Communication method, terminal, communication device, and storage medium

Abstract

The present disclosure relates to a communication method, a terminal, a communication device and a storage medium, belonging to the technical field of communications. The method comprises: under the condition that a cell handover process of a terminal meets a first condition and a second condition, storing first information, wherein the first information is used for training a first model, and the first model is used for handover prediction. According to the communication method of the present disclosure, the terminal can collect handover data when the AI performance is poor, such that an AI model can be trained and updated on the basis of the data, thereby improving the system performance.

Description

A communication method, terminal, communication device, and storage medium Technical Field

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

[0002] Wireless communication networks can use AI models for prediction and inference to improve system performance. Training AI models requires collecting a large amount of data, and different application scenarios require different amounts of data. Application scenarios can include mobile communication system processes such as 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 presents a communication method, device, and storage medium that can be used in the field of communication technology to solve the problem of handover failure or ping-pong handover caused by poor AI performance when using AI-assisted handover schemes.

[0005] According to a first aspect of the present disclosure, a communication method is proposed, executed by a terminal, comprising: storing first information when the cell handover process of the terminal satisfies a first condition and a second condition, wherein the first information is used to train a first model, and the first model is used for handover prediction.

[0006] According to a second aspect of the present disclosure, a terminal is provided, including a processing module for storing first information when the cell handover process of the terminal satisfies a first condition and a second condition, wherein the first information is used to train a first model and the first model is used for handover prediction.

[0007] According to a third aspect of the present disclosure, a communication device is provided, including a transceiver; a memory; and a processor, which are respectively connected to the transceiver and the memory, and configured to control the transmission and reception of wireless signals of the transceiver by executing computer-executable instructions on the memory, and to implement the method described in any one of the first aspects of the present disclosure.

[0008] According to a fourth aspect of the present disclosure, a computer storage medium is provided that stores computer-executable instructions, which, when executed by a processor, can implement the method described in any one of the first aspects of the present disclosure.

[0009] According to the communication method proposed in this disclosure, when the cell handover process of the terminal meets a first condition and a second condition, first information is stored, wherein the first information is used to train a first model, and the first model is used for handover prediction. The communication method of this disclosure enables the terminal to collect handover data when AI performance is poor, thereby training and updating the AI ​​model based on this data and improving system performance. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0011] Figure 1 is a schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0012] Figure 2 is an interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure;

[0013] Figure 3A is a schematic flowchart of a communication method for a terminal according to an embodiment of the present disclosure;

[0014] Figure 3B is a schematic flowchart of a communication method for a terminal according to an embodiment of the present disclosure;

[0015] Figure 4 is a schematic diagram of the structure of a terminal provided according to an embodiment of the present disclosure;

[0016] Figure 5A is a schematic diagram of the structure of a communication device provided according to an embodiment of the present disclosure;

[0017] Figure 5B is a schematic diagram of the structure of a chip according to an embodiment of the present disclosure. Detailed Implementation

[0018] This disclosure provides a communication method and device, a communication device, and a storage medium.

[0019] In a first aspect, embodiments of this disclosure provide a communication method executed by a terminal, comprising: storing first information when the cell handover process of the terminal satisfies a first condition and a second condition, wherein the first information is used to train a first model and the first model is used for handover prediction.

[0020] In the above embodiments, the terminal collects first information under specific conditions, trains a first model based on the first information, thereby improving the performance of the first model, and applies it to the terminal's handover prediction.

[0021] In conjunction with some embodiments of the first aspect, in some embodiments, the first condition is: the first model was used during the cell handover process; the second condition includes at least one of the following: the cell handover process failed; the cell handover process involved ping-pong handover.

[0022] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: determining whether the cell handover process satisfies a first condition in the event of a first event.

[0023] In conjunction with some embodiments of the first aspect, in some embodiments, the first event includes at least one of the following: the terminal switches according to the inference result of the first model; the terminal sends the inference result of the first model to the network device; the terminal adjusts the switching parameters according to the inference result of the first model.

[0024] In conjunction with some embodiments of the first aspect, in some embodiments, the inference result includes at least one of the following: the measurement result of the predicted cell; the identifier of the target cell; the handover success information of the target cell; the handover failure information of the target cell; the dwell time of the terminal in the target cell; the time period during which a successful handover to the target cell can be achieved; the time period during which a handover to the target cell fails; and mobility events.

