Communication control method, communication device, communication system, and storage medium

Abstract

This application provides a communication control method, communication device, communication system, and storage medium. The communication control method includes: a terminal sending first information, wherein the first information includes at least: second information related to the method by which the terminal acquires measurement results, and the measurement results and the second information are used by a network device for artificial intelligence (AI) model training and / or AI inference. This effectively improves the performance of the AI ​​model and the performance of AI inference.

Description

Communication control methods, communication equipment, communication systems, and storage media Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication control method, communication equipment, communication system, and storage medium. Background Technology

[0002] In wireless communication networks, artificial intelligence (AI) can be used for prediction and reasoning to improve system performance. Training AI models requires collecting a large amount of data, and the data used varies depending on the application scenario. These application scenarios include, for example, beam management, channel state information (CSI) reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0003] Summary of the Invention

[0004] This disclosure provides a communication control method, network device, terminal, device, chip system, storage medium, computer program, and computer program product, which can be applied in the field of communication technology to solve the technical problem of "poor performance of AI models and AI inference in related technologies".

[0005] This disclosure proposes a communication control method, communication equipment, communication system, and storage medium.

[0006] According to a first aspect of the present disclosure, a communication control method is proposed, executed by a terminal; comprising: sending first information, wherein the first information includes at least: second information related to the manner in which the terminal acquires measurement results, the measurement results and the second information being used by a network device for artificial intelligence (AI) model training and / or AI inference.

[0007] According to a second aspect of the present disclosure, a communication control method is proposed, executed by a network device; comprising: receiving first information, wherein the first information includes at least: second information related to the manner in which the terminal acquires measurement results; and performing AI model training and / or AI inference based on the measurement results and the first information.

[0008] According to a third aspect of the present disclosure, a communication control method is proposed, comprising: a terminal sending first information, wherein the first information includes at least: second information related to the manner in which the terminal acquires measurement results; and a network device performing AI model training and / or AI inference based on the measurement results and the first information.

[0009] According to a fourth aspect of the present disclosure, a terminal is provided, the terminal comprising: a transceiver module for transmitting first information, wherein the first information includes at least: second information related to the manner in which the terminal acquires measurement results, and the measurement results and the second information are used by a network device for AI model training and / or AI inference.

[0010] According to a fifth aspect of the present disclosure, a network device is provided, comprising: a transceiver module for receiving first information, wherein the first information includes at least: second information related to the manner in which a terminal acquires measurement results; and a processing module for performing AI model training and / or AI inference based on the measurement results and the first information.

[0011] According to a sixth aspect of the present disclosure, a communication device is provided, comprising: one or more processors; wherein the processors are configured to invoke instructions to cause the communication device to execute a communication control method of any one of the first, second, and third aspects.

[0012] According to a seventh aspect of the present disclosure, a communication system is provided, characterized in that it includes a network device and a terminal, wherein the terminal is configured to implement the communication control method of the first aspect, and the network device is configured to implement the communication control method of the second aspect.

[0013] According to an eighth aspect of the present disclosure, a storage medium is provided, the storage medium storing instructions, characterized in that, when the instructions are executed on a communication device, the communication device causes the communication device to perform a communication control method as described in any one of the first, second, and third aspects.

[0014] According to a ninth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements a communication control method as described in any of the first, second, and third aspects. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments or background art of this disclosure, the accompanying drawings used in the embodiments or background art of this disclosure will be described below.

[0016] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure;

[0017] Figure 1B is a schematic diagram of obtaining measurement results based on a sliding window in an embodiment of this disclosure;

[0018] Figure 1C is a schematic diagram of obtaining measurement results based on the measurement period in an embodiment of this disclosure;

[0019] Figure 2 is an interactive schematic diagram of a communication control method according to an embodiment of the present disclosure;

[0020] Figure 3A is an interactive schematic diagram of a communication control method according to another embodiment of the present disclosure;

[0021] Figure 3B is an interactive schematic diagram of a communication control method according to yet another embodiment of the present disclosure;

[0022] Figure 4A is an interactive schematic diagram of a communication control method according to yet another embodiment of the present disclosure;

[0023] Figure 4B is an interactive schematic diagram of a communication control method according to yet another embodiment of the present disclosure;

[0024] Figure 5 is an interactive schematic diagram of a communication control method according to another embodiment of the present disclosure;

[0025] Figure 6A is a schematic diagram of the structure of the terminal proposed in an embodiment of this disclosure;

[0026] Figure 6B is a schematic diagram of the structure of the network device proposed in an embodiment of this disclosure;

[0027] Figure 7A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure;

[0028] Figure 7B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0029] This disclosure presents a communication control method, communication device, communication system, and storage medium.

[0030] In a first aspect, embodiments of this disclosure propose a communication control method, executed by a terminal; the method includes:

[0031] Send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device for artificial intelligence (AI) model training and / or AI inference.

[0032] In the above embodiments, when collecting data to train the AI ​​model, it is possible to effectively avoid mixing data obtained through different methods of acquiring measurement results, thereby effectively improving the performance of the AI ​​model. Furthermore, when inferring future measurement results based on the measurement results reported by the terminal, it is possible to ensure that the measurement results used to train the AI ​​model and the measurement results used as input during actual inference are obtained using the same method, thereby effectively improving the performance of AI inference.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement results include at least one of the following:

[0034] Community measurement results;

[0035] Beam measurement results.

[0036] In the above embodiments, the performance of AI models and AI inference can be improved in cell measurement and / or beam measurement, thereby supporting the improvement of cell measurement and / or beam measurement results.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following:

[0038] First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal;

[0039] The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period;

[0040] The third indication information is used to indicate the time-domain processing method for the terminal to acquire measurement results;

[0041] The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

[0042] In the above embodiments, it is possible to accurately instruct the network device on how the terminal obtains measurement results, enabling the network device to accurately perform AI model training and / or AI inference, thereby greatly improving the performance of AI models and AI inference.

[0043] In conjunction with some embodiments of the first aspect, in some embodiments, the first information further includes: device information, wherein the device information is used to indicate the source of the device.

[0044] In the above embodiments, the terminal can carry device information in the first information and indicate the device information to the network device by sending the first information. The network device can obtain the device information from the first information and determine multiple terminals using the same method of acquiring measurement results based on the device information. Therefore, the source of the device can be effectively indicated, enabling the network device to accurately determine multiple terminals using the same method of acquiring measurement results.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0046] Receive third information, wherein the third information is used to trigger the terminal to send the first information.

