Configuration method, terminal device, and network device

Abstract

The present application relates to a configuration method, a terminal device, a network device, a chip, a computer readable storage medium, a computer program product, a computer program, and a communication system. The configuration method comprises: a terminal device receives first configuration information sent by a network device, wherein the first configuration information is used for indicating a first parameter, the first parameter is used for the terminal device to execute a first function, and the first function comprises determining, on the basis of an AI model, a first candidate cell for cell handover. The embodiments of the present application can improve the handover performance.

Description

Configuration method, terminal device, and network device Technical Field

[0001] The present application relates to the field of communications, and more specifically, to a configuration method, terminal equipment, network equipment, chip, computer-readable storage medium, computer program product, computer program, and communication system. Background Art

[0002] In cellular communication systems, mobility management is a core process in the control plane. To enable handover between cells, terminal devices need to submit measurement reports. Measurement reports can be submitted based on the judgment of measurement events, and network equipment can make handover decisions based on the reported information. However, due to the complexity of radio wave propagation, the fixed process of processing measurement results, judging measurement events, and making handover decisions to determine candidate handover cells may not necessarily produce optimal results. Improving handover performance requires consideration.

[0003] Summary of the Invention

[0004] Embodiments of the present application provide a configuration method, terminal device, network device, chip, computer-readable storage medium, computer program product, computer program, and communication system, which can improve switching performance.

[0005] This embodiment of the present application provides a configuration method, including:

[0006] The terminal device receives first configuration information sent by the network device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an AI (Artificial Intelligence) model.

[0007] This embodiment of the present application provides a configuration method, including:

[0008] The network device sends first configuration information to the terminal device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

[0009] An embodiment of the present application provides a terminal device, including:

[0010] The first communication unit is used to receive first configuration information sent by a network device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

[0011] An embodiment of the present application provides a network device, including:

[0012] The second communication unit is used to send first configuration information to the terminal device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

[0013] An embodiment of the present application provides a terminal device, comprising: a transceiver, a processor, and a memory. The memory is used to store a computer program, the transceiver is used to communicate with other devices, and the processor is used to call and execute the computer program stored in the memory to enable the terminal device to perform the above-mentioned configuration method.

[0014] An embodiment of the present application provides a network device, comprising: a transceiver, a processor, and a memory. The memory is used to store a computer program, the transceiver is used to communicate with other devices, and the processor is used to call and execute the computer program stored in the memory to enable the network device to perform the above-mentioned configuration method.

[0015] An embodiment of the present application provides a chip for implementing the above configuration method.

[0016] Specifically, the chip includes: a processor, which is used to call and run a computer program from a memory, so that a device equipped with the chip executes the above configuration method.

[0017] An embodiment of the present application provides a computer-readable storage medium for storing a computer program, which, when executed by a device, enables the device to execute the above-mentioned configuration method.

[0018] An embodiment of the present application provides a computer program product, including computer program instructions, which enable a computer to execute the above configuration method.

[0019] An embodiment of the present application provides a computer program, which, when executed on a computer, enables the computer to execute the above-mentioned configuration method.

[0020] An embodiment of the present application provides a communication system, including a terminal device and a network device for executing the above-mentioned configuration method.

[0021] In an embodiment of the present application, a network device can configure a first parameter for a terminal device so that the terminal device can perform a first function, determine a first candidate cell for cell switching based on an AI model, without sequentially executing multiple links in an inherent process to implement a switching decision, and utilize the nonlinear processing capability of an AI algorithm to more accurately determine candidate cell information, thereby improving switching performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application.

[0023] FIG2 is a schematic diagram of an application method of a TTT timer in the related art.

[0024] FIG3 is a schematic diagram of a measurement model in the related art.

[0025] FIG4 is a schematic flowchart of a configuration method according to an embodiment of the present application.

[0026] FIG5 is a schematic flowchart of a configuration method according to another embodiment of the present application.

[0027] FIG6 is a schematic diagram of an application example of the configuration method according to an embodiment of the present application.

[0028] FIG7 is a schematic diagram of another application example of the configuration method according to an embodiment of the present application.

[0029] FIG8 is a schematic diagram of another application example of the configuration method according to an embodiment of the present application.

[0030] FIG9 is a schematic block diagram of a terminal device according to an embodiment of the present application.

[0031] FIG10 is a schematic block diagram of a terminal device according to another embodiment of the present application.

[0032] FIG11 is a schematic block diagram of a terminal device according to another embodiment of the present application.

[0033] FIG12 is a schematic block diagram of a network device according to an embodiment of the present application.

[0034] FIG13 is a schematic block diagram of a communication device according to an embodiment of the present application.

[0035] FIG14 is a schematic block diagram of a chip according to an embodiment of the present application.

[0036] FIG15 is a schematic block diagram of a communication system according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0038] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: Long Term Evolution (LTE) system, Advanced Long Term Evolution (LTE-A) system, New Radio (NR) system, NR system evolution system, LTE on unlicensed spectrum (LTE-U) system, NR on unlicensed spectrum (NR-based access to unlicensed spectrum, NR-U) system, Non-Terrestrial Networks (NTN) system, Universal Mobile Telecommunication System (UMTS), Wireless Local Area Networks (WLAN), Wireless Fidelity (WiFi), Fifth Generation Communication (5G) system, Sixth Generation Communication (6G) system or other communication systems.

[0039] Generally speaking, traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, mobile communication systems will not only support traditional communications, but will also support, for example, device-to-device (D2D) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), vehicle-to-vehicle (V2V) communication, or vehicle-to-everything (V2X) communication, etc. The embodiments of the present application can also be applied to these communication systems.

[0040] In one embodiment, the communication system in the embodiment of the present application can be applied to a carrier aggregation (CA) scenario, a dual connectivity (DC) scenario, and a standalone (SA) networking scenario.

[0041] In one embodiment, the communication system in the embodiment of the present application can be applied to an unlicensed spectrum, wherein the unlicensed spectrum can also be considered as a shared spectrum; or, the communication system in the embodiment of the present application can also be applied to an authorized spectrum, wherein the authorized spectrum can also be considered as an unshared spectrum.

[0042] The embodiments of the present application describe various embodiments in conjunction with network devices and terminal devices, wherein the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

[0043] The terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a next-generation communication system such as an NR network, or a terminal device in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0044] In an embodiment of the present application, the terminal device can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.).

[0045] In an embodiment of the present application, the terminal device may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, or a wireless terminal device in a smart home, etc.