[0025] In conjunction with some embodiments of the first aspect, in some embodiments, the predicted cell measurement result includes at least one of the following: the measurement value of the serving cell of the terminal in a future time period; the measurement value of the neighboring cells of the serving cell in a future time period; and the measurement value of the second cell predicted from the measurement value of the first cell.

[0026] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following: the inference result of the first model; the index corresponding to the inference result of the first model; the identifier of the first model; the function corresponding to the first model; the application conditions when the inference result of the first model is obtained; the handover failure indication; the ping-pong handover indication; the identifier of the source cell; and the identifier of the target cell.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, the metrics include at least one of the following: confidence level; accuracy; probability.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the application conditions include at least one of the following: the terminal's moving speed; the terminal's battery level; the terminal's power; the terminal's computing power; the terminal's location; the terminal's service type; the terminal's antenna configuration; the terminal's rotation speed; the terminal's storage space; the network device's cell type; the network device's network deployment scenario; the network device's wireless channel quality; the frequency of the network device's cell; the location of the network device's cell; the distance between network devices; the network device's antenna configuration; the network device's transmission power; and the network device's digital parameter representation (Numerology).

[0029] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending first information to a network device, wherein the first model is deployed on the network device.

[0030] In the above embodiments, if the terminal meets the first condition and the second condition, it indicates that the current AI performance is poor. Then, the terminal collects the first information and uses the first information to update the AI ​​model to improve system performance.

[0031] Secondly, embodiments of this disclosure provide a terminal, including a processing module, for storing first information when the cell handover process of the terminal meets a first condition and a second condition, wherein the first information is used to train a first model, and the first model is used for handover prediction.

[0032] Thirdly, embodiments of this disclosure provide a communication device, including: a transceiver; a memory; and a processor, respectively connected to the transceiver and the memory, configured to control the transmission and reception of wireless signals of the transceiver by executing computer-executable instructions on the memory, and capable of implementing the method described in any embodiment of the first aspect of this disclosure.

[0033] Fourthly, embodiments of this disclosure provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, can implement the method described in any of the embodiments of the first aspect of this disclosure.

[0034] Fifthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method described in the optional implementation of the first aspect.

[0035] In a sixth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the method as described in an alternative implementation of the first aspect.

[0036] In a seventh aspect, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described in the optional implementation of the first aspect above.

[0037] It is understood that the aforementioned terminals, communication devices, storage media, program products, computer programs, chips, or chip systems 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.

[0038] This disclosure provides a communication method and apparatus, a communication device, and a storage medium. In some embodiments, the terms "communication method" and "information processing method" can be used interchangeably, and the terms "terminal" and "information processing device" and "communication device" can be used interchangeably.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the aforementioned," "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 or a plural expression.

[0043] In the embodiments disclosed herein, "multiple" refers to two or more.

[0044] In some embodiments, the terms “at least one of”, “at least one of”, “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0045] The descriptions in this disclosure, such as "at least one of A, B, C..." or "A and / or B and / or C...", include the case where any one of A, B, C... exists alone, as well as the case where any combination of any of A, B, C... exists alone. Each case can exist alone. For example, "at least one of A, B, C" includes the cases of A alone, B alone, C alone, A and B combination, A and C combination, B and C combination, and A and B and C combination. For example, A and / or B includes the cases of A alone, B alone, and A and B combination.

[0046] In some embodiments, the notation "in one case A, in another case B" or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: A is executed regardless of B, i.e., A is executed in some embodiments; B is executed regardless of A, i.e., B is executed in some embodiments; A and B are selectively executed, i.e., A and B are selected for execution in some embodiments; A and B are both executed, i.e., A and B are executed in some embodiments. The same applies when there are more branches such as A, B, and C.

[0047] 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.

[0048] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0049] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0050] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0051] 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”.

[0052] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0053] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0054] In some embodiments, the terms "access network device (AN device)," "radio access network device (RAN device)," "base station (BS)," "radio base station," "fixed station," "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," "carrier," "component carrier," and "bandwidth part (BWP)" can be used interchangeably.

[0055] In some embodiments, the terms "terminal", "terminal device", "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", and "client" can be used interchangeably.