[0047] In the above embodiments, the network device can trigger the terminal to send first information by sending third information to the terminal. The third information can indicate the specific content of the indication information contained in the first information, and the terminal can refer to the third information to report the first information to the network device. This enables on-demand sending of the first information, effectively applicable to personalized AI application scenarios.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the third information includes at least one of the following:

[0049] The fifth instruction information is used to instruct the terminal to send the first information;

[0050] The sixth instruction information, wherein the sixth instruction information is used to determine the type of instruction information contained in the second information.

[0051] In the above embodiments, on-demand transmission of the first information is supported, and on-contract transmission of the second information between network devices and terminals is realized, thereby improving the flexibility of AI applications and making them suitable for personalized AI application scenarios.

[0052] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0053] Measurements are performed based on the second piece of information to obtain the measurement results.

[0054] In the above embodiments, the terminal can perform measurements in a timely manner based on the second information to obtain measurement results. This improves the accuracy of the measurement results and supports efficient reporting of the measurement results.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0056] Send a fourth message, which indicates the measurement result.

[0057] In the above embodiments, the terminal can report measurement results by sending fourth information to the network device. The reported measurement results can be used by the network device to train AI models or for AI inference, thereby supporting efficient reporting of measurement results.

[0058] Secondly, embodiments of this disclosure propose a communication control method, executed by a network device; the method includes:

[0059] Receive first information, wherein the first information includes at least: second information related to the method by which the terminal acquires the measurement results;

[0060] AI model training and / or AI inference are performed based on measurement results and initial information.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the measurement results include at least one of the following:

[0062] Community measurement results;

[0063] Beam measurement results.

[0064] In conjunction with some embodiments of the second aspect, in some embodiments, the second information includes at least one of the following:

[0065] First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal;

[0066] The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period;

[0067] The third indication information is used to indicate the time-domain processing method for the terminal to acquire measurement results;

[0068] The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

[0069] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0070] Send a third message, which is used to trigger the terminal to send the first message.

[0071] In conjunction with some embodiments of the second aspect, in some embodiments, the third information includes at least one of the following:

[0072] The fifth instruction information is used to instruct the terminal to send the first information;

[0073] The sixth instruction information, wherein the sixth instruction information is used to determine the type of instruction information contained in the second information.

[0074] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0075] Receive fourth information, wherein the fourth information is used to indicate the measurement result, which is obtained by the terminal based on the second information.

[0076] In conjunction with some embodiments of the second aspect, in some embodiments, the first information further includes: device information, wherein the device information is used to indicate the source of the device; wherein the method further includes:

[0077] Based on the device information, identify multiple terminals that use the same method to acquire measurement results.

[0078] In conjunction with some embodiments of the second aspect, in some embodiments, AI model training is performed based on measurement results and first information, including:

[0079] Based on the first information, a first result is obtained, wherein the first result is the measurement result obtained by other terminals, and the terminals and other terminals use the same method to obtain the measurement result;

[0080] The AI ​​model is trained based on the measurement results and the initial results.

[0081] In conjunction with some embodiments of the second aspect, in some embodiments, AI inference is performed based on the measurement results and the first information, including:

[0082] Based on the first information, a first AI model is selected. The first AI model is trained using the measurement results collected during the training phase. The method of obtaining the measurement results collected during the training phase is the same as the method of obtaining the measurement results by the terminal.

[0083] AI inference is performed based on the first AI model.

[0084] Thirdly, embodiments of this disclosure propose a communication control method, the method comprising:

[0085] The terminal sends first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results;

[0086] Network devices train AI models and / or perform AI inference based on measurement results and initial information.

[0087] Fourthly, embodiments of this disclosure provide a terminal, which includes:

[0088] The transceiver module is used to send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device for AI model training and / or AI inference.

[0089] Fifthly, embodiments of this disclosure provide a network device, which includes:

[0090] A transceiver module is used to receive first information, wherein the first information includes at least: second information related to the method by which the terminal obtains measurement results;

[0091] The processing module is used to train and / or infer AI models based on measurement results and initial information.

[0092] Sixthly, embodiments of this disclosure provide a communication device, comprising:

[0093] One or more processors;

[0094] The processor is used to execute the communication control method of any one of the first, second, and third aspects.

[0095] In a seventh aspect, embodiments of this disclosure provide a communication system including a terminal and a network device, wherein the terminal is configured to implement the communication control method of the first aspect, and the network device is configured to implement the communication control method of the second aspect.

[0096] Eighthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform a communication control method as described in the first, second, or third aspect.

[0097] Ninthly, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements a communication control method as described in any of the first, second, and third aspects.

[0098] It is understood that the aforementioned communication control method, network device, terminal, communication equipment, chip system, storage medium, computer program, and computer program product are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0099] This disclosure provides a communication control method and apparatus, a communication device, a communication system, and a storage medium. In some embodiments, the terms "communication control method" and "information processing method" or "communication method" can be used interchangeably; the terms "communication control apparatus" and "information processing apparatus" or "communication apparatus" can be used interchangeably; and the terms "information processing system" or "communication system" can be used interchangeably.

[0100] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0101] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0102] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0103] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0104] In the embodiments disclosed herein, "multiple" refers to two or more.

[0105] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0106] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0107] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0108] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0109] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0110] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0111] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0112] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0113] In some embodiments, "network" can be interpreted as devices included in a network, such as access network devices, core network devices, etc.

[0114] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)," "base station (BS)," "radio base station," or "fixed station." In some embodiments, it may also be understood as "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," or "bandwidth part (BWP)."

[0115] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0116] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0117] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0118] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1A, the communication system 100 may include a terminal 101 and a network device 102. The network device 102 may include at least one of an access network device and a core network device.

[0119] In some embodiments, terminal 101 includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0120] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a WiFi system.

[0121] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0122] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0123] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), or a Next Generation Core (NGC).

[0124] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0125] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0126] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), 6th generation mobile communication system (6G), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0127] Optionally, the terminal can obtain the location of the serving cell's synchronization signal and physical broadcast channel block (SSB) through system information. The serving cell can configure the SSB measurement timing configuration (SMTC) for other cells for the terminal. The terminal can determine the location of other cells' SSBs based on the SMTC and measure and obtain the radio channel quality of other cells. The location of the SSB is determined by the SSB transmission period, the duration of each SSB transmission, and the time offset. Each opportunity to measure an SSB is called a measurement opportunity (MO).

