifth aspect, a communication device is provided, including a processor and a memory. The processor is connected to the memory. The memory is used to store instructions, and the processor is used to execute the instructions. When the processor executes the instructions stored in the memory, the processor is capable of performing the methods in any possible implementation of the first or second aspect described above.

[0113] According to the sixth aspect, this application provides a communication system, including a communication device in any possible implementation of the third aspect and a communication device in any possible implementation of the fourth aspect.

[0114] According to a seventh aspect, this application provides a computer-readable storage medium including instructions. When the instructions are executed on a processor, the processor is capable of performing the methods in any possible implementation of the first or second aspect described above.

[0115] According to an eighth aspect, this application provides a computer program product including computer program code. When the computer program code is run on a computer, the computer is able to perform the methods in any possible implementation of the first or second aspect described above.

[0116] It should be noted that all or part of the aforementioned computer program code can be stored in the first storage medium. The first storage medium can be packaged together with the processor or packaged separately from the processor.

[0117] According to a ninth aspect, this application provides a chip system including a memory and a processor. The memory is used to store a computer program, and the processor is used to retrieve and run the computer program from the memory, causing an electronic device equipped with the chip system to perform the methods in any possible implementation of the first or second aspect described above. Attached Figure Description

[0118] Figure 1 This is a schematic diagram of a communication system according to an embodiment of this application.

[0119] Figure 2 This is a schematic diagram of a communication system 100 according to an embodiment of this application.

[0120] Figure 3 This is a schematic diagram of ED 110 and base stations 170a, 170b and / or 170c according to an embodiment of this application.

[0121] Figure 4 This is a schematic diagram of a unit or module in the device according to an embodiment of this application.

[0122] Figure 5 This is a schematic diagram of an AI-based communication device.

[0123] Figure 6This is a schematic diagram of device 500 receiving reference data samples from device 600 in an embodiment of this application.

[0124] Figure 7 This is a schematic diagram of a reference data sample including multiple groups, according to an embodiment of this application.

[0125] Figure 8 This is a schematic diagram of an approximation based on DNN in an embodiment of this application.

[0126] Figure 9 This is a flowchart of a communication method according to an embodiment of this application.

[0127] Figure 10 This is a flowchart of a communication method according to an embodiment of this application.

[0128] Figure 11 This is a schematic diagram of projecting a high-dimensional signal onto a low-dimensional signal according to an embodiment of this application.

[0129] Figure 12 This is a flowchart of a communication method according to an embodiment of this application.

[0130] Figure 13 This is a schematic diagram of the determination matrix U in an embodiment of this application.

[0131] Figure 14 This is a schematic diagram of the first sampling matrix P1 in an embodiment of this application.

[0132] Figure 15 This is a schematic diagram of the sampling matrix compression matrix U in an embodiment of this application.

[0133] Figure 16 This is a schematic diagram of the scoring distance in the low-frequency space according to an embodiment of this application.

[0134] Figure 17 This is a schematic diagram of an embodiment of the communication method of this application.

[0135] Figure 18 This is a schematic diagram of the BS instructing the UE to perform model switching according to an embodiment of this application.

[0136] Figure 19 This is a schematic diagram of another BS instructing the UE to perform model switching in an embodiment of this application.

[0137] Figure 20 This is a schematic diagram of another BS instructing the UE to perform model switching in an embodiment of this application.

[0138] Figure 21 This is a schematic diagram of a UE instructing a BS to perform model switching according to an embodiment of this application.

[0139] Figure 22This is a schematic block diagram of a communication device according to an embodiment of this application.

[0140] Figure 23 This is a schematic block diagram of another communication device according to an embodiment of this application.

[0141] Figure 24 This is a schematic block diagram of another communication device according to an embodiment of this application. Detailed Implementation

[0142] The technical solutions in this application are described below with reference to the accompanying drawings.

[0143] The technical solutions in this application will now be described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this application.

[0144] This application will provide aspects, embodiments, or features relating to systems comprising multiple devices, components, modules, etc. It should be understood and recognized that individual systems may include additional devices, components, modules, etc., and / or may exclude all devices, components, modules, etc. discussed in conjunction with the accompanying drawings. Furthermore, combinations of these options may also be used.

[0145] Furthermore, in the embodiments of this application, the terms "exemplarily" and the phrase "for example" are used to indicate illustration or description, etc. Any embodiment or design described as "exemplarily" in this application should not be construed as superior to or more advantageous than other embodiments or designs. Rather, the term "example" is used to present the concept in a particular manner.

[0146] Unless otherwise specified, the phrases "in some possible embodiments," "in some possible application scenarios," etc., appearing in different places in this specification do not necessarily refer to the same embodiment, but rather to "one or more, but not all, embodiments." Unless otherwise specifically specified, the terms "comprising," "including," "having," and variations thereof mean "including but not limited to."

[0147] In this application, "at least one" means one or more, and "more than" means two or more. "And / or" describes the association of related objects, indicating that three relationships can exist. For example, A and / or B can mean only A, both A and B, or only B, where A and B can be singular or plural. The character " / " typically indicates that the preceding and following related objects are in an "OR" relationship.

[0148] The application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. Those skilled in the art will understand that, with the evolution of system architecture and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0149] The technical solutions of this application embodiment can be applied to various communication systems, such as Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA) system, Wideband Code Division Multiple Access (WCDMA) system, General Packet Radio Service (GPRS) system, Long Term Evolution (LTE) system, LTE Frequency Division Duplex (FDD) system, LTE Time Division Duplex (TDD) system, Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) system, Wireless Local Area Network (WLAN), Fifth Generation (5G) wireless communication system, New Radio (NR) wireless communication system, Sixth Generation (6G) wireless communication system, or other evolved communication systems.

[0150] To better describe the solutions of the embodiments of this application, the concepts and terms that may be involved in this application will be described below.

[0151] (1) Data collection Data is a crucial component of artificial intelligence (AI) and machine learning (ML) technologies. Data collection refers to the process by which network nodes, management entities, or user experiences (UX) gather data for AI / ML model training, data analysis, and inference.

[0152] (2) AI / ML model training AI / ML model training refers to the process of training an AI / ML model by learning the input / output relationship in a data-driven manner and then using the trained AI / ML model for inference.

[0153] (3) AI / ML model inference The process of using a trained AI / ML model to produce a set of outputs from a set of inputs.

[0154] (4) AI / ML model validation As a sub-process of training, validation is used to evaluate the quality of the AI / ML model using a different dataset than the one used for model training. Validation can help in selecting model parameters that generalize well beyond the dataset used for model training. The trained model parameters can be further tuned through the validation process.

[0155] (5) AI / ML model testing Similar to validation, testing is also a sub-process of training. It is used to evaluate the performance of the final AI / ML model using a different dataset than the datasets used for model training and validation. Unlike AI / ML model validation, testing does not assume subsequent model tuning.

[0156] (6) Online training Online training refers to an AI / ML training process in which the model used for inference is typically trained continuously in (near) real-time as new training samples arrive.

[0157] (7) Offline training Offline training refers to an AI / ML training process in which a model is trained on a collected dataset and then used or delivered for inference.

[0158] (8) AI / ML model delivery / transfer AI / ML model delivery / transfer is a general term referring to the delivery of an AI / ML model from one entity to another in any way. Delivering an AI / ML model over an air interface includes providing parameters of the model structure known to the receiving end, as well as providing a new model with parameters. Delivery can include a complete model or a partial model.

[0159] (9) Life cycle management (LCM) When training and / or inferring AI / ML models on a device, the entire AI / ML process needs to be monitored and managed to ensure the performance gains achieved through AI / ML technology. For example, due to the randomness of wireless channels and the mobility of UEs, the propagation environment of wireless signals changes frequently. However, it is difficult for AI / ML models to maintain optimal performance in all scenarios, and performance may even degrade sharply in some scenarios. Therefore, lifecycle management (LCM) of AI / ML models is crucial for the sustainable operation of AI / ML over the NR air interface. Lifecycle management covers the entire process of applying AI / ML technology on one or more nodes. Specifically, lifecycle management includes at least one of the following sub-processes: data collection, model training, model identification, model registration, model deployment, model configuration, model inference, model selection, model activation, deactivation, model switching, model rollback, model monitoring, model update, model transmission / delivery, and UE capability reporting. Model monitoring can be based on inference accuracy, including metrics related to key performance indicators (KPIs), or on system performance, including metrics related to system performance KPIs, such as accuracy and relevance, overhead, complexity (computational and memory costs), latency (timeliness of monitoring results, from model failure to recovery), and power consumption. Furthermore, data distribution may change after deployment due to environmental variations; therefore, models based on input or output data distribution should also be considered.

[0160] (10) Supervised learning The goal of supervised learning algorithms is to train a model that maps feature vectors (inputs) to labels (outputs) based on training data that includes example feature-label pairs. Supervised learning analyzes the training data and generates an inference function that can be used to map inference data. Supervised learning can be further divided into two types: classification and regression. Classification is used when the output of the AI / ML model is categorical data, i.e., it has two or more classes. Regression is used when the output of the AI / ML model is real numbers or continuous values.

[0161] (11) Unsupervised learning Unlike supervised learning, where AI / ML models learn to map inputs to target outputs, unsupervised methods learn concise representations of input data without labeled data. These representations can be used for data exploration, analysis, or the generation of new data. A typical example of unsupervised learning is clustering, which explores the hidden structure of the input data and provides classification results.

[0162] (12) Reinforcement learning Reinforcement learning is used to solve sequential decision-making problems. It is the process of training an agent's actions based on inputs (states) and feedback signals (rewards) from the environment. In reinforcement learning, the agent interacts with the environment by performing actions to maximize cumulative rewards. Each time the agent performs an action, the current state of the environment may transition to a new state, which in turn brings a corresponding reward. The agent can then perform the next action based on the received reward and the new state in the environment. During the training phase, the agent interacts with the environment to accumulate experience. Because direct interaction with real systems is costly, the environment is typically simulated by a simulator. During the inference phase, the agent can use the optimal decision rules learned from the training phase to achieve the maximum cumulative reward.