[0046] As an example and not a limitation, in the embodiment of the present application, the terminal device may also be a wearable device. Wearable devices may also be called wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0047] In an embodiment of the present application, the network device may be a device for communicating with a mobile device. The network device may be an access point (AP) in a WLAN, an evolved base station (eNB or eNodeB) in LTE, or a relay station or access point, or a vehicle-mounted device, a wearable device, and a network device (gNB) in an NR network, or a network device in a future evolved PLMN network or a network device in an NTN network, etc.

[0048] As an example and not a limitation, in an embodiment of the present application, the network device may have a mobile feature, for example, the network device may be a mobile device. Alternatively, the network device may be a satellite or a balloon station. For example, the satellite may be a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite, a geostationary earth orbit (GEO) satellite, a high elliptical orbit (HEO) satellite, etc. Optionally, the network device may also be a base station set up in a location such as land or water.

[0049] In an embodiment of the present application, the network device can provide services for a cell, and the terminal device communicates with the network device through the transmission resources used by the cell (for example, frequency domain resources, or spectrum resources). The cell can be a cell corresponding to the network device (for example, a base station). The cell can belong to a macro base station or a base station corresponding to a small cell. The small cells here may include: metro cells, micro cells, pico cells, femto cells, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

[0050] FIG1 exemplarily illustrates a communication system 100. The communication system includes a network device 110 and two terminal devices 120. In one embodiment, the communication system 100 may include multiple network devices 110, and each network device 110 may include a different number of terminal devices 120 within its coverage area, which is not limited in this embodiment of the present application.

[0051] In one embodiment, the communication system 100 may further include other network entities such as a Mobility Management Entity (MME) and an Access and Mobility Management Function (AMF), which is not limited in this embodiment of the present application.

[0052] Among them, the network equipment may include access network equipment and core network equipment. That is, the wireless communication system also includes multiple core networks for communicating with the access network equipment. The access network equipment can be an evolutionary base station (evolutional node B, abbreviated as eNB or e-NodeB) macro base station, micro base station (also called "small base station"), pico base station, access point (AP), transmission point (TP) or new generation base station (new generation Node B, gNodeB), etc. in a long-term evolution (LTE) system, a next-generation (mobile communication system) (next radio, NR) system or an authorized auxiliary access long-term evolution (LAA-LTE) system.

[0053] It should be understood that in the embodiments of the present application, a device having a communication function in a network / system may be referred to as a communication device. Taking the communication system shown in Figure 1 as an example, the communication device may include a network device and a terminal device having a communication function. The network device and the terminal device may be specific devices in the embodiments of the present application and will not be described in detail here. The communication device may also include other devices in the communication system, such as a network controller, a mobility management entity, and other network entities, which are not limited in the embodiments of the present application.

[0054] It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship.

[0055] It should be understood that the "indication" mentioned in the embodiments of this application can be a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association between A and B.

[0056] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and being indicated, configuration and being configured, etc.

[0057] To facilitate understanding of the technical solutions of the embodiments of the present application, the relevant technologies of the embodiments of the present application are described below. The following relevant technologies can be arbitrarily combined with the technical solutions of the embodiments of the present application as optional solutions, and they all fall within the protection scope of the embodiments of the present application.

[0058] In 3GPP (3rd Generation Partnership Project) cellular communication systems, switching RRC (Radio Resource Control) connections between different cells (i.e., mobility management) is a core process in the control plane of the standard specification. To enable handover between cells, the UE needs to report measurement reports.

[0059] In second-generation communication systems, measurement reports are always reported at a certain period. Starting from third-generation communication systems, including CDMA (Code Division Multiple Access), fourth-generation communication systems LTE, and fifth-generation communication systems NR, measurement reports can be mainly divided into three types based on the reporting method:

[0060] 1. Periodic reporting;

[0061] 2. Reporting based on measurement events;

[0062] 3. Report based on measurement events and continue reporting periodically thereafter.

[0063] Regardless of the reporting format of the measurement report, the measurement report can include specific measurement events and / or measurement results, such as the signal strength or signal quality of the cell; where the signal strength is, for example, RSRP (Reference Signal Receiving Power), with dBm as the dimension; the signal quality is, for example, RSRQ (Reference Signal Receiving Quality), with dB as the dimension. The reported cells include the current serving cell and neighboring cells. The measurement object can be the same frequency, different frequency, or the frequency of different communication systems.

[0064] The triggering of a measurement event includes the following basic elements:

[0065] 1. Measurement results, including measurement results of the serving cell and / or neighboring cells, such as the signal strength of the cells.

[0066] 2. Comparison parameters, such as thresholds, hysteresis values, offset values, etc. The dimensions of general measurement results in standard protocols are such that the larger the value, the higher the signal strength or quality. Absolute comparison refers to comparing the measurement value of a cell with a certain threshold. In this case, if the measurement result is greater than the sum of the threshold and the hysteresis value, it means that the entry condition is met, and if the measurement result is less than the difference between the threshold and the hysteresis value, it means that the exit condition is met. Relative comparison usually refers to comparing the measurement results of the neighboring cell with the measurement results of the serving cell. Before comparison, each cell needs to add its own relevant offset value. For the serving cell, the offset value (Off_event) related to the corresponding event must also be added. Finally, the hysteresis value (Hys) also needs to be considered when comparing. Taking the A3 event as an example, the measurement results, offset values, etc. of the serving cell are marked with s, and the measurement results, offset values, etc. of the neighboring cell are marked with n. Then, the entry condition of the A3 event can be expressed as:

[0067] Mn+Ofn>Ms+Ofs+Hys+Off_event;

[0068] The exit condition of event A3 can be expressed as:

[0069] Mn+Ofn <Ms+Ofs-Hys+Off_event;

[0070] Among them, Mn represents the measurement result of the neighboring cell, Ofn represents the offset value related to the neighboring cell; Ms represents the measurement result of the serving cell, Ofs represents the offset value related to the serving cell, Hys represents the hysteresis value, and Off_event represents the offset value related to the event.

[0071] 3. A timer that indicates the robustness of measurement results, namely the TTT (time to trigger) timer. Figure 2 illustrates the application of the TTT timer. When a cell meets the entry conditions for an event at time T0, the TTT timer starts. When the TTT timer expires, if the cell continues to meet the entry conditions for the event, it indicates that the cell has triggered the measurement event.

[0072] In standard protocols, the measurement results used for measurement event determination are those filtered at Layer 3, while the initial measurement results within the UE are physical layer measurements of a single beam. Figure 3 shows a schematic diagram of the measurement model, illustrating the process from single-beam measurement results to measurement event triggering.

[0073] Specifically, as shown in Figure 3, for the measurement results obtained by beam measurement of L1 (Layer 1), the UE first consolidates / selects them, and performs weighted averaging on the measurement results of the beams that meet the conditions in the cell to obtain a cell-level measurement result of Layer 1. Meeting the conditions means that the L1 beam measurement result must be greater than a pre-configured threshold value. If at least one beam meets the conditions, the measured power (microwatt, mW) of the beams that meet the conditions is weighted averaged and then converted into a measurement value with the dimension of dBm. If no beam meets the conditions, the beam with the highest measurement value is used as the merging result.