[0128] Optionally, during measurement, the terminal acquires the measurement results of reference signals from N measurement opportunities within a measurement period. Each measurement result is called a Layer 1 (L1) measurement result. These reference signals are processed to obtain a Reference Signal Receiving Power (RSRP) measurement value. This processing is called L1 filtering. The measurement value obtained after L1 filtering is the L1-filtered measurement result, which can be used to evaluate measurement reporting events and report to the network.

[0129] Optionally, the processing method of L1 filtering depends on the device implementation. It can average the N measurement results, select the strongest measurement result, or select the intermediate measurement result. The value of N may also be different for different terminals.

[0130] Optionally, the measurement results of the serving cell or neighboring cells used in evaluating and reporting measurement events are: measurement results obtained after filtering the terminal's Layer 1 measurement results by Layer 3 (L3). The Layer 3 filtering is as follows: F n = (1-a)×F n-1 +a×M n ;

[0131] Among them, M n This is the latest L1 measurement result, F n This is the filtered result, F n-1 This is the measurement result after the last filtering, where 'a' represents the coefficient.

[0132] Optionally, there are two methods for acquiring the time-domain measurement results, as shown in Figure 1B and Figure 1C. Figure 1B is a schematic diagram of acquiring measurement results based on a sliding window in an embodiment of this disclosure. Figure 1B shows the acquisition of L1 / L3 measurement results based on a sliding window, that is, the terminal performs L1 / L3 filtering after acquiring each L1 measurement result, and obtains the L1 and L3 filtered measurement results. Figure 1C is a schematic diagram of acquiring measurement results based on a measurement period in an embodiment of this disclosure. Figure 1C shows the acquisition of L1 / L3 measurement results based on a measurement period, that is, the terminal performs L1 / L3 filtering after each measurement period, and obtains the L1 and L3 filtered measurement results (e.g., Reference Signal Receiving Power (RSRP)).

[0133] Alternatively, the layer 1 described above may also be referred to as the physical layer, and the layer 3 may also be referred to as the Radio Resource Control (RRC) layer, without any limitation.

[0134] Optionally, machine learning algorithms are one of the important methods for implementing artificial intelligence technology. In machine learning, models can be obtained through a large amount of training data, and these models can be used to predict events. Models trained by machine learning can achieve very accurate prediction results.

[0135] Optionally, in wireless communication networks, artificial intelligence (AI) can be used for prediction and reasoning to improve system performance. Training AI models requires collecting a large amount of data, and different application scenarios use different data. These application scenarios include, for example, beam management, channel state information (CSI) reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0136] Optionally, during mobility operations, the terminal can predict cell measurement results and handover to a target cell. The terminal can also execute other mobility events. Among these, the terminal's ability to predict future cell measurement results can be referred to as temporal prediction. Alternatively, the terminal's ability to predict the measurement results of unmeasured cells can be referred to as spatial prediction. Mobility events include measurement reporting conditions being met, handover failure, cell dwell time, radio link failure, etc.

[0137] Optionally, the network can control terminals to report cell measurement results, and use these results to train models, thereby predicting future measurement results based on current and historical data. This reduces the need for reference signal transmission and improves mobility control, including determining handover timing and selecting the target cell for handover.

[0138] Optionally, if the AI ​​model is trained on the network side, the terminal can report the collected data to the network. Additionally, to avoid frequent reporting of collected data, the terminal can store multiple collections of data locally and report all stored data to the network in a single report.

[0139] Optionally, different terminals may acquire measurement results in different ways, including: the N value for L1 filtering, the method of processing N measurement results for L1 filtering, and the time-domain processing method. In related technologies, when the network collects data to train the model, if data obtained through different methods of acquiring measurement results are mixed, it will lead to a decrease in the performance of the AI ​​model. Furthermore, when inferring future measurement results based on the measurement results reported by the terminals, if the measurement results used to train the AI ​​model are obtained using a different method than the measurement results used as input during actual inference, it will also lead to a decrease in the performance of AI inference.

[0140] The method provided in this disclosure can avoid the degradation of AI performance caused by different methods of acquiring measurement results on different terminals. This improves AI model performance and AI inference performance.

[0141] Figure 2 is an interactive schematic diagram of a communication control method according to an embodiment of the present disclosure. As shown in Figure 2, the embodiment of the present disclosure relates to a communication control method, which can be used in a communication system 100. The method includes:

[0142] Step S2101: The network device sends third information.

[0143] The third piece of information is used to trigger the terminal to send the first piece of information. In other words, the network device can trigger the terminal to report the first piece of information by sending the third piece of information to the terminal.

[0144] Optionally, in some embodiments, the base station can trigger the UE to report the first information by sending third information to the UE.

[0145] In other words, if a network device needs to know how a terminal obtains measurement results, it can trigger the terminal to send first information by sending third information. The third information can indicate the specific content of the indication information contained in the first information, and the terminal can refer to the third information to report the first information to the network device.

[0146] Optionally, in some embodiments, the third information includes at least one of the following: a fifth indication information and a sixth indication information; wherein the fifth indication information is used to instruct the terminal to send the first information; and the sixth indication information is used to determine the type of indication information included in the second information. This supports on-demand sending of the first information and is effectively applicable to personalized AI application scenarios.

[0147] The fifth instruction information is used to instruct the terminal to send the first information. This fifth instruction information can be considered a trigger instruction; the network device sends the fifth instruction information to the terminal to trigger the terminal to report the first information. The sixth instruction information is used to define the type of instruction information included in the second information within the first information, enabling the network device and terminal to send the second information according to the agreement, improving the flexibility of AI applications, and making it suitable for personalized AI application scenarios.

[0148] Optionally, in some embodiments, the network device can indicate third information to the terminal by sending a message to the terminal, including third information in the message. This message can be a reused existing message, such as a reconfiguration message, a message carrying system information, or a terminal capability request message. Therefore, the network device can effectively save indication overhead by reusing existing messages to indicate third information to the terminal.

[0149] Optionally, in some embodiments, the network device can indicate third information to the terminal by sending a new message containing third information. That is, the network device can indicate third information to the terminal through a new message, which improves the flexibility of third information indication and is effectively applicable to personalized AI application scenarios.

[0150] In step S2102, the terminal sends first information based on the third information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results.