[0163] (13) Federal Learning Federated learning (FL) is a machine learning technique used to train AI / ML models by a central node (e.g., a server) and multiple distributed edge nodes (e.g., UEs, next-generation NodeBs (gNBs)). According to wireless FL technology, the server can provide edge nodes with a set of model parameters (e.g., weights, biases, gradients) describing the global AI / ML model. Edge nodes can use the received global AI / ML model parameters to initialize their local AI / ML models. The edge nodes can then use local data samples to train their local AI / ML models, resulting in trained local AI / ML models. Subsequently, the edge nodes can provide the server with a set of AI / ML model parameters describing their local AI / ML models. After receiving multiple sets of AI / ML model parameters describing the corresponding local AI / ML models at multiple edge nodes, the server can aggregate the local AI / ML model parameters reported from multiple UEs and update the global AI / ML model based on this aggregation. Subsequent iterations proceed very similarly to the first iteration. The server can then send the aggregated global model to multiple edge nodes. The above process involves multiple iterations until a global AI / ML model is finally determined, for example, when the AI / ML model converges or meets the training stopping criterion. It's important to note that wireless FL technology does not involve the exchange of local data samples. In fact, local data samples are retained at the corresponding edge nodes.

[0164] AI-based algorithms have been introduced into modern wireless communications to solve wireless problems such as channel estimation, scheduling, channel state information (CSI) compression (from user equipment to base station), multiple-in-multiple-out (MIMO) beamforming, and localization. AI algorithms are a data-driven approach that tunes some predefined architecture using a set of data samples called a training dataset. Recent AI methods use the SGD algorithm to set up neurons to train DNN architectures (including CNNs, RNNs, Transformers, etc.).

[0165] AI technologies (including ML technologies) in communications encompass AI-based communication at the physical layer and / or the MAC layer. At the physical layer, AI communication can aim to optimize component design and / or improve algorithm performance. At the MAC layer, AI / ML-based communication can leverage AI / ML capabilities to learn, predict, and / or make decisions to solve complex optimization problems using potentially better strategies and / or optimal solutions. This includes optimizing features in the MAC layer such as intelligent TRP management, intelligent beam management, intelligent channel resource allocation, intelligent power control, intelligent spectrum utilization, intelligent modulation and coding scheme (MCS), intelligent hybrid automatic repeat request (HARQ) strategies, and intelligent transmit / receive (Tx / Rx) mode adaptation.

[0166] AI architectures can include multiple nodes, which may be organized in either a centralized or distributed mode, both of which can be deployed in access networks, core networks, edge computing systems, or third-party networks. Centralized training and computing architectures are limited by potentially high communication overhead and strict user data privacy. Distributed training and computing architectures can include several frameworks, such as distributed machine learning and federated learning. In some embodiments, the AI ​​architecture may include an intelligent controller, which can perform as a single agent or multiple agents based on joint optimization or individual optimization. New protocols and signaling mechanisms are needed to allow corresponding interface links to be personalized with custom parameters to meet specific needs, while minimizing signaling overhead and maximizing the overall system spectral efficiency through personalized AI technologies.

[0167] The new protocols and signaling mechanisms are provided to operate within and switch between different operating modes, including switching between AI and non-AI modes, and also to provide measurement and feedback to accommodate different possible measurements and information that may require feedback, depending on the implementation.

[0168] Nowadays, it's common for neural network models to become larger and deeper, easily requiring significantly more computing resources than just one or two computers. Most neural network models are trained on powerful computing clouds. Users with the desired neural network architecture, original training dataset, and training objectives may not have sufficient local computing resources to train their models locally. To access a powerful computing cloud, users must completely send all specifications of their neural network architecture, training dataset, and training objectives to the cloud. This requires users to trust the cloud and fully authorize it to manipulate their intellectual property (neural network architecture, training dataset, and training objectives).

[0169] As a data-driven approach, AI-based algorithms inevitably suffer from low generalization ability: if the test data samples are outliers in the training dataset, the neural network will be unable to make good inferences about the test data samples. Even if the AI ​​model is trained on a large dataset, it may not have the necessary knowledge to perform effectively in other environments, especially in wireless communications where channel information changes rapidly.

[0170] In this application, the AI ​​model is exemplified by DNN, i.e., a deep neural network or network. Specific AI models should not be construed as limiting this application.

[0171] Figure 1 This is a schematic diagram of a communication system according to an embodiment of this application.

[0172] refer to Figure 1As a non-limiting illustrative example, a simplified schematic diagram of a communication system is provided. Communication system 100 includes a radio access network 120. Radio access network 120 may be a next-generation (e.g., sixth-generation, 6G, or later) radio access network, or a traditional (e.g., 5G, 4G, 3G, or 2G) radio access network. One or more electronic devices (EDs) 110a to 120j (generally referred to as 110) may be interconnected with each other or connected to one or more network nodes (170a, 170b, generally referred to as 170) in radio access network 120. Core network 130 may be part of the communication system and may depend on or be independent of the radio access technology used in communication system 100. Furthermore, communication system 100 includes a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160.

[0173] Figure 2 This is a schematic diagram of a communication system 100 according to an embodiment of this application.

[0174] Figure 2 An example communication system 100 is illustrated. Generally, communication system 100 enables multiple wireless or wired components to transmit data and other content. The purpose of communication system 100 may be to provide content such as voice, data, video, and / or text via broadcast, multicast, and unicast. Communication system 100 can operate by sharing resources (e.g., carrier spectrum bandwidth) among its constituent units. Communication system 100 may include terrestrial communication systems and / or non-terrestrial communication systems. Communication system 100 can provide a wide range of communication services and applications (e.g., earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, automated delivery and mobility, etc.). Communication system 100 can provide high availability and robustness through the joint operation of terrestrial and non-terrestrial communication systems. For example, integrating a non-terrestrial communication system (or components thereof) into a terrestrial communication system can enable heterogeneous networks comprising multiple layers. Compared to traditional communication networks, heterogeneous networks can achieve better overall performance through efficient multi-link joint operation, more flexible function sharing, and faster physical layer link switching between terrestrial and non-terrestrial networks.

[0175] Terrestrial and non-terrestrial communication systems can be considered as subsystems of a communication system. In the example shown, communication system 100 includes electronic devices (EDs) 110a to 110d (generally referred to as ED 110), radio access networks (RANs) 120a and 120b, a non-terrestrial communication network 120c, a core network 130, a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160. RANs 120a and RAN 120b include corresponding base stations (BSs) 170a and 170b, which can generally be referred to as terrestrial transmit and receive points (T-TRPs) 170a and 170b. The non-terrestrial communication network 120c includes access nodes 120c, which can generally be referred to as non-terrestrial transmit and receive points (NT-TRPs) 172.

[0176] Alternatively or additionally, any ED 110 can be used to connect to, access, or communicate with any other T-TRP 170a and 170b, NT-TRP 172, Internet 150, core network 130, PSTN 140, other network 160, or any combination thereof. In some examples, ED 110a can communicate uplink and / or downlink with T-TRP 170a via interface 190a. In some examples, ED 110a, ED 110b, and ED 110d can also communicate directly with each other via one or more side-channel air interfaces 190b. In some examples, ED 110d can communicate uplink and / or downlink with NT-TRP 172 via interface 190c.

[0177] Air interfaces 190a and 190b can use similar communication technologies, such as any applicable wireless access technology. For example, communication system 100 can implement one or more channel access methods in air interfaces 190a and 190b, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or single-carrier FDMA (SC-FDMA). Air interfaces 190a and 190b can utilize other higher-dimensional signal spaces, which may involve combinations of orthogonal and / or non-orthogonal dimensions. Air interface 190c enables communication between ED 110d and one or more NT-TRPs 172 via a wireless link or simply through a link. In some examples, the link is a dedicated connection for unicast transmission, a connection for broadcast transmission, or a connection between a group of EDs and one or more NT-TRPs for multicast transmission.

[0178] RAN 120a and RAN 120b communicate with core network 130 to provide various services, such as voice, data, and other services, to ED 110a, ED 110b, and ED 110c. RAN 120a and RAN 120b and / or core network 130 may communicate directly or indirectly with one or more other RANs (not shown), which may or may not be directly served by core network 130, and may or may not use the same radio access technology as RAN 120a, RAN 120b, or both. Core network 130 may also serve as a gateway access between (i) RAN 120a and RAN 120b or ED 110a, ED 110b, and ED 110c, or both, and (ii) other networks (e.g., PSTN 140, Internet 150, and other networks 160). Additionally, some or all of ED 110a, ED 110b, and ED 110c may include the ability to communicate with different wireless networks via different wireless links using different wireless technologies and / or protocols. ED 110a, ED 110b, and ED 110c may communicate with a service provider or exchange (not shown) via a wired communication channel and with the Internet 150, rather than wirelessly (or also wirelessly). PSTN 140 may include a circuit-switched telephone network for providing plain old telephone service (POTS). The Internet 150 may include a network of computers and subnets (intranets) or both, incorporating protocols such as Internet Protocol (IP), Transmission Control Protocol (TCP), and User Datagram Protocol (UDP). ED 110a, ED 110b, and ED 110c may be multimode devices capable of operating according to multiple wireless access technologies and include multiple transceivers required to support these technologies.

[0179] Figure 3 This is a schematic diagram of ED 110 and base stations 170a, 170b and / or 170c according to an embodiment of this application.

[0180] Figure 3Another example of an ED 110 and base stations 170a, 170b, and / or 170c is shown. The ED 110 is used to connect people, objects, machines, etc. The ED 110 can be widely used in various scenarios, such as cellular communication, device-to-device (D2D), vehicle-to-everything (V2X), peer-to-peer (P2P), machine-to-machine (M2M), machine-type communication (MTC), internet of things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, automated delivery, and mobility.