[0074] The cell-level measurement results of layer 1 are then filtered in layer 3 to obtain the cell-level measurement results of layer 3. The filtering formula of layer 3 is as follows: n =(1-a)*F n-1 +a*M n

[0075] Among them, parameter a is the L3 (Layer 3) filter coefficient, which is converted from another configuration parameter. nis the measured value of the current period, F n-1 is the value after the last L3 filtering, F n This is the value after the current L3 filtering.

[0076] In the measurement model shown in Figure 3, operational details such as beam selection / combining, Layer 1 filtering of beam measurement results, Layer 3 filtering of cell and / or beam measurement results, and the measurement event decision process (measurement report evaluation) are specified in detail in the protocol and controlled by network-configured RRC parameters. Due to the complexity of radio wave propagation, this artificially defined linear operational process is not necessarily the optimal solution. In other words, the measurement results obtained using this measurement model and the handover decisions (including candidate cell information) ultimately triggered by the measurement events inferred from these measurement results are not necessarily optimal.

[0077] The embodiments of this application are mainly intended to solve the above-mentioned technical problems.

[0078] FIG4 is a schematic flow chart of a configuration method according to an embodiment of the present application. The method can optionally be applied to the terminal device in the system shown in FIG1 , but is not limited thereto. The method includes:

[0079] S410. The terminal device receives first configuration information sent by the network device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function; the first function includes determining a first candidate cell for cell switching based on an AI model.

[0080] Corresponding to the above method, FIG5 is a schematic flow chart of a configuration method according to another embodiment of the present application. The method can optionally be applied to the network device in the system shown in FIG1 , but is not limited thereto. The method includes:

[0081] S510. The network device sends first configuration information to the terminal device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an AI model.

[0082] According to the above method, after receiving the first configuration information, the terminal device can perform the first function based on the first parameter. By performing the first function, a first candidate cell for cell handover can be obtained based on the AI ​​model. Optionally, the first candidate cell can be a candidate cell output by the AI ​​model.

[0083] It should be noted that the AI ​​model in the embodiments of the present application can be implemented based on ML (Machine Learning), that is, the AI ​​model can include an ML model. In some examples, the AI ​​model can be described as an AI / ML model. In addition, the AI ​​model is implemented based on an AI algorithm or AI function and can similarly be described as an AI / ML algorithm or AI / ML function.

[0084] According to an embodiment of the present application, a network device can configure a first parameter for a terminal device so that the terminal device can perform a first function, and determine a first candidate cell for cell switching based on an AI model, without sequentially executing multiple links in an inherent process to implement a switching decision to obtain the first candidate cell. Specifically, the AI ​​model can be used to implement a reasonable inference from the measurement result to the first candidate cell, so as to replace one or more processes of merging, filtering, measuring event judgment, and switching decision of the measurement results in the related art, thereby eliminating the need to perform the above process step by step according to artificially prescribed linear operations. On the one hand, the nonlinear inference capability of the AI ​​algorithm can be used to more accurately determine the candidate cell information. On the other hand, compared to the related art, which can only obtain the local optimal solution of each process by gradually implementing the above process, the use of the AI ​​algorithm to replace multiple processes can obtain a relatively global optimal solution. Therefore, according to the above method, the candidate cell for switching can be determined more accurately, and frequent switching caused by unreasonable switching can be avoided, thereby improving switching performance and system stability.

[0085] In some embodiments, before the terminal device receives the first configuration information sent by the network device, the above configuration method may further include: the terminal device sends first reporting information to the network device; wherein the first reporting information is used to indicate that the terminal device supports the first function.

[0086] Accordingly, in some embodiments, before the network device sends the first configuration information to the terminal device, the method further includes: the network device receives first reporting information sent by the terminal device; wherein the first reporting information is used to indicate that the terminal device supports the first function.

[0087] According to the above embodiment, the network device can configure the first parameter for the terminal device based on the first reporting information.

[0088] In some embodiments, the first reporting information is used to indicate that the terminal device supports the use of the AI ​​model to determine the first candidate cell, and the network device configures the first parameter for the terminal device based on the triggering of the first reporting information.

[0089] Alternatively, the first reporting information is used to indicate a function supported by the terminal device. When the function is the first function, the network device can configure the corresponding first parameter for the first function. Exemplarily, the system can use different functions to determine the candidate cells for cell switching. Different functions require different parameter configurations. The terminal device can send a first reporting information to indicate the functions it supports, and the network device configures corresponding parameters for the functions supported by the terminal device. Optionally, different functions may include determining the first candidate cell based on traditional methods and determining the first candidate cell based on an AI model. Alternatively, different functions may include determining the first candidate cell according to different AI models, that is, different functions correspond to different capabilities of the AI ​​algorithm, so that the network device configures corresponding parameters for different AI models.

[0090] According to the above embodiment, by means of the terminal device reporting capabilities and the network device configuring parameters, the network device can be configured in a targeted manner, thereby improving the switching performance for specific scenarios.

[0091] In some embodiments, the input information of the AI ​​model includes measurement results of the serving cell of the terminal device and / or measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

[0092] In other words, by inputting the measurement results of the terminal device's serving cell and / or neighboring cells into the AI ​​model, the AI ​​model outputs a first candidate cell. This means that the first function implemented based on the AI ​​model can at least replace the calculation of candidate cell information in measurement event determination and handover decision-making in related technologies, thereby achieving a more comprehensive optimal solution.

[0093] In some embodiments, the measurement results include beam measurement results and / or cell-level measurement results. For example, the measurement results of the serving cell may include beam measurement results of the serving cell and / or cell-level measurement results of the serving cell. The measurement results of the neighboring cell may include beam measurement results of the neighboring cell and / or cell-level measurement results of the neighboring cell.

[0094] In some embodiments, the AI ​​model's input includes beam measurement results of the serving cell and / or neighboring cells, and its output includes handover candidate cells. Thus, the AI ​​model-based first function can replace processes such as beam combining, layer 3 filtering, and measurement event determination in the measurement model.

[0095] In some embodiments, the input information of the AI ​​model may include the beam measurement results of the serving cell and / or the beam measurement results of the neighboring cells, as well as the cell-level measurement results of the serving cell and / or the cell-level measurement results of the neighboring cells. In this way, the accuracy of the inference results of the AI ​​model can be improved by utilizing different types of measurement results.