[0151] The first information includes at least the second information related to the method by which the terminal acquires the measurement results. That is, the first information may contain at least the second information, which refers to information related to the method by which the terminal acquires the measurement results. The second information may include, for example, the name, type, and specific details of the method by which the terminal acquires the measurement results, etc., and there are no restrictions on this.

[0152] Optionally, in some embodiments, the above measurement results include at least one of the following: cell measurement results; beam measurement results. Therefore, in cell measurement and / or beam measurement, the performance of the AI ​​model and AI inference can be improved, thereby supporting improved cell measurement and / or beam measurement results.

[0153] Optionally, in some embodiments, the terminal can receive a message sent by the network device and obtain third information from the message. This message can be a reused existing message, such as a reconfiguration message, a message carrying system information, or a terminal capability request message. Thus, the terminal can obtain the third information indicated by the network device from existing messages, effectively saving indication overhead.

[0154] Optionally, in some embodiments, the terminal can receive a message sent by the network device and obtain third information from the message. This message can be a new message. Therefore, the terminal can obtain the third information indicated by the network device from a new message sent by the network device, which improves the flexibility of the third information indication and is effectively applicable to personalized AI application scenarios.

[0155] Optionally, in some embodiments, the second information includes at least one of the following: first indication information, second indication information, third indication information, and fourth indication information; wherein, the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal; the second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period; the third indication information is used to indicate the time-domain processing method used by the terminal to acquire the measurement results; and the fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results. Therefore, it is possible to accurately instruct the network device on the method by which the terminal acquires the measurement results, supporting the network device to accurately perform AI model training and / or AI inference, and significantly improving AI model performance and AI inference performance.

[0156] Examples illustrating the above description are as follows:

[0157] Optionally, in some embodiments, the first indication information is used to indicate the Layer 1 filtering processing method adopted by the terminal. For example, the first indication information may instruct the UE to perform Layer 1 filtering processing. The Layer 1 filtering processing method may, for example, be taking the average, the maximum value, the median value, or the minimum value. For example, the Layer 1 filtering processing method can be indicated by an identifier. If different UEs indicate the same identifier, it means that different UEs have adopted the same Layer 1 filtering processing method; if different UEs indicate different identifiers, it means that different UEs have adopted different Layer 1 filtering processing methods.

[0158] Optionally, in some embodiments, the second indication information is used to indicate the number of measurement results acquired by the terminal within a measurement period. For example, the second indication information may indicate the number of measurement results acquired by the UE within a measurement period. For example, the number of measurement results acquired by the UE within a measurement period may be indicated by a quantity identifier. If different UEs indicate the same identifier, it means that different UEs acquired the same number of measurement results within a measurement period; if different UEs indicate different identifiers, it means that different UEs acquired different numbers of measurement results within a measurement period.

[0159] Optionally, in some embodiments, the third indication information is used to indicate the time-domain processing method used by the terminal to acquire the measurement results. For example, the third indication information is used to indicate the time-domain processing method used by the UE to acquire the measurement results. For example, the time-domain processing method can be a sliding window-based processing method or a non-sliding window-based processing method. For example, the time-domain processing method can be indicated by an identifier. If different UEs indicate the same identifier, it means that different UEs use the same time-domain processing method to acquire the measurement results; if different UEs indicate different identifiers, it means that different UEs use different time-domain processing methods to acquire the measurement results.

[0160] Optionally, in some embodiments, the fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results. That is, the type of method by which the terminal acquires the measurement results can specifically be, for example, the type indicated by the first indication information, the second indication information, and the third indication information. Each of the first, second, and third indication information corresponds to a type of method for acquiring measurement results. For example, the fourth indication information is used to indicate the type of method by which the UE acquires the measurement results. For example, the type of method by which the UE acquires the measurement results can be indicated by an identifier of the method for acquiring the measurement results. If different UEs indicate the same identifier, it means that different UEs use the same type of method for acquiring measurement results; if different UEs indicate different identifiers, it means that different UEs use different types of methods for acquiring measurement results.

[0161] In the embodiments of this disclosure, the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device for AI model training and / or AI inference. That is to say, the network device can use the second information contained in the first information, as well as the measurement results reported by the terminal, to train an AI model, or to perform AI inference, or to train an AI model and perform AI inference, as detailed in subsequent embodiments.

[0162] Optionally, in some embodiments, the first information further includes device information, wherein the device information is used to indicate the source of the device. The device can refer to a component in the terminal (such as a chip, processor, etc.), specifically, a device used to acquire measurement results. The device information describes the source of the component (e.g., which manufacturer it originates from), and specifically, a device identifier, which can be used to uniquely identify the source of the corresponding component. The terminal can carry the device information in the first information and indicate the device information to the network device by sending the first information. The network device can obtain the device information from the first information and determine multiple terminals using the same method of acquiring measurement results based on the device information. Thus, the source of the device can be effectively indicated, enabling the network device to accurately determine multiple terminals using the same method of acquiring measurement results.

[0163] Optionally, in some embodiments, when the first information also includes device information, the network device can determine multiple terminals using the same method of acquiring measurement results based on the device information. For example, since the device information indicates the source of the device, the network device can determine the source of the device used to acquire the measurement results in each terminal, and determine whether the sources of the devices used to acquire the measurement results in different terminals are the same. If the sources of the devices used to acquire the measurement results in different terminals are the same, then it is determined that the different terminals use the same method of acquiring measurement results; if the sources of the devices used to acquire the measurement results in different terminals are different, then it is determined that the different terminals use different methods of acquiring measurement results. Alternatively, the network device can refer to the device information and, based on the foregoing description, determine multiple terminals using the same method of acquiring measurement results from multiple candidate terminals. Thus, it achieves accurate determination of multiple terminals using the same method of acquiring measurement results.

[0164] Optionally, in some embodiments, the terminal can indicate the first information to the network device by sending a message containing the first information. This message can be a reused existing message, such as a measurement reporting message, a message carrying terminal auxiliary information, a reconfiguration completion message, or a terminal capability reporting message. Therefore, the terminal can effectively save indication overhead by reusing existing messages to indicate the first information to the network device.

[0165] Optionally, in some embodiments, the terminal can indicate the first information to the network device by sending a new message containing the first information. That is, the terminal can indicate the first information to the network device through a new message, which improves the flexibility of the first information indication and is effectively applicable to personalized AI application scenarios.

[0166] In step S2103, the terminal performs a measurement based on the second information and obtains the measurement result.