[0181] Each ED 110 represents any suitable end-user equipment used for wireless operation, which may include (or be referred to as) user equipment / user device (UE), wireless transmit / receive unit (WTRU), mobile station, fixed or mobile subscriber unit, cellular phone, station (STA), machine type communication (MTC) device, personal digital assistant (PDA), smartphone, laptop, computer, tablet, wireless sensor, consumer electronics, smart book, vehicle, car, truck, bus, train, or IoT device, industrial equipment or apparatus of the above (e.g., communication module, modem, or chip), etc. Future generations of ED 110 may be referred to using other terms. Base stations 170a and 170b are T-TRPs and will be referred to as T-TRP 170 below. Similarly, Figure 3 As shown, NT-TRP is referred to below as NT-TRP 172. Each ED 110 connected to T-TRP 170 and / or NT-TRP 172 can be configured to be dynamically or semi-statically turned on (i.e., established, activated, or enabled), turned off (i.e., released, deactivated, or disabled), and / or in response to one or more of connection availability and connection necessity.

[0182] ED 110 includes a transmitter 201 and a receiver 203 coupled to one or more antennas 204. Only one antenna 204 is shown in the figure. One, part, or all of the antennas may also be panels. The transmitter 201 and receiver 203 may, for example, be integrated as a transceiver. The transceiver is used to modulate data or other content for transmission through at least one antenna 204 or a network interface controller (NIC). The transceiver is also used to demodulate data or other content received through at least one antenna 204. Each transceiver includes any suitable structure for generating signals for wireless or wired transmission and / or for processing signals received wirelessly or wiredly. Each antenna 204 includes any suitable structure for transmitting and / or receiving wireless or wired signals.

[0183] ED 110 includes at least one memory 208. Memory 208 stores instructions and data used, generated, or collected by ED 110. For example, memory 208 may store software instructions or modules for implementing some or all of the functions and / or embodiments described herein, and executed by one or more processing units 210. Each memory 208 includes any suitable one or more volatile and / or non-volatile storage and retrieval devices. Any suitable type of memory can be used, such as random access memory (RAM), read-only memory (ROM), hard disk, optical disk, subscriber identity module (SIM) card, memory stick, secure digital (SD) card, and processor cache, etc.

[0184] ED 110 may also include one or more input / output devices (not shown) or interfaces (e.g., to...). Figure 1 (Wired interface of Internet 150 in the network). Input / output devices support interaction with users or other devices on the network. Each input / output device includes any suitable structure for providing or receiving information from the user, such as a speaker, microphone, keypad, keyboard, display, or touchscreen, including network interface communication.

[0185] ED 110 also includes a processor 210 for performing various operations, including operations related to preparing for uplink transmission to NT-TRP 172 and / or T-TRP 170, operations related to processing downlink transmissions received from NT-TRP 172 and / or T-TRP 170, and operations related to processing sidelink transmissions to and from another ED 110. Processing operations related to preparing for uplink transmission may include operations such as encoding, modulation, transmission beamforming, and generating symbols for transmission. Processing operations related to processing downlink transmission may include operations such as receive beamforming, demodulation, and decoding of received symbols. According to an embodiment, receiver 203 may receive downlink transmissions (possibly using receive beamforming), and processor 210 may extract signaling from the downlink transmissions (e.g., by detecting and / or decoding signaling). For example, the signaling may be a reference signal transmitted by NT-TRP 172 and / or T-TRP 170. In some embodiments, processor 276 performs transmit beamforming and / or receive beamforming based on beam direction indications (e.g., beam angle information (BAI)) received from T-TRP 170. In some embodiments, processor 210 may perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as operations related to detecting synchronization sequences, decoding, and acquiring system information. In some embodiments, processor 210 may perform channel estimation, for example, using reference signals received from NT-TRP 172 and / or T-TRP 170.

[0186] Although not shown, processor 210 may be part of transmitter 201 and / or receiver 203. Although not shown, memory 208 may be part of processor 210.

[0187] The processing components in processor 210, transmitter 201, and receiver 203 may be implemented by the same or different processors, which execute instructions stored in memory (e.g., memory 208). Alternatively, some or all of the processing components in processor 210, transmitter 201, and receiver 203 may be implemented using special-purpose circuitry such as a field-programmable gate array (FPGA), a graphics processing unit (GPU), or an application-specific integrated circuit (ASIC).

[0188] In some implementations, T-TRP 170 may be referred to by other names, such as base station, base transceiver station (BTS), wireless base station, network node, network device, network-side device, transmit / receive node, Node B, evolved NodeB (eNodeB or eNB), femtocell, next-generation NodeB (gNB), transmission point (TP), site controller, access point (AP) or wireless router, relay station, remote radio head, ground node, ground network device or ground base station, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), location node, and other possibilities. T-TRP 170 can be a macro BS, micro BS, relay node, host node, or a combination thereof. T-TRP 170 may refer to the aforementioned equipment or to a device within the aforementioned equipment (e.g., a communication module, modem, or chip).

[0189] In some embodiments, the various parts of T-TRP 170 may be distributed. For example, some modules of T-TRP 170 may be located remotely from the device housing the antenna of T-TRP 170 and may be coupled to the device housing the antenna via a communication link (not shown), sometimes referred to as a fronthaul, such as the Common Public Radio Interface (CPRI). Therefore, in some embodiments, the term T-TRP 170 may also refer to modules on the network side that perform processing operations such as ED 110 location determination, resource allocation (scheduling), message generation, and encoding / decoding; these modules are not necessarily part of the device housing the antenna of T-TRP 170. These modules may also be coupled to other T-TRPs. In some embodiments, T-TRP 170 may actually be multiple T-TRPs that work together, for example, through coordinated multicast transmissions, to serve ED 110.

[0190] T-TRP 170 includes at least one transmitter 252 and at least one receiver 254 coupled to one or more antennas 256. Only one antenna 256 is shown in the figure. One, part or all of the antennas may also be panels. The transmitter 252 and receiver 254 may be integrated as a transceiver. T-TRP 170 also includes a processor 260 for performing various operations, including operations related to: preparing transmissions for downlink transmissions to ED 110, processing uplink transmissions received from ED 110, preparing transmissions for backhaul transmissions to NT-TRP 172, and processing transmissions received from NT-TRP 172 via backhaul. Processing operations related to preparing transmissions for downlink or backhaul transmissions may include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or backhaul may include operations such as receive beamforming, demodulation, and decoding of received symbols. Processor 260 can also perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as generating the contents of a synchronization signal block (SSB), generating system information, etc. In some embodiments, processor 260 also generates beam direction indications, such as BAI, which can be scheduled for transmission by scheduler 253. Processor 260 performs other network-side processing operations described herein, such as determining the location of ED 110, determining the location for deploying NT-TRP 172, etc. In some embodiments, processor 260 can generate signaling, for example, for configuring one or more parameters of ED 110 and / or one or more parameters of NT-TRP 172. Any signaling generated by processor 260 is transmitted by transmitter 252. Note that the term "signaling" as used herein may also be referred to as control signaling. Dynamic signaling can be transmitted in control channels, such as the physical downlink control channel (PDCCH). Static or semi-static higher-layer signaling can be included in packets transmitted in data channels such as the physical downlink shared channel (PDSCH).

[0191] Scheduler 253 may be coupled to processor 260. Scheduler 253 may be included within or operate separately from T-TRP 170, and may schedule uplink, downlink, and / or backhaul transmissions, including issuing scheduling authorizations and / or configuring unscheduled (“configured authorization”) resources. T-TRP 170 also includes memory 258 for storing information and data. Memory 258 stores instructions and data used, generated, or collected by T-TRP 170. For example, memory 258 may store software instructions or modules executed by processor 260 for implementing some or all of the functions and / or embodiments described herein.

[0192] Although not shown, processor 260 may form part of transmitter 252 and / or receiver 254. Furthermore, processor 260 may implement scheduler 253, which is not shown in the figure. Although not shown, memory 258 may be part of processor 260.

[0193] The processing components in processor 260, scheduler 253, transmitter 252, and receiver 254 can be implemented by the same or different processors, which execute instructions stored in memory, such as instructions in memory 258. Alternatively, some or all of the processing components in processor 260, scheduler 253, transmitter 252, and receiver 254 can be implemented using dedicated circuitry, such as FPGA, GPU, or ASIC.

[0194] Although the NT-TRP 172 is shown as a drone only as an example, it can be implemented in any suitable non-terrestrial form. Furthermore, in some implementations, the NT-TRP 172 may be referred to by other names, such as a non-terrestrial node, a non-terrestrial network device, or a non-terrestrial base station. The NT-TRP 172 includes a transmitter 272 and a receiver 274 coupled to one or more antennas 280. Only one antenna 280 is shown in the figure. One, some, or all of the antennas may also be panels. The transmitter 272 and receiver 274 may be integrated as a transceiver. The NT-TRP 172 also includes a processor 276 for performing various operations, including operations related to: preparing transmissions for downlink transmissions to ED 110, processing uplink transmissions received from ED 110, preparing transmissions for backhaul transmissions to T-TRP 170, and processing transmissions received from T-TRP 170 via backhaul. Processing operations related to preparing for downlink or backhaul transmissions may include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to receiving transmissions in the uplink or backhaul may include operations such as receive beamforming, demodulation, and decoding of received symbols. In some embodiments, processor 276 performs transmit beamforming and / or receive beamforming based on beam direction information (e.g., BAI) received from T-TRP 170. In some embodiments, processor 276 may generate signaling, for example, to configure one or more parameters of ED 110. In some embodiments, NT-TRP 172 implements physical layer processing but does not implement higher-level functions such as medium access control (MAC) or radio link control (RLC) layer functions. Since this is only an example, NT-TRP 172 may generally implement higher-level functions in addition to physical layer processing.

[0195] The NT-TRP 172 also includes a memory 278 for storing information and data. Although not shown, a processor 276 may be part of the transmitter 272 and / or receiver 274. Although not shown, the memory 278 may be part of the processor 276.