[0096] In actual applications, the measurement result type included in the input information of the AI ​​model can be configured according to the function type of the AI ​​model, the measurement event associated with the AI ​​model, etc. For example, if the AI ​​model is used to obtain the first candidate cell based on the beam measurement result, that is, to replace the processes such as beam merging, layer 3 filtering, and measurement event judgment, then the input information of the AI ​​model may include the beam measurement result of the serving cell and / or the beam measurement result of the neighboring cell. For another example, different measurement events are associated with different AI models. For example, the first AI model can be associated with the first measurement event to replace the event judgment and handover decision process related to the first measurement event, and the first measurement event is related to the measurement result of the neighboring cell. In this case, the input information of the first AI model includes the measurement result of the neighboring cell; the second AI model can be associated with the second measurement event to replace the event judgment and handover decision process related to the second measurement event. In this case, the second measurement event is related to the measurement results of the neighboring cell and the serving cell. In this case, the input information of the second AI model includes the measurement results of the neighboring cell and the serving cell. It can be understood that in specific implementation, the input and output configuration of the AI ​​model can be performed according to protocol agreements, system agreements or scenario requirements, and the AI ​​model can be trained to output the first candidate cell for specific input information. The types of measurement results that may be included in the input information of the AI ​​model are not listed here one by one.

[0097] In some embodiments, the input information of the AI ​​model includes a first parameter. Optionally, the input information of the AI ​​model includes the aforementioned measurement results and the first parameter. That is, the first parameter of the network device configuration can be used as input to the AI ​​model. By inputting the first parameter of the network configuration into the AI ​​model, the AI ​​model can obtain reasonable output based on the network configuration information, thereby ensuring system performance.

[0098] In some embodiments, the first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function. Specifically, the first indication information can be used to indicate to the terminal device that the first function can currently be executed, that is, to trigger the terminal device to use the AI ​​model to determine the first candidate cell.

[0099] In some embodiments, the first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

[0100] Optionally, the configuration parameters of the measurement event may include RRC configuration parameters, such as RRC configuration parameters in one or more processes such as beam measurement result merging, measurement result filtering, and measurement event determination. For example, the RRC configuration parameters may include one or more parameters such as a filter coefficient, a TTT timer duration configuration, a threshold for event determination, an offset value, and a hysteresis value.

[0101] In some embodiments, the input information of the AI ​​model may include the configuration parameters of the above-mentioned measurement event. The AI ​​model may determine the first candidate cell with reference to the configuration parameters, so that the AI ​​model can output reasonable results for different configuration parameters, thereby improving the generalization effect of the model.

[0102] In some embodiments, the first parameter may include only simple indication information, i.e., the first indication information, and the input information of the AI ​​model may not include the configuration parameters of the measurement event. In this case, the AI ​​model can be trained using switching statistics corresponding to different configuration parameters, so that the AI ​​model can achieve good generalization effect even for different configuration parameters.

[0103] Optionally, the control parameters reported for the first candidate cells may include the number of first candidate cells. Similarly, the input information of the AI ​​model may also include the control parameters reported for the first candidate cells. The AI ​​model may refer to the control parameters reported for the first candidate cells and output the expected number of first candidate cells.

[0104] In some embodiments, the input information of the AI ​​model may also include status parameters of the terminal device.

[0105] Figure 6 is a schematic diagram of an application example of the configuration method of an embodiment of the present application. As shown in Figure 6, the input information of the AI ​​model may include the measurement results of the serving cell, the measurement results of the neighboring cells, and other parameters. Here, the other parameters may include the state parameters of the terminal device. Compared with the measurement model of the related art that uses a simple RRM measurement result without taking additional factors into account, the use of the AI ​​model to infer the candidate cell can consider the state parameters of the terminal device, thereby referring to the state parameters for inference, which can improve the accuracy of the inference result of the AI ​​model.

[0106] In some embodiments, the above-mentioned state parameters include one or more of the terminal device's location, moving speed, moving direction, transmission power, and antenna orientation.

[0107] The above-mentioned AI model can adopt an online or offline training method. The data set used for model training can come from the switching statistics of the existing network. In the existing network, the measurement results based on the traditional measurement template will produce reasonable switching decisions most of the time, but not every switching decision is reasonable. These reasonable switching decisions and the measurement results for a period of time before and / or after the switching decision, which may include the measurement results of the serving cell and the neighboring cell, can be used as data sets for algorithm training. When the training data set is large enough, the AI ​​model can learn these nonlinear algorithms that can make reasonable judgments, and record the learning results through numerous parameters within the algorithm (mainly the weights of the neural network). In this way, when an actual situation arises, as long as certain measurement results are output to the algorithm, the algorithm can make a reasonable decision. Specifically, in some embodiments, the AI ​​model is trained based on a first data set, and the first data set includes at least one of the following:

[0108] Information on candidate cells used in handover decisions contained in handover statistics;

[0109] The measurement results in the first time window before the switching decision;

[0110] measurement results within a second time window after the handover decision;

[0111] Configuration parameters for measurement events related to handover decisions;

[0112] Status parameters of the terminal device that are relevant to the handover decision.

[0113] Optionally, the switching decision associated with the data in the first data set may be a reasonable decision after screening.

[0114] Exemplarily, the first data set may include candidate cell information in the handover decision, measurement results within a first time window before the handover decision, and measurement results within a second time window after the handover decision, so that the AI ​​model can determine the first candidate cell based on the measurement results.

[0115] Exemplarily, the first data set may include candidate cell information in the switching decision, measurement results within a first time window before the switching decision, measurement results within a second time window after the switching decision, and configuration parameters of measurement events related to the switching decision, so that the AI ​​model can determine the first candidate cell based on the measurement results and achieve better generalization effect for different configuration parameters.

[0116] Exemplarily, the first data set may include candidate cell information in the switching decision, measurement results within a first time window before the switching decision, measurement results within a second time window after the switching decision, and state parameters of the terminal device related to the switching decision, so that the AI ​​model can determine the first candidate cell based on the measurement results and consider different state parameters to improve the accuracy of the inference results.

[0117] It should be noted that the information in the first dataset can be adjusted accordingly based on the input and output configuration of the AI ​​model. In practical applications, the information in the first dataset can be set based on system specifications, protocol specifications, scenario requirements, etc., and can specifically include one or more of the above information, which are not listed here.

[0118] Figure 7 is a schematic diagram of an application example of the configuration method according to an embodiment of the present application. As shown in Figure 7, the first data set may include the measurement results of the neighboring cells before a reasonable decision, the measurement results of the serving cell before a reasonable decision, and other parameters, such as RRC configuration parameters or status parameters of the terminal device, and the above information is used as input information of the AI ​​model during the training process. The first data set may also include candidate cell information in a reasonable switching decision as label data for the output information of the AI ​​model. According to this application example, the AI ​​model can learn to perform reasonable processing from the neighboring cell measurement results and the serving cell measurement results to determine the capabilities of the candidate cell. In actual operation, as long as the measurement results of the neighboring cells, the measurement results of the serving cell, and other parameters are input, the AI ​​model can output reasonable decision information, which includes the first candidate cell, thereby improving the switching performance.