[0167] The second information refers to information related to the method by which the terminal obtains the measurement results. In other words, the terminal can refer to the second information to perform the measurement and obtain the results. For example, it can use the method indicated by the second information to perform the measurement and obtain the results.

[0168] For example, the terminal can use the layer 1 filtering processing method indicated by the first indication information in the second information to process the measured data and obtain the measurement result; and / or the terminal can use the number of measurement results acquired during the measurement period indicated by the second indication information in the second information to measure and obtain measurement results that meet that number; and / or the terminal can use the time-domain processing method for obtaining measurement results indicated by the third indication information in the second information to measure and obtain the measurement result, without any restrictions.

[0169] In step S2104, the terminal sends fourth information, which is used to indicate the measurement result.

[0170] Optionally, in some embodiments, after the terminal performs measurement based on the second information and obtains the measurement result, it can report the measurement result. For example, the terminal can report the measurement result by sending a fourth piece of information to the network device. The reported measurement result can be used by the network device to train an AI model or for AI inference, without limitation.

[0171] Optionally, in some embodiments, the terminal can send a measurement reporting message to the network device, carrying the fourth information in the measurement reporting message to indicate the measurement result to the network device, thereby effectively improving the reporting efficiency of the measurement result. Of course, the terminal can also report the fourth information to the network device to indicate the measurement result through any other possible message (such as an existing message or a new message), without limitation.

[0172] In step S2105, the network device performs AI model training and / or AI inference based on the measurement results and the first information.

[0173] Optionally, in some embodiments, the network device may receive fourth information, which indicates the measurement result obtained by the terminal based on the second information. Thus, the network device can promptly obtain the terminal's measurement results, supporting AI model training and / or AI inference on the network device side.

[0174] Optionally, in some embodiments, the order in which the terminal sends the measurement result and the first information is not limited. The terminal may send the measurement result first and then send the first information; or the terminal may send the first information first and then send the measurement result; or the terminal may send the first information and the measurement result simultaneously, without any restriction.

[0175] Optionally, in some embodiments, during the process of training the AI ​​model based on the measurement results and the first information, a first result can be obtained based on the first information. This first result is a measurement result obtained by another terminal. The terminal and the other terminals use the same method for obtaining the measurement results, and the AI ​​model is trained based on the measurement results and the first result. Therefore, when collecting data to train the AI ​​model, it is possible to effectively avoid mixing data obtained through different methods of obtaining measurement results, thereby effectively improving the performance of the AI ​​model.

[0176] For example, terminal A sends the first information to the network device. Based on the first information, the network device determines that terminal A obtains the measurement results in mode A. In addition, the network device determines that other terminals B, C, D and terminal A all use the same mode A to obtain the measurement results. Then the network device can use the measurement results reported by terminals A, B, C and D to train the AI ​​model.

[0177] Optionally, in some embodiments, the network device can receive a message sent by the terminal and obtain first information from the message. This message can be a reused existing message, such as a measurement reporting message, a message carrying terminal auxiliary information, a reconfiguration completion message, or a terminal capability reporting message. Thus, the network device can obtain the first information reported by the terminal from existing messages, effectively saving indication overhead.

[0178] Optionally, in some embodiments, the network device can receive a message sent by the terminal and obtain first information from the message. This message can be a new message. Therefore, the network device can obtain the first information reported by the terminal from a new message sent by the terminal, improving the flexibility of the first information indication and effectively adapting to personalized AI application scenarios.

[0179] Optionally, in some embodiments, the network device can receive measurement reporting messages sent by the terminal and obtain the fourth information reported by the terminal from the measurement reporting messages to know the measurement results reported by the terminal, thereby effectively improving the efficiency of obtaining measurement results. Of course, the network device can also obtain the fourth information through any other possible messages reported by the terminal (such as existing messages or new messages) to obtain the measurement results, and there is no limitation on this.

[0180] Optionally, in some embodiments, during the process of performing AI inference based on measurement results and first information, a first AI model may be selected based on the first information. This first AI model is trained using measurement results collected during the training phase. The method of acquiring the measurement results collected during the training phase is the same as the method by which the terminal acquires the measurement results. AI inference is then performed based on the first AI model. When inferring future measurement results based on the measurement results reported by the terminal, it can be ensured that the measurement results used to train the AI ​​model and the measurement results used as input during actual inference are acquired using the same method, thereby effectively improving the performance of AI inference.

[0181] For example, a network device can pre-deploy multiple candidate AI models. Each candidate AI model can be trained using different measurement results, and the measurement results used for training each candidate AI model are obtained based on a specific method of obtaining the measurement results. After receiving the method of obtaining the measurement results reported by the terminal, the network device can compare the method of obtaining the measurement results reported by the terminal with the method of obtaining the measurement results associated with each candidate AI model. The device will then select the method of obtaining the measurement results that is the same as the method of obtaining the measurement results reported by the terminal, and designate the candidate AI model associated with the "same method of obtaining the measurement results" as the first AI model. Subsequently, the first AI model can be used for AI inference.

[0182] The communication control method disclosed in this embodiment may include at least one of steps S2101 to S2105. For example, step S2101 may be implemented as a standalone embodiment, step S2102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps S2101+S2102 may be implemented as standalone embodiments, and steps S2101+S2102+S2103 may be implemented as standalone embodiments, but is not limited thereto.

[0183] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0184] In this embodiment, the network device sends third information, and the terminal sends first information based on the third information. The first information includes at least second information related to the method by which the terminal obtains the measurement results. Measurements are performed based on the second information to obtain the measurement results. The terminal then sends fourth information, which indicates the measurement results. The network device performs AI model training and / or AI inference based on the measurement results and the first information. Since the network device receives the measurement results and knows the method by which the terminal obtains the measurement results based on the received first information, it can effectively avoid mixing data obtained through different methods of obtaining measurement results when collecting data to train the AI ​​model, thereby effectively improving the performance of the AI ​​model. Furthermore, when inferring future measurement results based on the measurement results reported by the terminal, it can ensure that the measurement results used to train the AI ​​model and the measurement results used as input during actual inference use the same acquisition method, thereby effectively improving the performance of AI inference.

[0185] Figure 3A is an interactive schematic diagram of a communication control method according to another embodiment of the present disclosure. As shown in Figure 3A, the embodiments of the present disclosure relate to a communication control method, which can be used in a terminal. The method includes:

[0186] Step S3101: Send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device for AI model training and / or AI inference.