[0196] The processing components in processor 276, transmitter 272, and receiver 274 may be implemented by the same or different processors, which execute instructions stored in memory, such as instructions in memory 278. Alternatively, some or all of the processing components in processor 276, transmitter 272, and receiver 274 may be implemented using dedicated circuitry, such as a programmed FPGA, GPU, or ASIC. In some embodiments, NT-TRP 172 may actually be multiple NT-TRPs that work together, for example, through coordinated multipoint transmissions, to serve ED 110.

[0197] T-TRP 170, NT-TRP 172 and / or ED 110 may include other components, but these components have been omitted for clarity.

[0198] Figure 4 This is a schematic diagram of a unit or module in the device according to an embodiment of this application.

[0199] according to Figure 4 One or more steps in the provided embodiment method can be executed by the corresponding unit or module. Figure 4 The diagram illustrates units or modules within a device, such as in ED 110, T-TRP 170, or NT-TRP 172. For example, signals may be transmitted by a transmitting unit or transmitting module. Signals may be received by a receiving unit or receiving module. Signals may be processed by a processing unit or processing module. Other steps may be performed by an artificial intelligence (AI) module or a machine learning (ML) module. The corresponding units or modules may be implemented using hardware, one or more components or devices executing software, or a combination thereof. For example, one or more of these units or modules may be integrated circuits, such as a programmed FPGA, GPU, or ASIC. It should be understood that if these modules are implemented by a processor using software, these modules may be retrieved by the processor, wholly or partially, individually or collectively, for processing, in one or more instances, and these modules themselves may include instructions for further deployment and instantiation.

[0200] Further details regarding ED 110, T-TRP 170, and NT-TRP 172 are known to those skilled in the art. Therefore, these details are omitted herein.

[0201] Figure 5 This is a schematic diagram of an AI-based communication device.

[0202] The wireless system includes multiple connected devices. Device 500 is a base station (BS) or user equipment (UE). Device 500 may have three systems: a sensing system 510, a communication system 520, and / or an AI system 530. The sensing system 510 senses and collects signals and data, the communication system 520 transmits and receives signals and data, and the AI ​​system 530 trains and infers AI implementations. An exemplary AI implementation is based on two cycles of deep learning: a training cycle and an inference cycle. In some possible application scenarios, the training cycle may also be called a learning cycle, and the inference cycle may also be called an inference cycle.

[0203] Deep learning consists of two cycles: training (or learning) and inference (or deduction). During the training cycle, the coefficients of neurons are learned from training data to achieve a specific training goal or target. During the inference cycle, input data samples are fed into the trained neural network, which then outputs a prediction.

[0204] During the training cycle, the AI ​​system 530 of device 500 can train one or more DNNs, wherein the perception system 510 of device 500 can generate signals and / or data. The communication system 520 of device 500 can receive signals or data from one or more other devices. During and / or after the AI ​​system 530 completes training, the device's communication can send the training results to one or more other devices.

[0205] During the inference cycle, the AI ​​system 530 of device 500 can perform one or more inferences with one or more DNNs to complete one or more tasks. The perception system 510 of device 500 can generate signals and / or data, and the communication system 520 of device 500 can receive signals or data from one or more other devices. After the AI ​​system 530 of device 500 completes the inference, the communication system 520 of device 500 can send the inference results to one or more other devices.

[0206] AI implementations can switch between these two cycles, or remain in both cycles simultaneously. For example, the AI ​​system 530 of device 500 can train a second DNN, but still perform inference on the first DNN.

[0207] During the training cycle, the AI ​​system 530 of device 500 can operate in single-user mode. In this mode, the AI ​​system 530 trains one or more DNNs using data provided by the perception system 510 of device 500. Examples of data include local perception data and local channel data. Local perception data includes RGB data, light detection and ranging (LiDAR) data, temperature data, barometric pressure data, power outage data, etc. Local channel data includes channel state information (CSI), received signal strength indicator (RSSI), latency data, etc.

[0208] Optionally, the AI ​​system 530 of device 500 can operate in a collaborative mode. In this mode, the AI ​​system 530 trains one or more DNNs using data received by the communication system 520 of device 500. Example data includes perceptual data, channel data, neuron data, and potential output data. Perceptual data includes RGB data, LiDAR data, temperature data, barometric pressure data, power outage data, etc. Channel data includes CSI, RSSI, latency data, etc. Neuron data includes multiple neurons or multiple gradients. Potential output data includes multiple potential outputs.

[0209] Figure 6 This is a schematic diagram of device 500 receiving reference data samples from device 600 according to an embodiment of this application. In collaborative mode, the AI ​​system 530 of device 500 can use the following data: accumulating the perception data received by the communication system 520 of device 500 into a training dataset; accumulating the channel data received by the communication system 520 of device 500 into a training dataset; setting local neurons using neurons received by the communication system 520 of device 500, which is a typical joint learning scheme; and inputting the potential output received by the communication system 520 of device 500 into its DNN.

[0210] Alternatively, in collaborative mode, the AI ​​system 530 of device 500 can use the data received by the communication system 520 of device 500 and its local data, for example: mixing the local perception data provided by the perception system 510 of device 500 with the perception data received by the communication system 520 of device 500 into a training dataset; mixing the local channel data provided by the perception system 510 of device 500 with the channel data received by the communication system 520 of device 500 into a training dataset; averaging the local neurons owned by the AI ​​system 530 of device 500 with the neurons received by the communication system 520 of device 500, which is a typical joint learning scheme; averaging the local latent outputs owned by the AI ​​system 530 of device 500 and inputting them into its DNN.

[0211] Figure 7 This is a schematic diagram of reference data samples consisting of multiple groups, according to an embodiment of this application. During a training cycle, the communication system 520 of device 500 can receive some reference data samples in single-user or cooperative mode. Some devices transmit reference data samples in broadcast, multicast, or unicast channels. Other devices transmit one or more indicators about which layer(s) the reference data samples are associated with, where, for example, there are three groups of reference data samples: the first group of reference data samples is indicated to be associated with the input layer of the DNN, the second group of reference data samples is indicated to be associated with the output of a latent layer of the DNN, and the third group of reference data samples is indicated to be associated with the layer output of the DNN.

[0212] The AI ​​system 530 of device 500 can measure the distance between its local data samples and reference data samples in groups. The AI ​​system 530 of device 500 can sample its local layer inputs, local latent layer outputs, and / or layer outputs randomly, non-randomly, uniformly, or non-uniformly. Then, the AI ​​system 530 of device 500 measures the distance between the local samples and the reference samples received by the communication system 520 of device 500. If the average distance across all groups is consistently below one or more predefined thresholds, the AI ​​system 530 of device 500 can indicate that the current training process is working as expected; otherwise, the AI ​​system 530 can indicate an anomaly.

[0213] Even when a device lacks an AI system but possesses a perception and communication system, its perception system can still measure the distance between its local data samples and reference data samples associated with the DNN layer inputs. If the average distance across the layer inputs is below a predefined threshold, the device's perception system can consider the data captured as "good" data; otherwise, it considers it as bad data. The device's communication system can either send only good data to other devices while omitting bad data, or it can label the perceived data with distances before sending it to other devices.

[0214] The UE can report information about its data to the BS, which then determines whether the data differs significantly from the training data. If the difference is too large, the BS can switch its operating mode from AI mode to non-AI mode, or switch to another AI model. However, the UE directly reporting raw data may be considered an infringement of user privacy. Transmitting raw data over the air is inefficient and may also violate privacy policies. Therefore, a pressing technical challenge is how to securely and efficiently transmit data status information.

[0215] To protect the original data and save bandwidth, a set of reference data samples is encoded or compressed into a space with a lower dimension than its original space. The encoder or compressor can be linear or nonlinear. Linear encoders can be implemented using standard bases such as Fourier bases, DCT, wavelets, or custom bases. These bases can consist of a unitary matrix (orthogonal normalized). Nonlinear encoders can be implemented using DNNs. Figure 8 This is a schematic diagram of an approximation based on DNN in an embodiment of this application.

[0216] Unlike traditional compression schemes built for reliable reconstruction, when data is compressed to a lower-dimensional space, the encoder intentionally avoids reliable reconstruction but preserves as much topological distance as possible. In other words, after encoding to a lower-dimensional space, the relative distance between two data samples in their original signal space is well preserved.

[0217] Figure 9 This is a flowchart of a communication method according to an embodiment of this application.

[0218] In 710, the first coefficient is sent.

[0219] The first coefficient is determined based on the first data and the reference basis, and the dimension of the first coefficient is smaller than the dimension of the first data.

[0220] The first data includes monitoring or measurement data from user equipment or network equipment. Further, the first data is monitoring or measurement data related to the AI ​​model. In this embodiment, the network equipment can be a BS (Browser / Server).

[0221] If the first data is data sent from the UE uplink to the BS, then this data is the UE's monitoring or measurement data. If the first data is data sent from the BS downlink to the UE, then this data is the BS's monitoring or measurement data.

[0222] One or more reference bases are predefined or configured. A reference base is one of a plurality of predefined or configured reference bases. For example, a reference base can be configured by the BS to the UE. A reference base can be an orthogonal basis, and any two columns of the reference base are completely orthogonal to each other. A typical orthogonal basis is a DFT basis.

[0223] In 720, communication is based on the first coefficient.

[0224] Figure 10 This is a flowchart of a communication method according to an embodiment of this application. To protect the original data and save bandwidth, a set of reference data samples is encoded or compressed into a space with a lower dimension than its original space. The encoder or compressor can be linear or nonlinear. A linear encoder can be implemented using some standard basis, such as Fourier basis, discrete cosine transform (DCT), or wavelet. Alternatively, a linear encoder can have some custom basis, which can consist of a unitary matrix (orthogonal normalized). A nonlinear encoder can be implemented using some DNN.