[0119] Optionally, after the terminal device determines the first candidate cell, the network device may make the final handover decision. In some embodiments, the configuration method may further include: the terminal device sending second reporting information to the network device; wherein the second reporting information includes relevant information about the first candidate cell.

[0120] Accordingly, in some embodiments, the configuration method may further include: the network device receives second reporting information sent by the terminal device; wherein the second reporting information includes relevant information of the first candidate cell.

[0121] The relevant information of the first candidate cell can be used by the network device to make a handover decision. Optionally, the first reporting information can also include measurement results of the serving cell and / or measurement results of neighboring cells to assist the network device in making a handover decision.

[0122] Optionally, in a conditional handover scenario, where the network device configures candidate cells for conditional handover for the terminal device, the terminal device may autonomously switch based on the output of the AI ​​model. Specifically, in some embodiments, the first candidate cell includes a second candidate cell for conditional handover configured by the network device; the configuration method may further include: the terminal device autonomously switching to the second candidate cell first.

[0123] Optionally, before the terminal device autonomously switches to the second candidate cell in priority, the network device sends a switching command to the terminal device, which includes a candidate cell list for conditional switching configured by the network device. The candidate cell list may include multiple candidate cells, including the second candidate cell. When the first candidate cell determined based on the AI ​​model includes the second candidate cell, the terminal device may autonomously switch to the second candidate cell in priority. The priority here means that the terminal device gives priority to the candidate cells provided by the network device to respect the configuration of the network device. For example, when the first candidate cell includes the second candidate cell and other candidate cells that are not in the candidate cell list for conditional switching, the terminal device switches to the second candidate cell instead of other candidate cells. Autonomous switching means that the terminal device can decide to switch to the second candidate cell on its own without reporting to the network device.

[0124] It is understood that, in the event that the first candidate cell does not include the second candidate cell, the terminal device may also switch to the first candidate cell. For example, after reporting the first candidate cell to the network device, the terminal device may switch to the first candidate cell based on the handover decision of the network device. In other words, the terminal device may be allowed to switch to a candidate cell that is not in the candidate cell list for conditional handover.

[0125] According to the above embodiment, the terminal device can determine the second candidate cell based on the candidate cell list for conditional switching and the output of the AI ​​model, and thus autonomously switch to the second candidate cell first, thereby improving switching efficiency.

[0126] In the above embodiment, if the first candidate cell output by the AI ​​model obtained when the terminal device executes the first function includes a second candidate cell, and the terminal device needs to send a second reporting information to the network device, the second reporting information may not include information about the second candidate cell, that is, the terminal device can switch to the second candidate cell autonomously without reporting to the network device for decision-making.

[0127] In some embodiments, the terminal device giving priority to autonomously switching to the second candidate cell may include: when a conditional switching triggering event associated with the second candidate cell is not triggered, the terminal device giving priority to autonomously switching to the second candidate cell.

[0128] Specifically, when the network device configures a list of candidate cells for conditional switching in a switching command, it can also configure a conditional switching trigger event associated with each candidate cell. When the conditional switching trigger event associated with the second candidate cell is triggered, the terminal device can switch to the second candidate cell; when the conditional switching trigger event associated with the second candidate cell is not triggered, the terminal device can determine whether to autonomously switch to the second candidate cell based on the output of the AI ​​model. In this way, compatibility with conditional switching is achieved, a more flexible switching mechanism can be provided, and switching performance can be improved.

[0129] Optionally, in the LTM (L1 / L2 triggered Mobility) handover scenario, that is, when the network device configures an LTM candidate cell for the terminal device, the terminal device can perform autonomous handover-related processing based on the AI ​​model. Specifically, in some embodiments, the first candidate cell includes a third candidate cell for LTM handover configured by the network device; the above configuration method may also include at least one of the following terminal device autonomous behaviors:

[0130] (1) The terminal device performs advance synchronization with the third candidate cell. For example, the terminal device may send a preamble to trigger the network device of the third candidate cell to send a response message containing TA (timing advance).

[0131] (2) The terminal device activates a beam for communicating with the third candidate cell. For example, the terminal device activates the TCI state of the third candidate cell.

[0132] (3) The terminal device preferentially switches to the third candidate cell; for example, the terminal device turns to the third candidate cell and establishes a communication connection.

[0133] Optionally, before the terminal device executes the above-mentioned autonomous behavior, the network device sends a switching command to the terminal device, which includes a candidate cell list for LTM switching configured by the network device. The candidate cell list may include multiple candidate cells, including a third candidate cell. When the first candidate cell determined based on the AI ​​model includes the third candidate cell, the terminal device may execute any one of the above-mentioned terminal device autonomous behaviors (1) to (3). The autonomous behavior here means that the terminal device does not need to report to the network device and can execute it on its own.

[0134] According to the above embodiment, the terminal device can determine the third candidate cell based on the candidate cell list of LTM switching and the output of the AI ​​model, so as to autonomously achieve at least one of early synchronization with the third candidate cell, beam activation, and cell switching, thereby improving switching efficiency.

[0135] In the above embodiment, if the first candidate cell output by the AI ​​model obtained when the terminal device executes the first function includes a third candidate cell, and the terminal device needs to send a second reporting information to the network device, the second reporting information may not include information about the third candidate cell, that is, the terminal device can autonomously perform at least one of early synchronization, beam activation, and cell switching with the third candidate cell without reporting to the network device for decision-making.

[0136] Figure 8 is a schematic diagram of an application example of a configuration method according to an embodiment of the present application. As shown in Figure 8, the terminal device can use AI / ML model processing to replace the beam combining selection, layer 3 filtering, and measurement report evaluation processes in related technologies. The terminal device supports the first function, which can obtain candidate cell information based on the layer 1 beam measurement results. The configuration method of the embodiment of the present application may include:

[0137] Step 1: The terminal device reports a first function that can be performed by the AI / ML model to the network. For example, the capability of the first function is expressed as: using the AI / ML model to make a decision on a candidate cell for handover based at least on the L1 beam measurement result.