[0187] The communication control method disclosed in this embodiment may include step S3101. For example, step S3101 may be implemented as a standalone embodiment, but is not limited thereto.

[0188] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0189] Figure 3B is an interactive schematic diagram illustrating a communication control method according to yet another embodiment of the present disclosure. As shown in Figure 3B, the embodiments of the present disclosure relate to a communication control method, which can be used in a terminal. The method includes:

[0190] Step S3201: Receive third information, wherein the third information is used to trigger the terminal to send the first information.

[0191] Step S3202: Send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device for AI model training and / or AI inference.

[0192] Step S3203: Perform a measurement based on the second information to obtain the measurement result.

[0193] Step S3204: Send fourth information, wherein the fourth information is used to indicate the measurement result.

[0194] The communication control method disclosed in this embodiment may include at least one of steps S3201 to S3204. For example, step S3201 may be implemented as a standalone embodiment, step S3202 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps S3201+S3202 may be implemented as standalone embodiments, but are not limited thereto.

[0195] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0196] In some embodiments of this disclosure, the measurement results include at least one of the following:

[0197] Community measurement results;

[0198] Beam measurement results.

[0199] In some embodiments of this disclosure, the second information includes at least one of the following:

[0200] First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal;

[0201] The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period;

[0202] The third indication information is used to indicate the time-domain processing method for the terminal to acquire measurement results;

[0203] The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

[0204] In some embodiments of this disclosure, the first information further includes: device information, wherein the device information is used to indicate the source of the device.

[0205] In some embodiments of this disclosure, the third information includes at least one of the following:

[0206] The fifth instruction information is used to instruct the terminal to send the first information;

[0207] The sixth instruction information, wherein the sixth instruction information is used to determine the type of instruction information contained in the second information.

[0208] Figure 4A is an interactive schematic diagram illustrating a communication control method according to yet another embodiment of the present disclosure. As shown in Figure 4A, the embodiments of the present disclosure relate to a communication control method that can be used in network devices. The method includes:

[0209] Step S4101: Receive first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results.

[0210] Step S4102: Perform AI model training and / or AI inference based on the measurement results and the first information.

[0211] The communication control method disclosed in this embodiment may include at least one of steps S4101 to S4102. For example, step S4101 may be implemented as a standalone embodiment, step S4102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps S4101+S4102 may be implemented as standalone embodiments, but are not limited thereto.

[0212] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0213] Figure 4B is an interactive schematic diagram illustrating a communication control method according to yet another embodiment of the present disclosure. As shown in Figure 4B, the embodiments of the present disclosure relate to a communication control method that can be used in network devices. The method includes:

[0214] Step S4201: Send third information, wherein the third information is used to trigger the terminal to send the first information.

[0215] Step S4202: Receive first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results.

[0216] Step S4203: Receive fourth information, wherein the fourth information is used to indicate the measurement result, and the measurement result is obtained by the terminal based on the second information.

[0217] Step S4204: Perform AI model training and / or AI inference based on the measurement results and the first information.

[0218] The communication control method disclosed in this embodiment may include at least one of steps S4201 to S4204. For example, step S4201 may be implemented as a standalone embodiment, step S4202 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps S4201+S4202 may be implemented as standalone embodiments, but are not limited thereto.

[0219] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0220] In some embodiments of this disclosure, the measurement results include at least one of the following:

[0221] Community measurement results;

[0222] Beam measurement results.

[0223] In some embodiments of this disclosure, the second information includes at least one of the following:

[0224] First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal;

[0225] The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period;

[0226] The third indication information is used to indicate the time-domain processing method for the terminal to acquire measurement results;

[0227] The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

[0228] In some embodiments of this disclosure, the third information includes at least one of the following:

[0229] The fifth instruction information is used to instruct the terminal to send the first information;

[0230] The sixth instruction information, wherein the sixth instruction information is used to determine the type of instruction information contained in the second information.

[0231] In some embodiments of this disclosure, the first information further includes: device information, wherein the device information is used to indicate the source of the device; wherein the method further includes:

[0232] Based on the device information, identify multiple terminals that use the same method to acquire measurement results.

[0233] In some embodiments of this disclosure, AI model training is performed based on measurement results and first information, including:

[0234] Based on the first information, a first result is obtained, wherein the first result is the measurement result obtained by other terminals, and the terminals and other terminals use the same method to obtain the measurement result;

[0235] The AI ​​model is trained based on the measurement results and the initial results.

[0236] In some embodiments of this disclosure, AI inference is performed based on measurement results and first information, including:

[0237] Based on the first information, a first AI model is selected. The first AI model is trained using the measurement results collected during the training phase. The method of obtaining the measurement results collected during the training phase is the same as the method of obtaining the measurement results by the terminal.

[0238] AI inference is performed based on the first AI model.

[0239] Figure 5 is an interactive schematic diagram illustrating a communication control method according to another embodiment of the present disclosure. As shown in Figure 5, the embodiments of the present disclosure relate to a communication control method, which can be used in a communication system. The method includes:

[0240] Step S5101: The terminal sends first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results.

[0241] In step S5102, the network device performs AI model training and / or AI inference based on the measurement results and the first information.

[0242] The communication control method disclosed in this embodiment may include at least one of steps S5101 to S5102. For example, step S5101 may be implemented as a standalone embodiment, step S5102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps S5101+S5102 may be implemented as standalone embodiments, but are not limited thereto.

[0243] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0244] The following is an exemplary description of the above method.

[0245] Optionally, the following embodiments are available:

[0246] In the following examples, the terminal is UE.

[0247] For UE:

[0248] 1. The UE sends first information to the network, the first information being used to instruct the UE on how to obtain the measurement results.

[0249] As an example, the measurement results can be cell measurement results or beam measurement results.

[0250] 2. Based on 1, the second information (which may be included in the first information and is used to indicate the method by which the UE acquires the measurement results) includes any of the following:

[0251] a. The UE's L1 filtering process (an optional example of the first indication information).

[0252] As an example, the processing method may include taking the average, taking the maximum value, taking the median value, and taking the minimum value.

[0253] As an example, the L1 filtering method can be indicated by an identifier. If different UEs indicate the same identifier, it means that different UEs use the same processing method; if different UEs indicate different identifiers, it means that different UEs use different processing methods.

[0254] b. The number of measurement results acquired by the UE within a measurement period (an optional example of the second indication information).