[0225] In the embodiments given below, the UE projects the high-dimensional signal onto the low-dimensional signal (coefficients) through a transformation (orthogonal normalized basis U). Reporting coefficients instead of raw data is more efficient and better for privacy.

[0226] In 810, one or more reference bases are configured or predefined.

[0227] The coefficient of reference basis indicator (CRBI) is used to indicate coefficients relative to a reference basis (e.g., an orthogonal basis). Suppose {u1, u2, …, ur} is an orthogonal set of vectors in a subspace Rn. This set forms the basis U of the subspace Rn. The elements represented by basis U in the subspace Rn can be written as a finite weighted linear combination of these basis elements. The coefficients of this weighted linear combination are called the components or coordinates of the vectors relative to basis U. ).

[0228] Figure 11 This is a schematic diagram illustrating the projection of a high-dimensional signal onto a low-dimensional signal according to an embodiment of this application. For example, ,in Let U be an n×1 primitive space, and U be an n×r orthogonal basis. Let n be an r×1 spectral subspace, where n is an integer greater than 1 and r < 1. <n。 This refers to the data that the UE needs to report, such as sensing data, measurement data, AI / machine learning (ML) data, channel data, and environmental data. U is both a reference basis and an orthogonal basis; any two columns of U are completely orthogonal to each other. Embodiments of this application can use columns as a basis, which can be easily applied to a basis matrix with rows as the basis, abbreviated as UH. A typical orthogonal basis is the Discrete Fourier Transform (DFT) basis. It is CRBI, which is a reference coefficient.

[0229] It is expressed as an n×1 reference sample, and U is an n×r matrix. It can be represented by a weighted linear combination of each column of U: , where are r×1 spectral coefficients or weights. In the case of r << n, is equivalent low-dimensional space signal (vector). The matrix U is a unitary matrix, where , . Then, the matrix UH is an encoder or compressor that compresses the high-dimensional (n×1) reference sample into a low-dimensional (r×1) .

[0230] In a possible implementation scenario, multiple reference bases (UA, UB, UC...) are configured or predefined. The BS configures the reference base to be used, such as UX. The UE reports CRBI according to UX. According to the formula , the UE knows U and , so it can calculate the coefficient .

[0231] In a possible implementation scenario, a reference matrix U is configured or predefined, and one or more pruning bases are indicated or predefined as reference bases. The reference matrix Y is a matrix with M rows and N columns. The pruning bases of the reference base are K columns of Y, such as the first K columns of Y, and K is configured, K ≤ N. Optionally, it can be specified which K columns of Y are selected as pruning bases.

[0232] In 820, the UE determines the coefficients of its reference base.

[0233] The reference base (U) is configured or predefined. The BS can configure one or more reference signals, and the UE can obtain the original data by measuring the reference signals . Optionally, the reference signals can also not be configured, and the UE can obtain the original data by sensing . The UE determines its own CRBI through . U is a unitary matrix, which satisfies that the conjugate transpose of the matrix is equal to the inverse of the matrix, that is, , and I is the identity matrix.

[0234] The UE can obtain one or more reported data from a single timeslot. Based on the observation interval in time (or without restriction), the UE should obtain the CRBI values ​​reported in the uplink timeslot. For example, the UE reports the CRBI value in uplink timeslot n. The UE can obtain one or more corresponding CRBI values ​​by measuring data within a configured time window n-5 to n-1. The UE can choose to report multiple CRBI values, or it can choose to report the average / maximum / minimum of multiple CRBI values.

[0235] In 830, the UE reports CRBI or CRBI index.

[0236] For example, the UE can acquire P reported data points within a time window from n-5 to n-1, which can be achieved through... Obtain P CRBI values ​​corresponding to P reported data points. The UE can choose to report the average, maximum, or minimum of the P CRBI values. The reported data includes the UE's monitoring data or measurement data.

[0237] The UE can directly report CRBI or report the index corresponding to the CRBI. The base station can configure a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) for the UE to report CRBI. CRBI reporting supports periodic reporting, non-periodic reporting, and semi-static reporting.

[0238] In some potential application scenarios, the UE reports the index corresponding to the CRBI. In this scenario, one or more CRBI tables are predefined or configured. A reference base can be associated with one CRBI table or multiple CRBI tables. When a reference base is associated with multiple CRBI tables, the BS indicates which CRBI table to use.

[0239] The CRBI indexes of the CRBI table are reported by the UE. As shown in Table 1, the CRBI index is represented by 4 bits. Although all CRBI values ​​in Table 1 are represented as the same {c0, c1, …, cr}, each CRBI index corresponds to a different CRBI value. In some possible implementations, the value of {c0, c1, …, cr} is different in different rows of the CRBI table; for example, some are {c0, c1, …, c5}, and some are {c0, c1, …, c6}.

[0240] Table 1

[0241] In some possible implementations, a CRBI index may correspond to a CRBI range, and Table 1 should not be construed as a limitation of this application.

[0242] The communication method provided in this embodiment allows the UE to report its data to the BS with minimal air interface overhead. The BS then determines whether the data differs significantly from the training data, which improves the efficiency of data reporting while protecting data privacy.

[0243] Figure 12 This is a flowchart of a communication method according to an embodiment of this application. In this embodiment, differential CRBI indexing can be used for reporting.

[0244] In 910, the reference CRBI index is determined.

[0245] The reference CRBI index can be indicated by the BS, or it can be configured or predefined.

[0246] In 920, the offset is reported to the BS.

[0247] The UE reports the offset to the BS. Based on the offset and the reference CRBI index, the BS can determine the CRBI index of the current data. For example, the differential CRBI can be obtained through equation (1).

[0248] Offset = Current data CRBI index - Reference CRBI index (1) The communication method provided in this embodiment allows the UE to report its data to the BS with minimal air interface overhead. The BS then determines whether the data differs significantly from the training data, which improves the efficiency of data reporting while protecting data privacy.

[0249] Furthermore, the communication method provided in this application can also be applied to downlink (DL) transmissions where the BS instructs the UE to CRBI or CRBI index, used to indicate data information on the BS side. For specific implementation details, please refer to [link / reference]. Figures 9 to 12 The description of the subject matter will not be repeated in this application.

[0250] Figure 13 This is a schematic diagram of the determination matrix U in an embodiment of this application.

[0251] Each column of matrix U can be a standard basis such as a Fourier basis, DCT basis, or wavelet basis. Alternatively, the r columns of matrix U can be constructed on the distribution of a set of reference samples x. , An example of calculating matrix U on the distribution of ... can be shown below: Accumulate a sufficient number (M) of n×1 samples , ... , M << n; juxtapose them into an n×M matrix , the order of the data samples is not important; apply the descending singular value decomposition (SVD) to : , where U is an n×r unitary (orthogonal) matrix representing the commonality among all M reference samples .

[0252] Since a set of reference data samples corresponds to a layer output, each set of reference data samples has its own matrix U. The first set has matrix U1 and its compressed version , the second set has matrix U2 and its compressed version .

[0253] The communication system of the device receives the first matrix U1 and the first set of reference samples (after compression) , as well as the second matrix U2 and the second set of reference samples (after compression) .

[0254] Figure 14 is a schematic diagram of the first sampling matrix P1 in an embodiment of the present application.

[0255] The first matrix U1 is n1×r1, and the second matrix U2 is n2×r2. If n1 and / or n2 are very large numbers, the first sampling matrix P1 can be applied to the first matrix U1, and the second sampling matrix P2 can be applied to the second matrix U2. The first sampling matrix P1 is m1×n1 (m1 << n1), and each row has only one "1", indicating the position to be sampled in . The second sampling matrix P2 is m2×n2 (m2 << n2), and each row has only one "1", indicating

[0256] Figure 15 is a schematic diagram of the sampling matrix for compressing matrix U in an embodiment of the present application.

[0257] In one possible implementation, the device's communication system receives a first compact matrix θ1, a first sampling matrix P1, and a first set of compressed reference samples. The device's communication system receives the second compact matrix θ2, the second sampling matrix P2, and the second set of compressed reference samples. .

[0258] Alternatively, the device's communication system receives the left inverse of the first compact matrix. The first sampling matrix P1 and the first set of reference samples (compressed) The device's communication system receives the inverse of the second compact matrix. The second sampling matrix P2 and the second set of reference samples (compressed) .

[0259] Figure 16 This is a schematic diagram of the scoring distance in the low-frequency space according to an embodiment of this application.

[0260] The device's communication system can receive measurements of the first two samples. and The first scoring function for the distance between The device's communication system can receive measurements of two samples from the second group. and The second scoring function for the distance between The first rating function d1 and the second rating function d2 can be the same or different. The first rating function... Second scoring function This could be a dot product, inner product, Euclidean distance, etc. Alternatively, it could be the first scoring function. Second scoring function It can be based on DNN.

[0261] Alternatively, the device's communication system can receive measurements of the two distributions in the first group. and The first scoring function for the distance between The device's communication system can receive measurements from two distributions in the second group. and The second scoring function for the distance between The first rating function d1 and the second rating function d2 can be the same or different. The first rating function... Second scoring function This could be mutual information, the Hilbert-Schmidt independence criterion (HSIC) measure, KL divergence, graph edit distance, Wasserstein distance, Jensen-Shannon divergence (JSD), etc. Alternatively, a first scoring function could be used. Second scoring function It can be based on DNN.

[0262] After the communication system 520 of device 500 receives the reference data sample set, the AI ​​system 530 of device 500 can measure the distance between its local data sample and the reference data sample.

[0263] The AI ​​system 530 of device 500 uses a first set of reference data samples to sample local data on the layer indicated by the indicator. Specifically, the AI ​​system 530 of device 500 can use the first sampling matrix... Instructions Sampled from each location ×1 local sample Then the AI ​​system 530 can calculate low-dimensional space. The AI ​​system 530 can sample each data point in a periodic batch, or it can process a periodic batch... The data is randomly sampled: .