[0138] Step 2: The network device configures appropriate parameters for the terminal device via RRC signaling based on the first function of the AI / ML model reported by the terminal device. If the network expects the terminal device to report candidate handover cells, it can configure control parameters for reporting candidate handover cells. These control parameters may include, for example, the number of candidate cells reported. Optionally, the network device can also send relevant RRC parameters, such as those used in event determination, to the terminal device. The terminal device can use these parameters as input parameters for the AI / ML model, thereby improving the generalization of the AI / ML model. This is because during model training, the target cell handovers in the successful examples used for comparison are still based on existing event determination methods. If these parameters are used as input parameters for the AI / ML model during training, the model itself is no longer coupled to these parameters. In this case, when the AI / ML model officially begins operation, it will function properly regardless of changes to the RRC parameters used in these event determinations.

[0139] Step 3: When the AI / ML model is running inside the terminal device and a judgment result is obtained, the terminal device can report the candidate cell for switching based on the network configuration or directly execute the switching to the target cell in the case of conditional switching or LTM switching.

[0140] Because AI / ML algorithms can be trained using contextual data from measurements of reasonable handover decisions in the existing network, they can learn how to properly process, for example, L1 beam measurement results. These learned experiences are recorded through a large set of parameters within the AI / ML algorithm and run on the network or terminal device side as the basis for nonlinear algorithms. In actual operation, as long as the L1 beam measurement results are input, the algorithm can output reasonable measurement events or decision information such as handover candidate cells, thereby improving handover performance. In addition, for the network, it only needs to require the terminal device to report the expected information, such as measurement events or handover candidate cells, based on the algorithm's reporting or algorithm capabilities, thereby avoiding complex parameter configuration, making network maintenance more convenient and saving costs.

[0141] FIG9 is a schematic block diagram of a terminal device 900 according to an embodiment of the present application. The terminal device 900 may include:

[0142] The first communication unit 910 is used to receive first configuration information sent by the network device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

[0143] In some embodiments, the first communication unit 910 is further configured to:

[0144] Sending first reporting information to the network device; wherein the first reporting information is used to indicate that the terminal device supports the first function.

[0145] In some embodiments, the input information of the AI ​​model includes measurement results of the serving cell of the terminal device and / or measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

[0146] In some embodiments, the measurement results include beam measurement results and / or cell-level measurement results.

[0147] In some embodiments, the input information of the AI ​​model includes a first parameter.

[0148] In some embodiments, the first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function.

[0149] In some embodiments, the first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

[0150] In some embodiments, the input information of the AI ​​model includes state parameters of the terminal device.

[0151] In some embodiments, the state parameters include one or more of the terminal device's location, moving speed, moving direction, transmission power, and antenna orientation.

[0152] In some embodiments, the first communication unit 910 is further configured to:

[0153] Sending second reporting information to the network device; wherein the second reporting information includes relevant information of the first candidate cell.

[0154] In some embodiments, the first candidate cell includes a second candidate cell for conditional handover configured by the network device. As shown in FIG10 , the terminal device 900 further includes:

[0155] The first processing unit 920 is configured to autonomously switch to the second candidate cell in priority.

[0156] In some embodiments, the first processing unit 920 is specifically configured to:

[0157] When the conditional handover triggering event associated with the second candidate cell is not triggered, autonomous handover to the second candidate cell is prioritized.

[0158] In some embodiments, the first candidate cell includes a third candidate cell for a layer 1 / layer 2 triggered mobility LTM handover configured by the network device. As shown in FIG11 , the terminal device 900 further includes a second processing unit 930, which is configured to execute at least one of the following terminal device autonomous behaviors:

[0159] Perform early synchronization with the third candidate cell;

[0160] activating a beam for communicating with a third candidate cell;

[0161] Prioritize switching to the third candidate cell.

[0162] In some embodiments, the AI ​​model is trained based on a first dataset, where the first dataset includes at least one of the following:

[0163] Information on candidate cells used in handover decisions contained in handover statistics;

[0164] The measurement results in the first time window before the switching decision;

[0165] measurement results within a second time window after the handover decision;

[0166] Configuration parameters for measurement events related to handover decisions;

[0167] Status parameters of the terminal device that are relevant to the handover decision.

[0168] The terminal device 900 of the embodiment of the present application can implement the corresponding functions of the terminal device in the aforementioned method embodiment. The processes, functions, implementation methods and beneficial effects corresponding to the various modules (sub-modules, units or components, etc.) in the terminal device 900 can be found in the corresponding descriptions in the above-mentioned method embodiments, which will not be repeated here. It should be noted that the functions described by the various modules (sub-modules, units or components, etc.) in the terminal device 900 of the embodiment of the application can be implemented by different modules (sub-modules, units or components, etc.) or by the same module (sub-module, unit or component, etc.).

[0169] FIG12 is a schematic block diagram of a network device 1200 according to an embodiment of the present application. The network device 1200 may include:

[0170] The second communication unit 1210 is used to send first configuration information to the terminal device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

[0171] In some embodiments, the second communication unit 1210 is further configured to:

[0172] Receive first reporting information sent by the terminal device; wherein the first reporting information is used to indicate that the terminal device supports the first function.

[0173] In some embodiments, the input information of the AI ​​model includes measurement results of the serving cell of the terminal device and / or measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

[0174] In some embodiments, the measurement results include beam measurement results and / or cell-level measurement results.

[0175] In some embodiments, the input information of the AI ​​model includes a first parameter.

[0176] In some embodiments, the first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function.

[0177] In some embodiments, the first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

[0178] In some embodiments, the input information of the AI ​​model includes state parameters of the terminal device.

[0179] In some embodiments, the state parameters include one or more of the terminal device's location, moving speed, moving direction, transmission power, and antenna orientation.

[0180] In some embodiments, the second communication unit 1210 is further configured to:

[0181] Receive second reporting information sent by the terminal device; wherein the second reporting information includes relevant information of the first candidate cell.

[0182] In some embodiments, the AI ​​model is trained based on a first dataset, where the first dataset includes at least one of the following:

[0183] Information on candidate cells used in handover decisions contained in handover statistics;

[0184] The measurement results in the first time window before the switching decision;

[0185] measurement results within a second time window after the handover decision;

[0186] Configuration parameters for measurement events related to handover decisions;

[0187] Status parameters of the terminal device that are relevant to the handover decision.

[0188] The network device 1200 of the embodiment of the present application can implement the corresponding functions of the network device in the aforementioned method embodiment. The processes, functions, implementation methods and beneficial effects corresponding to each module (sub-module, unit or component, etc.) in the network device 1200 can be found in the corresponding description in the above method embodiment, and will not be repeated here. It should be noted that the functions described in the various modules (sub-module, unit or component, etc.) in the network device 1200 of the embodiment of the application can be implemented by different modules (sub-module, unit or component, etc.) or by the same module (sub-module, unit or component, etc.).