[0255] As an example, a quantity identifier can be used to indicate the number of measurement results acquired by the UE within a measurement period. If different UEs indicate the same identifier, it means that different UEs acquired the same number of measurement results within a measurement period; if different UEs indicate different identifiers, it means that different UEs acquired different numbers of measurement results within a measurement period.

[0256] c. Time-domain processing method for UE to acquire measurement results (an optional example of third indication information).

[0257] As an example, the processing method can be a sliding window-based processing method or a non-sliding window-based processing method.

[0258] As an example, the time-domain processing method can be indicated by an identifier. If different UEs indicate the same identifier, it means that different UEs use the same processing method; if different UEs indicate different identifiers, it means that different UEs use different processing methods.

[0259] d. The method by which the UE obtains measurement results (an optional example of the fourth indication information).

[0260] As an example, the method of obtaining measurement results can be indicated by a measurement result acquisition method identifier. If different UEs indicate the same identifier, it means that different UEs use the same processing method; if different UEs indicate different identifiers, it means that different UEs use different processing methods.

[0261] 3. Based on 2, the first information may also include a device identifier (an optional example of device information).

[0262] As an example, when a UE reports a device identifier, the network can combine the device identifier with the identifier in point 2 to determine whether two UEs use the same method to obtain measurement results.

[0263] 4. Based on step 1, the UE receives the third information sent by the network and sends the first information.

[0264] Based on step 4, step 5 further instructs the UE on which second information to send. The UE sends the second information according to the third information.

[0265] 6. Based on 1-5, when the UE reports the measurement results, it should also report the method of obtaining the measurement results to the network.

[0266] As an example, the reported measurement results are used for model training on the network side or as input for AI inference on the network side.

[0267] As an example, the method of obtaining the measurement result can be determined by the second information, or by the device identifier and the second information.

[0268] For network use:

[0269] 1. Receive the first information sent by the UE, and determine the method used by the UE to obtain the measurement results based on the first information.

[0270] 2. Based on 1, during model training, data reported by UEs using the same measurement result acquisition method are used to train the AI ​​model.

[0271] 3. Based on point 1, during inference, the first AI model is determined to be used according to the method of obtaining the measurement results as input to the model. The method of obtaining the measurement results of the training data of the first AI model is the same as the method of obtaining the measurement results as input to the model.

[0272] This disclosure also provides embodiments of an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., a RAN) in any of the above methods.

[0273] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0274] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0275] Figure 6A is a schematic diagram of the structure of a terminal according to an embodiment of this disclosure. As shown in Figure 6A, the terminal 6100 may include at least one of a transceiver module 6101, a processing module 6102, etc. The terminal 6100 may include:

[0276] The transceiver module 6101 is used to send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device for AI model training and / or AI inference.

[0277] In some embodiments of this disclosure, the measurement results include at least one of the following:

[0278] Community measurement results;

[0279] Beam measurement results.

[0280] In some embodiments of this disclosure, the second information includes at least one of the following:

[0281] First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal;

[0282] The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period;

[0283] The third indication information is used to indicate the time-domain processing method for the terminal to acquire measurement results;

[0284] The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

[0285] In some embodiments of this disclosure, the first information further includes: device information, wherein the device information is used to indicate the source of the device.

[0286] In some embodiments of this disclosure, the transceiver module 6101 is used for:

[0287] Receive third information, wherein the third information is used to trigger the terminal to send the first information.

[0288] In some embodiments of this disclosure, the third information includes at least one of the following:

[0289] The fifth instruction information is used to instruct the terminal to send the first information;

[0290] The sixth instruction information, wherein the sixth instruction information is used to determine the type of instruction information contained in the second information.

[0291] In some embodiments of this disclosure, the processing module 6102 is configured to:

[0292] Measurements are performed based on the second piece of information to obtain the measurement results.

[0293] In some embodiments of this disclosure, the transceiver module 6101 is used for:

[0294] Send a fourth message, which indicates the measurement result.

[0295] Figure 6B is a schematic diagram of the structure of a network device according to an embodiment of this disclosure. As shown in Figure 6B, the network device 6200 may include at least one of a transceiver module 6201, a processing module 6202, etc. The network device 6200 may include:

[0296] The transceiver module 6201 is used to receive first information, wherein the first information includes at least second information related to the method by which the terminal obtains the measurement results.

[0297] The processing module 6202 is used to train and / or infer AI models based on measurement results and first information.

[0298] In some embodiments of this disclosure, the measurement results include at least one of the following:

[0299] Community measurement results;

[0300] Beam measurement results.

[0301] In some embodiments of this disclosure, the second information includes at least one of the following:

[0302] First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal;

[0303] The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period;

[0304] The third indication information is used to indicate the time-domain processing method for the terminal to acquire measurement results;

[0305] The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

[0306] In some embodiments of this disclosure, the transceiver module 6201 is used for:

[0307] Send a third message, which is used to trigger the terminal to send the first message.

[0308] In some embodiments of this disclosure, the third information includes at least one of the following:

[0309] The fifth instruction information is used to instruct the terminal to send the first information;

[0310] The sixth instruction information, wherein the sixth instruction information is used to determine the type of instruction information contained in the second information.

[0311] In some embodiments of this disclosure, the transceiver module 6201 is used for:

[0312] Receive fourth information, wherein the fourth information is used to indicate the measurement result, which is obtained by the terminal based on the second information.

[0313] In some embodiments of this disclosure, the first information further includes: device information, wherein the device information is used to indicate the source of the device; wherein the processing module 6202 is used to determine, based on the device information, multiple terminals using the same method of acquiring measurement results.

[0314] In some embodiments of this disclosure, the processing module 6202 is used for:

[0315] Based on the first information, a first result is obtained, wherein the first result is the measurement result obtained by other terminals, and the terminals and other terminals use the same method to obtain the measurement result;

[0316] The AI ​​model is trained based on the measurement results and the initial results.

[0317] In some embodiments of this disclosure, the processing module 6202 is used for:

[0318] Based on the first information, a first AI model is selected. The first AI model is trained using the measurement results collected during the training phase. The method of obtaining the measurement results collected during the training phase is the same as the method of obtaining the measurement results by the terminal.

[0319] AI inference is performed based on the first AI model.

[0320] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0321] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0322] Figure 7A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure. The communication device 7100 can be a terminal, a network device, a chip, chip system, or processor that supports the terminal in implementing any of the above methods, or a chip, chip system, or processor that supports the network device in implementing any of the above methods. The communication device 7100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0323] As shown in Figure 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. The communication device 7100 is used to execute any of the above methods.