[0264] AI system 530 uses a second set of reference data samples to sample the local data indicating the metric. AI system 530 can then use the second sampling matrix... Instructions Sampled from each location ×1 local sample Then the AI ​​system 530 can calculate low-dimensional space. The AI ​​system 530 can sample each data point in a periodic batch, or it can process a periodic batch... The data is randomly sampled: .

[0265] AI System 530 can obtain the left inverse of a compact matrix in several ways. and For example, communication system 520 receives the inverse of a compact matrix. Alternatively, communication system 520 receives a compact matrix. and Then the first compact matrix Left reverse is , the second compact matrix Left reverse is Alternatively, communication system 520 receives the first matrix. Second matrix and the first sampling matrix Second sampling matrix AI system 530 calculates the left inverse of the first compact matrix. left inverse of the second compact matrix Alternatively, communication system 520 receives the first matrix. Second matrix The AI ​​system 530 generates the first sampling matrix locally. Second sampling matrix AI system 530 calculates the left inverse of the first compact matrix. left inverse of the second compact matrix Alternatively, the communication system receives the first matrix. Second matrix AI systems do not sample local data; mathematically, the first sampling matrix... It is the identity matrix The second sampling matrix It is the identity matrix The AI ​​system calculates the left inverse of the first compact matrix. left inverse of the second compact matrix If the first matrix If it is a unitary matrix, then If the second matrix If it is a unitary matrix, then .

[0266] The AI ​​system 530 of device 500 can measure the first set of local data samples. and The distance between them. The AI ​​system 530 of device 530 can measure the distance between the second set of local data samples. and The distance between them, wherein the measurement method is based on a scoring function received by the communication system 520 of device 500. and .

[0267] If the scoring function and Measure the distance between these two samples; for example, the average minimum distance for the first group is... The average minimum distance of the second group is If the scoring function and Measure the distance between these two distributions, for example, the first group is The second group is Optionally, the AI ​​system 530 can compute higher orders, such as... and The root-mean-square (RMS) and standard deviation.

[0268] Figure 17 This is a schematic diagram of an embodiment of the communication method of this application.

[0269] In 1710, obtain N anchor points.

[0270] Each of the N anchor points includes one or more reference data, where N≥1.

[0271] In one possible implementation scenario, if the BS assists the UE in model switching, the BS can configure N anchor points for the UE. In another possible implementation scenario, if the UE assists the BS in model switching, the UE can report N anchor points to the BS. The configuration signal can be radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI), and can be broadcast, multicast, or unicast.

[0272] In 1720, send at least one index of the M anchor points that differ the least from the first data.

[0273] The first data includes monitoring or measurement data from user equipment or network equipment. Further, the first data is monitoring or measurement data related to the AI ​​model. In this embodiment, the network equipment can be a base station (BS). If the first data is data sent from the UE uplink to the BS, then the data is the UE's monitoring or measurement data. If the first data is data sent from the BS downlink to the UE, then the data is the BS's monitoring or measurement data.

[0274] The M anchor points are the M anchor points with the smallest difference from the first data point out of the N anchor points. Each of the N anchor points corresponds to a configured AI model. M and N are integers greater than or equal to 1, and M ≤ N.

[0275] The UE or BS reports the most recent anchor index, which can be periodic, semi-static, or aperiodic. This reporting can be performed on the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0276] Figure 18 This is a schematic diagram illustrating a BS instructing a UE to perform model switching according to an embodiment of this application. In the field of communications, a bilateral model typically includes a model of the BS and a model of the UE, such as a machine learning model combining the encoder of the BS and the decoder of the UE. In this model, the BS encoder is used to encode raw data into encoded data, while the UE decoder is used to decode the received encoded data back into raw data. For example, the encoded data may be raw data processed into another data format (e.g., compressed raw data). Such a bilateral model can optimize the encoder and decoder through joint training, thereby improving the performance and efficiency of the communication system. Multiple candidate decoder models are configured for the UE. Since the UE's AI / ML capabilities to support large AI / ML models are limited, the optimal decoder may depend on the UE's location (surrounding environment).

[0277] In 1810, BS configures multiple candidate models with corresponding data anchors.

[0278] The BS configures multiple candidate AI / ML models for the UE, with each model having a model index. The configuration signal can be radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI), and can be broadcast, multicast, or unicast.

[0279] BS configures associated data anchors for each candidate AI / ML model. Anchors are sets of reference data, such as reference coefficients (…). The reference data are the reference coefficients of the reference base, which is configured or predefined. The set can be a vector, such as a one-dimensional array, where the vector has a size of r, which is predefined or configured. The set has a size of K (cj, j=1,2,…K), where K is predefined or configured. Each of the N anchors includes K reference coefficients.

[0280] The association between a data anchor and a candidate AI / ML model can be implicitly determined or explicitly configured. For example, the association between a data anchor and a candidate AI / ML model is implicitly determined by giving the anchor index the same value as the model index, i.e., {model index k, anchor index k}. For example, the association between a data anchor and a candidate AI / ML model is explicitly configured; the BS configures data anchor j to be associated with candidate model k, i.e., {model index k, anchor index j}.

[0281] In 1820, the UE reports the most recent anchor point.

[0282] Reference data (e.g., coefficients) The coefficients are the reference coefficients of the reference basis (orthogonal normalized basis U). During the information exchange between the UE and the BS, the UE can project the high-dimensional signal onto the low-dimensional signal (coefficients) through the transformation (orthogonal normalized basis U). In ), the transformation equation is: ,in For the reported data, U is the reference base. These serve as reference coefficients. One column of U is one of the bases, meaning that any two columns of U are perfectly orthogonal to each other. Reported data includes UE monitoring or measurement data.

[0283] Configure or predefine a reference base (U). For example, the BS can configure a reference signal with respect to the reference base. Optionally, this reference signal can also be sensed by the UE. The UE communicates with the UE via... Determine its coefficient of reference basis indicator (CRBI). U is a unitary matrix that satisfies the condition that the conjugate transpose of the matrix is ​​equal to the inverse of the matrix, i.e., I is the identity matrix.

[0284] UE calculates its data (e.g., coefficients) The difference between the reference data cj (j=1,2,…K) in the anchor point and the reference data cj in the anchor point can be calculated by a method or function indicated by BS or predefined. For example, the difference can be calculated by any one of equations (2), (3) and (4). It is the reference data in the reported data and anchor points. The difference between them. This is the data reported by the UE. It is the j-th reference data in the anchor point. <> represents the inner product. This represents the magnitude of the vector. This indicates other user-defined functions. , .

[0285] (2) (3) (4) Equations (2) to (4) are merely examples; the UE calculates its data (e.g., coefficients). The difference between the reference data cj (j=1,2,…K) in the anchor point and the reference data cj in the anchor point can also be calculated by dot product, Euclidean distance or DNN-based algorithm, etc. The above embodiments should not be construed as limiting the present application.

[0286] An anchor point is a set of reference data. The UE calculates the difference between its data and the anchor point according to a method that the BS can indicate or predefined, such as equation (5) or (6). It is the difference between the reported data and the anchor point. It can be reporting data K reference data in the anchor point The minimum value of the difference between them can also be the reported data. K reference data in the anchor point The average of the differences between them.

[0287] (5) (6) Alternatively, the difference between the reported data and the anchor point can be obtained through mutual information, the Hilbert-Schmidt independence criterion (HSIC) measure, Kullback-Leibler (KL) divergence, graph edit distance, Wasserstein distance, Jensen-Shannon divergence (JSD) distance, DNN-based algorithms, etc.

[0288] The UE calculates the difference (dj) between its data and anchor point j (j=1,2,…,N), where N is the number of anchor points. The UE can report the index of the anchor point with the smallest difference between its data and the UE's data to the BS, or it can report M indices of the anchor points with the smallest difference between its data and the UE's data to the BS, for example, the 1st smallest, the 2nd smallest, and the Mth smallest. M can be configured by the BS.

[0289] The UE's data can be raw measurement data or measurement data filtered by a Layer 3 filter. For Layer 3 filtering, the UE first filters the measurement according to equation (7) for each measurement, and then uses it to evaluate reporting criteria or measurement reports. Mn is the most recent measurement result received from the physical layer. Fn is the updated filtered measurement result used to evaluate reporting criteria or measurement reports. Fn-1 is the old filtered measurement result, where F0 is set to M1 when the first measurement result from the physical layer is received. For the 5G New Radio Measurement Object (MeasObjectNR)... Where ki is the filter coefficient for the corresponding measurement of the i-th QuantityConfigNR in the quantityConfigNR-List, and i is indicated by QuantityConfigIndex. For other measurements, , where k is the filter coefficient of the corresponding measurement received by quantityConfig.

[0290] (7) The QualityConfig-List is a data structure used to configure multiple measurement parameters for a device. It includes multiple QualityConfigs, each describing a set of measurement parameters and measurement reporting methods to guide the device in performing measurements and reporting. The QuantityConfigIndex is an index value that identifies the device's measurement parameter configuration. QuantityConfigs describe the parameters the device needs to measure and the measurement reporting method. QuantityConfigs include filter coefficients, which describe how the device smooths the measurement results.

[0291] The UE reports the most recent anchor index, which can be periodic, semi-static, or aperiodic. This reporting can be performed on the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0292] In 1830, the BS assists the UE in model switching.

[0293] According to the UE's report, the BS can assist the UE in model switching. When the UE has multiple AI / ML models, the BS assists the UE in model switching or switching to non-AI mode. When the UE has only one AI model, the BS assists the UE in switching between AI mode and non-AI mode.

[0294] Figure 19 This is another schematic diagram of a BS instructing a UE to perform a model switch according to an embodiment of this application. When the UE's measurement data is moving and moving towards anchor index 2, the BS instructs the UE to switch its model from model index 1 to model index 2.

[0295] Another reporting scheme can be differential reporting. If the most recent anchor index has not changed compared to the previous value, the UE reports "same as the previous one" to the BS, for example, using 1 bit to indicate whether there has been a change, with a value of 1 indicating a change and a value of 0 indicating the same. If the most recent anchor index has changed, the UE can also report the most recent anchor index to the BS.