[0056] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0057] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0058] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0059] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0060] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0061] Machine learning algorithms are one of the most important methods for implementing artificial intelligence technology. Machine learning can obtain models through massive amounts of training data, and these models can predict time. In many fields, models trained by machine learning can achieve highly accurate prediction results. An AI function is a collection of AI models that can be used for specific functions. Inference from an AI model or function can be executed on the terminal side or run on the network side. Management of AI models or functions includes activation, deactivation, and switching. Each AI model or function corresponds to a unique ID. The inference result output by the AI ​​can correspond to an indicator, which indicates the degree of certainty that the AI's inference result matches the true value. This indicator can be confidence, accuracy, or probability. When AI inference runs on the terminal side, the terminal can use the inference results locally to assist in handover, such as selecting a cell to handover based on the AI ​​inference results or scaling handover parameters. The terminal can also send the inference results to the network, which selects a target cell to handover or configures handover parameters based on the inference results reported by the terminal. However, during the handover process, the AI ​​inference results may result in handover failure or ping-pong handover, meaning the AI ​​inference results are inaccurate or the AI ​​performance is poor.

[0062] To address the aforementioned issues, this disclosure proposes an AI-assisted switching scheme that collects relevant data when switching fails or occurs during ping-pong switching. This collected data is then used to train the AI ​​model or function, thereby updating the AI ​​model based on this data and improving system performance.

[0063] Therefore, this disclosure proposes a communication method, device, and storage medium. By storing first information when a first condition and a second condition are met during the cell handover process of a terminal, the first information is used to train a first model, and the first model is used for handover prediction. The communication method of this disclosure enables the terminal to collect handover data when AI performance is poor, thereby training and updating the AI ​​model based on this data and improving system performance.

[0064] The method proposed in this disclosure is applicable to various communication systems, including but not limited to 4G, 5G, 5G-advance and subsequent communication technologies (such as 6G).

[0065] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 may include a terminal 101 and a network device 102.

[0066] In some embodiments, terminal 101 may be a device that performs a cell handover process.

[0067] In some embodiments, terminal 101 may be a device that stores first information.

[0068] In some embodiments, terminal 101 may be a device that sends first information to a network device.

[0069] In some embodiments, terminal 101 may be a device that performs a cell handover process using a first model.

[0070] In some embodiments, terminal 101 may be a device that has failed in the cell handover process.

[0071] In some embodiments, terminal 101 may be a device that performs a ping-pong handover during the cell handover process.

[0072] In some embodiments, terminal 101 may be a device that has sent the inference results of the first model to a network device.

[0073] In some embodiments, terminal 101 may be a device that adjusts switching parameters based on the inference results of the first model.

[0074] In some embodiments, terminal 101 may be a device that selects a target cell to switch to based on the inference result of the first model.

[0075] In some embodiments, the name of the terminal 101 is not limited, and may be, for example, "storage device for first information", "transmission device for first information", or "device for cell handover".

[0076] In some embodiments, network device 102 may be a device that receives first information.

[0077] In some embodiments, network device 102 may be a device that deploys the first model.

[0078] In some embodiments, the name of the network device 102 is not limited, and may be, for example, "a device for receiving first information", "a device for deploying the first model", etc.

[0079] In some embodiments, the terminal may include at least one of, but is not limited to, a mobile phone, a wearable device, an Internet of Things device, a car with communication capabilities, a smart car, a tablet computer, a computer with wireless transceiver capabilities, 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 surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and a wireless terminal device in a smart home.

[0080] In some embodiments, the access network equipment may include at least one of the following in a 5G communication system: an evolved NodeB (eNB), a next-generation eNB (ng-eNB), a next-generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a radio backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open RAN, a cloud RAN, a base station in other communication systems, and an access node in a Wi-Fi system, but is not limited thereto.

[0081] 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.

[0082] 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.

[0083] 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 the aforementioned 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).

[0084] 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.

[0085] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​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.

[0086] 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), 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 user plane path establishment methods, and next-generation systems extended from them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0087] Figure 2 is an interactive schematic diagram of a communication method provided in an embodiment of this disclosure. As shown in Figure 2, this embodiment of the disclosure relates to a communication method that can be executed by a communication system, such as the communication system 100 shown in Figure 1. The communication system includes a terminal and a network device. The interactive method may include the following steps:

[0088] Step 2101: The terminal determines whether the first condition and the second condition are met.

[0089] In some embodiments, the first condition is that a first model was used during cell handover.