[0324] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may also be located outside the communication device 7100.

[0325] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceivers 7103 perform at least one of the communication steps such as sending and / or receiving in the above method, and the processor 7101 performs other steps.

[0326] In some embodiments, a transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0327] In some embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuit 7104 is connected to the memory 7102, and the interface circuit 7104 can be used to receive signals from the memory 7102 or other devices, and can be used to send signals to the memory 7102 or other devices. For example, the interface circuit 7104 can read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0328] The communication device 7100 described in the above embodiments may be a terminal, a network device, or a third entity, but the scope of the communication device 7100 described in this disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A. The communication device may be a standalone device or a part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0329] Figure 7B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. For cases where the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the chip 7200 shown in Figure 7B, but it is not limited thereto.

[0330] Chip 7200 includes one or more processors 7201, which are used to perform any of the above methods.

[0331] In some embodiments, chip 7200 further includes one or more interface circuits 7202. Optionally, the interface circuit 7202 is connected to memory 7203, and the interface circuit 7202 can be used to receive signals from memory 7203 or other devices, and the interface circuit 7202 can be used to send signals to memory 7203 or other devices. For example, the interface circuit 7202 can read instructions stored in memory 7203 and send the instructions to processor 7201.

[0332] In some embodiments, the interface circuit 7202 performs at least one of the communication steps such as sending and / or receiving in the above method, while the processor 7201 performs other steps.

[0333] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0334] In some embodiments, chip 7200 further includes one or more memories 7203 for storing instructions. Optionally, all or part of the memories 7203 may be located outside of chip 7200.

[0335] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 7100, cause the communication device 7100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0336] This disclosure also provides a program product that, when executed by the communication device 7100, causes the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0337] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0338] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program can be transferred from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0339] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0340] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0341] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A communication control method characterized by comprising: Executed by a terminal; the method includes: Send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results, and the measurement results and the second information are used by the network device to perform artificial intelligence (AI) model training and / or AI inference.

2. The method of claim 1, wherein, The measurement results include at least one of the following: Community measurement results; Beam measurement results.

3. The method according to any one of claims 1 to 2, wherein, The second information includes at least one of the following: First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal; The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period; The third indication information is used to indicate the time-domain processing method for the terminal to acquire the measurement results; The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

4. The method according to any one of claims 1 to 3, characterized in that, The first information also includes: device information, wherein the device information is used to indicate the source of the device.

5. The method according to any one of claims 1 to 4, wherein The method further includes: Receive third information, wherein the third information is used to trigger the terminal to send the first information.

6. The method of claim 5, wherein, The third information includes at least one of the following: The fifth instruction information is used to instruct the terminal to send the first information; The sixth indication information, wherein the sixth indication information is used to determine the type of indication information contained in the second information.

7. The method according to any one of claims 1 to 6, wherein The method further includes: The measurement is performed based on the second information to obtain the measurement result.

8. The method according to any one of claims 1 to 7, wherein The method further includes: Send a fourth message, wherein the fourth message is used to indicate the measurement result.

9. A communication control method characterized by comprising: Performed by a network device; the method includes: Receive first information, wherein the first information includes at least: second information related to the method by which the terminal acquires the measurement results; AI model training and / or AI inference are performed based on the measurement results and the first information.

10. The method of claim 9, wherein, The measurement results include at least one of the following: Community measurement results; Beam measurement results.

11. The method according to any one of claims 9-10, wherein, The second information includes at least one of the following: First indication information, wherein the first indication information is used to indicate the layer 1 filtering processing method adopted by the terminal; The second indication information is used to indicate the number of measurement results acquired by the terminal during the measurement period; The third indication information is used to indicate the time-domain processing method for the terminal to acquire the measurement results; The fourth indication information is used to indicate the type of method by which the terminal acquires the measurement results.

12. The method according to any one of claims 9 to 11, wherein, The method further includes: Send a third message, wherein the third message is used to trigger the terminal to send the first message.

13. The method of claim 12, wherein, The third information includes at least one of the following: The fifth instruction information is used to instruct the terminal to send the first information; The sixth indication information, wherein the sixth indication information is used to determine the type of indication information contained in the second information.

14. The method according to any one of claims 9 to 13, characterized in that, The method further includes: Receive fourth information, wherein the fourth information is used to indicate a measurement result, the measurement result being obtained by the terminal based on the second information.

15. The method according to any one of claims 9 to 14, wherein, The first information further includes: device information, wherein the device information is used to indicate the source of the device; wherein the method further includes: Based on the device information, multiple terminals that use the same method to acquire measurement results are identified.

16. The method of any one of claims 9-15, wherein, The step of training the AI ​​model based on the measurement results and the first information includes: Based on the first information, a first result is obtained, wherein the first result is a measurement result obtained by other terminals, and the terminal and the other terminals use the same method to obtain the measurement result; The AI ​​model is trained based on the measurement results and the first result.

17. The method of any one of claims 9-16, wherein, The AI ​​reasoning based on the measurement results and the first information includes: Based on the first information, a first AI model is selected, wherein the first AI model is trained using measurement results collected during the training phase, and the method of obtaining the measurement results collected during the training phase is the same as the method of obtaining the measurement results by the terminal. Perform AI inference based on the first AI model.

18. A communication control method characterized by comprising: The method includes: The terminal sends first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results; The network device performs AI model training and / or AI inference based on the measurement results and the first information.

19. A terminal, characterized by The terminal includes: A transceiver module is used to send first information, wherein the first information includes at least: second information related to the method by which the terminal obtains measurement results, and the measurement results and the second information are used by the network device for AI model training and / or AI inference.

20. A network device, comprising: The network device includes: The transceiver module is used to receive first information, wherein the first information includes at least: second information related to the method by which the terminal obtains the measurement results; The processing module is used to perform AI model training and / or AI inference based on the measurement results and the first information.

21. A communications device, characterized by include: One or more processors; The processor is used to execute the communication control method according to any one of claims 1-18.

22. A storage medium, the storage medium storing instructions, wherein, When the instruction is executed on the communication device, it causes the communication device to perform the communication control method as described in any one of claims 1-18.

23. A computer program product, characterised in that, It includes a computer program that, when executed by a processor, implements the communication control method according to any one of claims 1-18.

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