[0189] Figure 13 is a schematic structural diagram of a communication device 1300 according to an embodiment of the present application. The communication device 1300 includes a processor 1310, which can call and execute a computer program from a memory to enable the communication device 1300 to implement the method in the embodiment of the present application.

[0190] In one embodiment, the communication device 1300 may further include a memory 1320. The processor 1310 may call and execute a computer program from the memory 1320 to enable the communication device 1300 to implement the method in the embodiment of the present application.

[0191] The memory 1320 may be a separate device independent of the processor 1310 , or may be integrated into the processor 1310 .

[0192] In one embodiment, the communication device 1300 may further include a transceiver 1330 , and the processor 1310 may control the transceiver 1330 to communicate with other devices. Specifically, the transceiver 1330 may send information or data to other devices, or receive information or data sent by other devices.

[0193] The transceiver 1330 may include a transmitter and a receiver. The transceiver 1330 may further include an antenna, and the number of antennas may be one or more.

[0194] In one embodiment, the communication device 1300 may be a network device of an embodiment of the present application, and the communication device 1300 may implement the corresponding processes implemented by the network device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0195] In one embodiment, the communication device 1300 may be a terminal device of an embodiment of the present application, and the communication device 1300 may implement the corresponding processes implemented by the terminal device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0196] 14 is a schematic structural diagram of a chip 1400 according to an embodiment of the present application. The chip 1400 includes a processor 1410, which can call and execute a computer program from a memory to implement the method according to the embodiment of the present application.

[0197] In one embodiment, the chip 1400 may further include a memory 1420. The processor 1410 may call and execute a computer program from the memory 1420 to implement the method executed by the terminal device or the network device in the embodiment of the present application.

[0198] The memory 1420 may be a separate device independent of the processor 1410 , or may be integrated into the processor 1410 .

[0199] In one embodiment, the chip 1400 may further include an input interface 1430. The processor 1410 may control the input interface 1430 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips.

[0200] In one embodiment, the chip 1400 may further include an output interface 1440. The processor 1410 may control the output interface 1440 to communicate with other devices or chips, and specifically, may output information or data to other devices or chips.

[0201] In one embodiment, the chip can be applied to the network device in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the network device in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0202] In one embodiment, the chip can be applied to the terminal device in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the terminal device in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0203] The chips used in the network device and the terminal device may be the same chip or different chips.

[0204] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0205] The processor mentioned above may be a general-purpose processor, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or other programmable logic devices, transistor logic devices, discrete hardware components, etc. The general-purpose processor mentioned above may be a microprocessor or any conventional processor, etc.

[0206] The memory mentioned above may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM).

[0207] It should be understood that the above-mentioned memories are exemplary but not restrictive. For example, the memories in the embodiments of the present application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM RAM (DR RAM), etc. In other words, the memories in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0208] FIG15 is a schematic block diagram of a communication system 1500 according to an embodiment of the present application. The communication system 1500 includes a terminal device 900 and a network device 1200 .

[0209] Among them, the network device 1200 is used to send first configuration information to the terminal device; wherein, the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

[0210] The terminal device 900 is used to receive first configuration information sent by the network device.

[0211] In some embodiments, the terminal device 900 is further configured to execute a first function based on the first parameter.

[0212] The terminal device 900 can be used to implement the corresponding functions implemented by the terminal device in the above method, and the network device 1200 can be used to implement the corresponding functions implemented by the network device in the above method. For the sake of brevity, they are not described here in detail.

[0213] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function in accordance with the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode to another website, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0214] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0215] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0216] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A configuration method, comprising: The terminal device receives first configuration information sent by the network device; wherein, the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

2. The method according to claim 1, wherein Before the terminal device receives the first configuration information sent by the network device, the method further includes: The terminal device sends first reporting information to the network device; wherein, the first reporting information is used to indicate that the terminal device supports the first function.

3. The method according to claim 1 or 2, wherein: The input information of the AI ​​model includes the measurement results of the serving cell of the terminal device and / or the measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

4. The method according to claim 3, wherein: The measurement results include beam measurement results and / or cell-level measurement results.

5. The method according to any one of claims 1 to 4, wherein The input information of the AI ​​model includes the first parameter.

6. The method according to any one of claims 1 to 4, wherein The first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function.

7. The method according to any one of claims 1 to 6, wherein The first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

8. The method according to any one of claims 1 to 7, wherein The input information of the AI ​​model includes the status parameters of the terminal device.

9. The method according to claim 8, wherein The state parameters include one or more of the position, moving speed, moving direction, transmission power and antenna orientation of the terminal device.

10. The method according to any one of claims 1 to 9, wherein The method further comprises: The terminal device sends second reporting information to the network device; wherein the second reporting information includes relevant information of the first candidate cell.

11. The method according to any one of claims 1 to 9, wherein The first candidate cell includes a second candidate cell for conditional handover configured by the network device; The method further comprises: The terminal device autonomously switches to the second candidate cell first.

12. The method according to claim 11, wherein The terminal device autonomously switches to the second candidate cell preferentially, including: In the case that the conditional switching trigger event associated with the second candidate cell is not triggered, the terminal device autonomously switches to the second candidate cell first.

13. The method according to any one of claims 1 to 9, wherein The first candidate cell includes a third candidate cell for layer 1 / layer 2 triggered mobility LTM handover configured by the network device; The method further includes at least one of the following terminal device autonomous behaviors: The terminal device performs early synchronization with the third candidate cell; activating, by the terminal device, a beam for communicating with the third candidate cell; The terminal device preferentially switches to the third candidate cell.

14. The method according to any one of claims 1 to 13, wherein: The AI ​​model is trained based on a first data set, where the first data set includes at least one of the following: Information on candidate cells used in handover decisions contained in handover statistics; a measurement result within a first time window before the handover decision; a measurement result within a second time window after the handover decision; Configuration parameters of measurement events related to the handover decision; Status parameters of the terminal device related to the switching decision.

15. A configuration method, comprising: The network device sends first configuration information to the terminal device; wherein, the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

16. The method according to claim 15, wherein Before the network device sends the first configuration information to the terminal device, the method further includes: The network device receives first reporting information sent by the terminal device; wherein the first reporting information is used to indicate that the terminal device supports the first function.

17. The method according to claim 15 or 16, wherein The input information of the AI ​​model includes the measurement results of the serving cell of the terminal device and / or the measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

18. The method according to claim 17, wherein The measurement results include beam measurement results and / or cell-level measurement results.

19. The method according to any one of claims 15 to 18, wherein The input information of the AI ​​model includes the first parameter.

20. The method according to any one of claims 15 to 19, wherein The first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function.

21. The method according to any one of claims 15 to 20, wherein The first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

22. The method according to any one of claims 15 to 21, wherein The input information of the AI ​​model includes the status parameters of the terminal device.