[0296] Another reporting scheme is event-triggered reporting. The UE will only report the most recent anchor index to the BS when the most recent anchor index changes.

[0297] This application provides a method for actively switching data anchor points.

[0298] In addition to reporting the index of the most recent data anchor, the UE can also report the difference between its data and the most recent data anchor. When the difference between the UE's data and the most recent anchor exceeds a certain threshold, the optimal AI / ML model may fail to function, so the UE reports this information to the BS. Based on the UE's report, the BS can instruct the UE to switch to another model or fall back to a non-AI mode. Figure 20 This is another schematic diagram of a BS instructing a UE to perform a model switch according to an embodiment of this application. When the UE's measurement data is moving away from all anchor points, the BS instructs the UE to switch to non-AI mode.

[0299] This application embodiment allows the UE to provide additional auxiliary information to the BS for active handover.

[0300] Figure 21 This is a schematic diagram of a UE instructing a BS to perform model switching according to an embodiment of this application.

[0301] In 2110, the UE reports one or more data anchors to the BS.

[0302] The UE reports one or more data anchors to the BS, with each data anchor associated with an index. The reported signals can be radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI), and can be broadcast, multicast, or unicast.

[0303] UE configures associated data anchors for each candidate AI / ML model. Anchors are sets of reference data, such as reference coefficients (…). The reference data are the reference coefficients of the reference base, which is configured or predefined. The set can be a vector, such as a one-dimensional array, where the vector has a size of r, which is predefined or configured. The set has a size of K (cj, j=1,2,…K), where K is predefined or configured. Each of the N anchors includes K reference coefficients.

[0304] The association between a data anchor and a candidate AI / ML model can be implicitly determined or explicitly configured. For example, the association between a data anchor and a candidate AI / ML model is implicitly determined by giving the anchor index the same value as the model index, i.e., {model index k, anchor index k}. For example, the association between a data anchor and a candidate AI / ML model is explicitly configured, where the UE configures data anchor j to be associated with candidate model k, i.e., {model index k, anchor index j}.

[0305] In 2120, the BS indicates the nearest anchor index to the UE.

[0306] Reference data (e.g., coefficients) The coefficients are the reference coefficients of the reference basis (orthogonal normalized basis U). During the information exchange between the UE and the BS, the BS can project the high-dimensional signal onto the low-dimensional signal (coefficients) through transformation (orthogonal normalized basis U). In ), the transformation equation is: ,in The measurement data is for BS, and U is the reference base. is the reference coefficient. Measurement data at BS can be obtained through sensing measurements or uplink (UL) channel measurements via a sounding reference signal (SRS). One column of U is one of the bases, meaning that any two columns of U are perfectly orthogonal to each other.

[0307] Configure or predefine a reference base (U). For example, the UE can configure a reference signal with respect to the reference base. Optionally, this reference signal can also be sensed by the BS. The BS communicates with the BS via... Determine its coefficient of reference basis indicator (CRBI). U is a unitary matrix that satisfies the condition that the conjugate transpose of the matrix is ​​equal to the inverse of the matrix, i.e., I is the identity matrix.

[0308] BS calculates its data (e.g., coefficients). The difference between the reference data cj (j=1,2,…K) in the anchor point and the reference data cj in the anchor point can be calculated by a method or function indicated by the UE or predefined. For example, the difference can be calculated by any one of equations (8), (9) and (10). It is the data in BS and the reference data in the anchor point. The difference between them. It's data from BS. It is the j-th reference data in the anchor point. <> represents the inner product. This represents the magnitude of the vector. This indicates other user-defined functions. , .

[0309] (8) (9) (10) It should be understood that equations (8) to (10) are merely examples, and BS calculates their data (e.g., coefficients). The difference between the reference data cj (j=1,2,…K) in the anchor point and the reference data cj in the anchor point can also be calculated by dot product, Euclidean distance or DNN-based algorithm, etc. The above embodiments should not be construed as limiting the present application.

[0310] Anchor points are a set of reference data. The BS calculates the difference between its data and the anchor points according to a method that the UE can indicate or predefined, such as equation (11) or (12). It is the difference between the data in the BS and the anchor point. It can be data from BS. K reference data in the anchor point The minimum value of the difference between them can also be the data from BS. K reference data in the anchor point The average of the differences between them.

[0311] (11) (12) Alternatively, the difference between the data in the BS and the anchor point can also be obtained through mutual information, the Hilbert-Schmidt independence criterion (HSIC) measure, Kullback-Leibler (KL) divergence, graph edit distance, Wasserstein distance, Jensen-Shannon divergence (JSD) distance, DNN-based algorithms, etc.

[0312] The BS calculates the difference (dj) between its data and anchor point j (j=1,2,…,N), where N is the number of anchor points. The BS can report to the UE the index of the anchor point with the smallest difference between its data and the BS's data, or it can report M indices of the anchor points with the smallest difference between their data and the BS's data, for example, the 1st smallest, the 2nd smallest, and the Mth smallest. M can be configured by the UE.

[0313] The data from the BS can be raw measurement data or measurement data filtered by a Layer 3 filter. For Layer 3 filtering, the BS first filters the measurement according to equation (13) for each measurement before using it to evaluate reporting criteria or measurement reports. Mn is the most recent measurement result received from the physical layer. Fn is the updated filtered measurement result used to evaluate reporting criteria or measurement reports. Fn-1 is the old filtered measurement result, where F0 is set to M1 when the first measurement result from the physical layer is received. For the fifth-generation new air interface's measurement object (MeasObjectNR), Where ki is the filter coefficient for the corresponding measurement of the i-th QuantityConfigNR in the quantityConfigNR-List, and i is indicated by QuantityConfigIndex. For other measurements, , where k is the filter coefficient of the corresponding measurement received by quantityConfig.

[0314] (13) The BS reports the most recent anchor index, which can be periodic, semi-static, or aperiodic. Reporting can be performed on either the PUCCH or PUSCH.

[0315] In 2130, the UE assists the BS in model switching.

[0316] According to the BS report, the UE can assist the BS in model switching. When the BS has multiple AI / ML models, the UE assists the BS in model switching or switching to non-AI mode. When the UE has only one AI model, the UE assists the BS in switching between AI mode and non-AI mode.

[0317] The implementation of UE assisting BS in model switching is similar to that of BS assisting UE in model switching. A detailed description can be found in... Figures 3 to 5 The description can be found therein, and will not be repeated in this application.

[0318] This application provides a method for actively switching data anchor points.

[0319] In addition to reporting the index of the most recent data anchor, the BS can also report the difference between its data and the most recent data anchor. When the difference between the BS's data and the most recent anchor exceeds a certain threshold, the optimal AI / ML model may fail to function, so the UE reports this information to the UE. Based on the BS's report, the UE can instruct the BS to switch to another model or fall back to a non-AI mode. In this embodiment, the BS is allowed to provide the UE with additional auxiliary information for proactive handover.

[0320] Figure 22 This is a schematic block diagram of a communication device 2200 according to an embodiment of this application. The communication device 2200 includes: an acquisition module 2210, configured to acquire N anchor points, wherein one of the N anchor points includes one or more reference data, and N≥1; and a transmission module 2220, configured to transmit at least one index of the M anchor points that differs from the first data the smallest, wherein M≤N and M≥1.

[0321] In one possible implementation, the M first differences corresponding to the M anchor points are the M smallest of the N first differences, and the nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, 1≤n≤N.

[0322] In one possible implementation, the first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The j-th second difference among the K second differences is the difference between the first data and the j-th reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

[0323] In one possible implementation, the sending module is also used to send a third difference, which is the difference between the first anchor point and the first data. The first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0324] In one possible implementation, the sending module is further configured to send a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

[0325] In one possible implementation, the sending module is further configured to send at least one index of the M anchor points that differ the least from the first data when it is determined that the anchor point with the smallest difference from the first data has changed during the time period.

[0326] In one possible implementation, the acquisition module is also used to receive a third message; the communication device 2200 also includes a processing module 2230, used to switch to other AI models or non-AI modes according to the third message.

[0327] In one possible implementation, the processing module is also used to switch to the first AI model corresponding to the first anchor point based on the third message. The first anchor point is the anchor point with the smallest difference from the first data among N anchor points.

[0328] In one possible implementation, the third message includes an index of the second AI model, and the processing module is also used to switch to the second AI model based on the index of the second AI model.

[0329] In one possible implementation, the sending module is further configured to send a fourth message, which indicates that a third difference is greater than a predetermined threshold. The third difference is the difference between the first data and the first anchor point, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0330] In one possible implementation, the fourth message includes an invalid anchor index.

[0331] In one possible implementation, the fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

[0332] In one possible implementation, the value of M is predefined or configured.

[0333] In one possible implementation, one of the N anchor points corresponds to an AI model and an index.

[0334] In one possible implementation, the first data includes monitoring or measurement data from user equipment or network equipment.

[0335] In one possible implementation, the first data includes filtered measurement data.

[0336] In one possible implementation, the indices of the M anchor points are sent via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0337] In one possible implementation, the N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

[0338] In one possible implementation, the device is located on a user equipment or network device.

[0339] Figure 23 This is a schematic block diagram of a communication device 2300 according to an embodiment of this application. The communication device 2300 includes: a receiving module 2310, configured to receive at least one index of M anchor points, wherein the M anchor points are the M anchor points with the smallest difference from the first data among N anchor points, and one of the M anchor points includes one or more reference data, where N≥1, M≤N and M≥1; and a sending module 2320, configured to send a third message, wherein the third message is used to instruct the receiver to switch the AI ​​model or mode.

[0340] In one possible implementation, the M first differences corresponding to the M anchor points are the M smallest of the N first differences, and the nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, 1≤n≤N.

[0341] In one possible implementation, the first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The j-th second difference among the K second differences is the difference between the first data and the j-th reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

[0342] In one possible implementation, the receiving module is also used to receive a third difference, which is the difference between the first anchor point and the first data. The first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0343] In one possible implementation, the receiving module is further configured to receive a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

[0344] In one possible implementation, the third message is used to instruct the receiver to switch to the first AI model corresponding to the first anchor point, which is the anchor point with the smallest difference from the first data among the M anchor points.

[0345] In one possible implementation, the third message includes an index of the second AI model, and the third message is used to instruct the receiver to switch to the second AI model.

[0346] In one possible implementation, the receiving module is further configured to receive a fourth message, which indicates that a third difference is greater than a predetermined threshold. The third difference is the difference between the first data and the first anchor point, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

[0347] In one possible implementation, the fourth message includes an invalid anchor index.

[0348] In one possible implementation, the fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

[0349] In one possible implementation, the value of M is predefined or configured.

[0350] In one possible implementation, one of the N anchor points corresponds to an AI model and an index.

[0351] In one possible implementation, the first data includes monitoring or measurement data from user equipment or network equipment.

[0352] In one possible implementation, the first data includes filtered measurement data.

[0353] In one possible implementation, the indices of the M anchor points are sent via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

[0354] In one possible implementation, the N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

[0355] In one possible implementation, the device is located on a user equipment or network device.

[0356] like Figure 24 As shown, the communication device 2400 may include a processor 2410 and a transceiver 2420. Optionally, the communication device 2400 may also include a memory 2430. The memory 2430 may be used to store instruction information, or to store code and instructions to be executed by the processor 2410.

[0357] The memory 2430 may include random access memory, flash memory, read-only memory, programmable read-only memory, non-volatile memory, registers, etc. The processor 2410 may be a central processing unit (CPU).

[0358] For other functions and operations of the communication device 2400, please refer to [reference needed]. Figures 5 to 21 The process of the method embodiment shown will not be repeated here to avoid repetition.

[0359] This application also provides a communication system. The communication system includes communication device 2200 and communication device 2300, or the communication system includes communication device 2400.

[0360] This application also provides a computer storage medium that can store program instructions to execute the steps in the above method.

[0361] Alternatively, the storage medium may specifically be memory 2430.

[0362] This application also provides a computer program product. The computer program product includes computer program code. When the computer program code is run on a computer, the computer is able to perform the steps in the above-described method.

[0363] Optionally, all or part of the computer program code may be stored in the first storage medium. The first storage medium may be packaged together with the processor or packaged separately from the processor.

[0364] This application also provides a chip system, the system chip including an input / output interface, at least one processor, at least one memory, and a bus. The at least one memory is used to store instructions, and the at least one processor is used to invoke the instructions from the at least one memory to perform the operations in the methods described above.

[0365] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program runs, it executes the processes of the methods in the above embodiments. The storage medium may include: a magnetic disk, an optical disk, a read-only memory (ROM), or a random-access memory (RAM).

[0366] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the described apparatus embodiments are merely exemplary. For example, the unit division is only a logical functional division, and other division methods may be used in actual implementation. For example, multiple units or components may be merged or integrated into another system, or some features may be ignored or not performed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be implemented through some interfaces. Indirect coupling or communication connection between devices or units can be implemented electronically, mechanically, or otherwise.

[0367] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiment solution according to actual needs.

[0368] Furthermore, the functional units in the embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0369] The above are merely preferred exemplary embodiments of the present invention. Various modifications and variations can be made to the present invention by those skilled in the art without departing from the scope and purpose of the invention.

Claims

1. A communication method, characterized in that, include: Obtain N anchor points, where one of the N anchor points includes one or more reference data, and N≥1; Send at least one index of the M anchor points that differ from the first data the smallest, where M≤N and M≥1.

2. The method according to claim 1, characterized in that, The M first differences corresponding to the M anchor points are the M smallest of the N first differences. The nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, where 1≤n≤N.

3. The method according to claim 2, characterized in that, The first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The jth second difference among the K second differences is the difference between the first data and the jth reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

4. The method according to any one of claims 1 to 3, characterized in that, Also includes: Send a third difference, which is the difference between the first anchor point and the first data, where the first anchor point is the anchor point with the smallest difference between itself and the first data among the N anchor points.

5. The method according to any one of claims 1 to 4, characterized in that, Also includes: Send a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

6. The method according to any one of claims 1 to 5, characterized in that, The at least one index of the M anchor points that differ smallest from the first data includes: When it is determined that the anchor point with the smallest difference from the first data has changed during the time period, at least one index of the M anchor points with the smallest difference from the first data is sent.

7. The method according to any one of claims 1 to 6, characterized in that, Also includes: Receive third message; Based on the third message, switch to other AI models or non-AI modes.

8. The method according to claim 7, characterized in that, The step of switching to other AI models or non-AI modes based on the third message includes: According to the third message, switch to the first AI model corresponding to the first anchor point, where the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

9. The method according to claim 7, characterized in that, The third message includes an index of the second AI model; The step of switching to other AI models or non-AI modes based on the third message includes: Switch to the second AI model based on the index of the second AI model.

10. The method according to any one of claims 1 to 9, characterized in that, Also includes: Send a fourth message, which indicates that a third difference is greater than a predetermined threshold. The third difference is the difference between the first data and the first anchor point, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

11. The method according to claim 10, characterized in that, The fourth message includes an invalid anchor index.

12. The method according to claim 10, characterized in that, The fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

13. The method according to any one of claims 1 to 12, characterized in that, The value of M is predefined or configured.

14. The method according to any one of claims 1 to 13, characterized in that, One of the N anchor points corresponds to an AI model and an index.

15. The method according to any one of claims 1 to 14, characterized in that, The first data includes monitoring data or measurement data of user equipment or network equipment.

16. The method according to claim 15, characterized in that, The first data includes filtered measurement data.

17. The method according to any one of claims 1 to 16, characterized in that, The indices of the M anchor points are transmitted via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

18. The method according to any one of claims 1 to 17, characterized in that, The N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

19. The method according to any one of claims 1 to 18, characterized in that, The method is performed by user equipment or network equipment.

20. A communication device, characterized in that, include: The acquisition module is used to acquire N anchor points, where one of the N anchor points includes one or more reference data, and N≥1; The sending module is used to send at least one index of the M anchor points that differ from the first data the smallest, where M≤N and M≥1.

21. The communication device according to claim 20, characterized in that, The M first differences corresponding to the M anchor points are the M smallest of the N first differences. The nth first difference among the N first differences is the difference between the first data and the nth anchor point among the N anchor points, where 1≤n≤N.

22. The communication device according to claim 21, characterized in that, The first difference corresponding to the nth anchor point is the minimum or average of the K second differences corresponding to the nth anchor point. The nth anchor point includes K reference data. The jth second difference among the K second differences is the difference between the first data and the jth reference data among the K reference data, where K≥1, 1≤j≤K and 1≤n≤N.

23. The communication device according to any one of claims 20 to 22, characterized in that, The sending module is also used to send a third difference, which is the difference between the first anchor point and the first data, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

24. The communication device according to any one of claims 20 to 23, characterized in that, The sending module is further configured to send a first message or a second message, wherein the first message indicates that the anchor point with the smallest difference from the first data has not changed within a time period, and the second message indicates that the anchor point with the smallest difference from the first data has changed within a time period.

25. The communication device according to any one of claims 20 to 24, characterized in that, The sending module is further configured to: when it is determined that the anchor point with the smallest difference from the first data has changed within the time period, send at least one index of the M anchor points with the smallest difference from the first data.

26. The communication device according to any one of claims 20 to 25, characterized in that, The acquisition module is also used to receive a third message; The processing module is used to switch to other AI models or non-AI modes based on the third message.

27. The communication device according to claim 26, characterized in that, The processing module is further configured to switch to the first AI model corresponding to the first anchor point according to the third message, wherein the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

28. The communication device according to claim 26, characterized in that, The third message includes an index of the second AI model, and the processing module is further configured to switch to the second AI model based on the index of the second AI model.

29. The communication device according to any one of claims 20 to 28, characterized in that, The sending module is further configured to send a fourth message, which indicates that a third difference is greater than a predetermined threshold. The third difference is the difference between the first data and the first anchor point, and the first anchor point is the anchor point with the smallest difference between the first data and the N anchor points.

30. The communication device according to claim 29, characterized in that, The fourth message includes an invalid anchor index.

31. The communication device according to claim 29, characterized in that, The fourth message includes information indicating that the sender of the fourth message has switched to non-AI mode.

32. The communication device according to any one of claims 20 to 31, characterized in that, The value of M is predefined or configured.

33. The communication device according to any one of claims 20 to 32, characterized in that, One of the N anchor points corresponds to an AI model and an index.

34. The communication device according to any one of claims 20 to 33, characterized in that, The first data includes monitoring data or measurement data of user equipment or network equipment.

35. The communication device according to claim 34, characterized in that, The first data includes filtered measurement data.

36. The communication device according to any one of claims 20 to 35, characterized in that, The indices of the M anchor points are transmitted via the physical uplink control channel (PUCCH) or the physical uplink shared channel (PUSCH).

37. The communication device according to any one of claims 20 to 36, characterized in that, The N anchor points are configured by radio resource control (RRC), medium access control-control element (MAC-CE), or downlink control information (DCI) signals.

38. The communication device according to any one of claims 20 to 37, characterized in that, The device is located on user equipment or network equipment.

39. A communication device, characterized in that, The device includes a processor and a memory, the processor being connected to the memory; the memory is used to store instructions, and the processor is used to execute the instructions; when the processor executes the instructions stored in the memory, the processor is capable of performing the method according to any one of claims 1 to 19.

40. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, which, when executed on a processor, enable the processor to perform the method according to any one of claims 1 to 19.

41. A computer program product, characterized in that, It includes computer program code, which, when run on a computer, enables the computer to perform the method according to any one of claims 1 to 19.

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