23. The method according to claim 22, wherein The state parameters include one or more of the position, moving speed, moving direction, transmission power and antenna orientation of the terminal device.

24. The method according to any one of claims 15 to 23, wherein: The method further comprises: The network device receives second reporting information sent by the terminal device; wherein the second reporting information includes relevant information of the first candidate cell.

25. The method according to any one of claims 15 to 24, wherein The AI ​​model is trained based on a first data set, where the first data set includes at least one of the following: Information on candidate cells used in handover decisions contained in handover statistics; a measurement result within a first time window before the handover decision; a measurement result within a second time window after the handover decision; Configuration parameters of measurement events related to the handover decision; Status parameters of the terminal device related to the switching decision.

26. A terminal device comprising: A first communication unit is used to receive first configuration information sent by a network device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

27. The terminal device according to claim 26, wherein: The first communication unit is further configured to: Sending first reporting information to the network device; wherein, the first reporting information is used to indicate that the terminal device supports the first function.

28. The terminal device according to claim 26 or 27, wherein: The input information of the AI ​​model includes the measurement results of the serving cell of the terminal device and / or the measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

29. The terminal device according to claim 28, wherein: The measurement results include beam measurement results and / or cell-level measurement results.

30. The terminal device according to any one of claims 26 to 29, wherein: The input information of the AI ​​model includes the first parameter.

31. The terminal device according to any one of claims 26 to 30, wherein: The first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function.

32. The terminal device according to any one of claims 26 to 31, wherein: The first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

33. The terminal device according to any one of claims 26 to 32, wherein: The input information of the AI ​​model includes the status parameters of the terminal device.

34. The terminal device according to claim 33, wherein: The state parameters include one or more of the position, moving speed, moving direction, transmission power and antenna orientation of the terminal device.

35. The terminal device according to any one of claims 26 to 34, wherein: The first communication unit is further configured to: Sending second reporting information to the network device; wherein the second reporting information includes relevant information of the first candidate cell.

36. The terminal device according to any one of claims 26 to 34, wherein: The first candidate cell includes a second candidate cell for conditional handover configured by the network device; The terminal device further includes: The first processing unit is configured to autonomously switch to the second candidate cell in a preferential manner.

37. The terminal device according to claim 36, wherein: The first processing unit is specifically configured to: When the conditional handover triggering event associated with the second candidate cell is not triggered, autonomous handover to the second candidate cell is prioritized.

38. The terminal device according to any one of claims 26 to 34, wherein: The first candidate cell includes a third candidate cell for layer 1 / layer 2 triggered mobility LTM handover configured by the network device; The terminal device further includes a second processing unit, which is configured to execute at least one of the following terminal device autonomous behaviors: Performing early synchronization with the third candidate cell; activating a beam for communicating with the third candidate cell; Prioritize switching to the third candidate cell.

39. The terminal device according to any one of claims 26 to 38, wherein: The AI ​​model is trained based on a first data set, where the first data set includes at least one of the following: Information on candidate cells used in handover decisions contained in handover statistics; a measurement result within a first time window before the handover decision; a measurement result within a second time window after the handover decision; Configuration parameters of measurement events related to the handover decision; Status parameters of the terminal device related to the switching decision.

40. A network device comprising: A second communication unit is used to send first configuration information to the terminal device; wherein the first configuration information is used to indicate a first parameter, and the first parameter is used by the terminal device to perform a first function, and the first function includes determining a first candidate cell for cell switching based on an artificial intelligence AI model.

41. The network device according to claim 40, wherein: The second communication unit is further configured to: Receive first reporting information sent by the terminal device; wherein, the first reporting information is used to indicate that the terminal device supports the first function.

42. The network device according to claim 40 or 41, wherein: The input information of the AI ​​model includes the measurement results of the serving cell of the terminal device and / or the measurement results of the neighboring cells of the terminal device; the output information of the AI ​​model includes the first candidate cell.

43. The network device according to claim 42, wherein: The measurement results include beam measurement results and / or cell-level measurement results.

44. The network device according to any one of claims 40 to 43, wherein: The input information of the AI ​​model includes the first parameter.

45. The network device according to any one of claims 40 to 44, wherein: The first parameter includes first indication information, and the first indication information is used to instruct the terminal device to execute the first function.

46. ​​The network device according to any one of claims 40 to 45, wherein: The first parameter includes a configuration parameter of a measurement event and / or a control parameter for reporting the first candidate cell.

47. The network device according to any one of claims 40 to 46, wherein: The input information of the AI ​​model includes the status parameters of the terminal device.

48. The network device according to claim 47, wherein The state parameters include one or more of the position, moving speed, moving direction, transmission power and antenna orientation of the terminal device.

49. The network device according to any one of claims 40 to 48, wherein: The second communication unit is further configured to: Receive second reporting information sent by the terminal device; wherein the second reporting information includes relevant information of the first candidate cell.

50. The network device according to any one of claims 40 to 49, wherein: The AI ​​model is trained based on a first data set, where the first data set includes at least one of the following: Information on candidate cells used in handover decisions contained in handover statistics; a measurement result within a first time window before the handover decision; a measurement result within a second time window after the handover decision; Configuration parameters of measurement events related to the handover decision; Status parameters of the terminal device related to the switching decision.

51. A terminal device comprising: A transceiver, a processor and a memory, wherein the memory is used to store a computer program, the transceiver is used to communicate with other devices, and the processor is used to call and run the computer program stored in the memory so that the terminal device executes the method according to any one of claims 1 to 14.

52. A network device comprising: A transceiver, a processor and a memory, wherein the memory is used to store a computer program, the transceiver is used to communicate with other devices, and the processor is used to call and run the computer program stored in the memory to enable the network device to perform the method according to any one of claims 15 to 25.

53. A chip comprising: A processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method according to any one of claims 1 to 14.

54. A chip comprising: A processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method according to any one of claims 15 to 25.

55. A computer-readable storage medium for storing a computer program, which, when executed by a device, causes the device to perform the method according to any one of claims 1 to 14.

56. A computer-readable storage medium for storing a computer program, which, when executed by a device, causes the device to perform the method according to any one of claims 15 to 25.

57. A computer program product comprising computer program instructions for causing a computer to perform the method of any one of claims 1 to 14.

58. A computer program product comprising computer program instructions for causing a computer to perform the method of any one of claims 15 to 25.

59. A computer program causing a computer to perform the method of any one of claims 1 to 14.

60. A computer program causing a computer to perform the method of any one of claims 15 to 25.

61. A communication system comprising: A terminal device, configured to execute the method according to any one of claims 1 to 14; A network device, configured to execute the method according to any one of claims 15 to 25.

Images