[0090] In this embodiment of the disclosure, the cell before the terminal performs cell handover can be referred to as the serving cell, the stationary cell, the source cell, etc., and the cell after the terminal performs cell handover can be referred to as the target cell, the cell to be handed over, etc.

[0091] In some embodiments, the first model may be an AI model or an AI function, and the AI ​​function may be a collection of multiple AI models used to implement an AI function, such as predictive beam.

[0092] In some embodiments, the second condition includes at least one of the following: cell handover process failure; ping-pong handover occurring during cell handover. Ping-pong handover indicates that the terminal experiences frequent handovers during the cell handover process.

[0093] In some embodiments, upon the occurrence of a first event, the terminal determines whether the cell handover process meets a first condition. In other words, the terminal determines whether the first condition is met only after the occurrence of the first event.

[0094] In some embodiments, the first event includes at least one of the following: the terminal switches according to the inference result of the first model; the terminal sends the inference result of the first model to the network device; the terminal adjusts the switching parameters according to the inference result of the first model.

[0095] In the above embodiments, the first event is used to determine that the terminal used AI functions during the handover process.

[0096] In some embodiments, the inference result includes at least one of the following: the measurement result of the predicted cell; the identifier of the target cell; the handover success information of the target cell; the handover failure information of the target cell; the dwell time of the terminal in the target cell; the time period during which a successful handover to the target cell can be achieved; the time period during which a handover to the target cell fails; and mobility events.

[0097] In some embodiments, the terminal may switch based on the inference result of the first model by including the identifier of the target cell or the handover success information of the target cell in the inference result of the first model. The target cell is the serving cell after the terminal performs the handover, and the terminal switches the serving cell to the target cell based on the inference result.

[0098] In some embodiments, the terminal may switch based on the inference result of the first model if the inference result of the first model includes handover failure information of the target cell, and the terminal may not switch the serving cell to the target cell based on the inference result.

[0099] In some embodiments, the terminal may switch based on the inference result of the first model, where the inference result of the first model includes the measurement results of the predicted cells, and the terminal selects a cell from the predicted cells for switching based on the measurement results.

[0100] In some embodiments, the terminal may switch based on the inference result of the first model, where the inference result of the first model includes a time period during which the terminal can successfully switch to the target cell, and the terminal switches the serving cell from the original cell to the target cell within the time period based on the inference result.

[0101] For example, the inference result of the first model includes a time period of 1 second during which the terminal successfully switches to the target cell. Based on this inference result, if the terminal can switch within 1 second within the service range of the target cell, it will switch; if it cannot switch within 1 second outside the service range of the target cell, it will not switch.

[0102] In some embodiments, the terminal may switch based on the inference result of the first model if the inference result of the first model includes the time period during which the terminal failed to switch to the target cell. Based on the inference result, the terminal will not switch the serving cell to the target cell.

[0103] For example, the inference result of the first model includes that the terminal will fail to switch to the target cell if it takes more than 2 seconds. Based on this inference result, if the terminal is not within the service range of the target cell and it would take more than 2 seconds to switch to the target cell, then the terminal will not switch the serving cell to the target cell.

[0104] In some embodiments, the terminal sending the inference result of the first model to the network device may be that the terminal sent the inference result of the first model to the network device before the handover.

[0105] In some embodiments, the terminal adjusts the handover parameters according to the inference result of the first model. For example, if the inference result of the first model is a successful handover message for the target cell, the terminal adjusts the handover parameters to the parameters for handing over the serving cell to the target cell based on the successful handover message.

[0106] In some embodiments, the terminal adjusts the handover parameters according to the inference result of the first model. For example, if the inference result of the first model is a handover failure message for the target cell, the terminal adjusts the handover parameters to parameters that enable the serving cell to be switched to a cell that can be successfully switched.

[0107] In some embodiments, the terminal adjusts the handover parameters according to the inference result of the first model. For example, if the inference result of the first model is the dwell time of the terminal in the target cell, then the terminal adjusts the handover parameters to parameters that enable handover to the target cell within the dwell time.

[0108] For example, the terminal selects a target cell for handover based on the reasoning results of an AI model or AI function. The reasoning results include information on handover failure or success, and the predicted cell dwell time, where the predicted cell dwell time can be the time the terminal can stay in the candidate cell.

[0109] For example, the terminal reports the inference results of the AI ​​model or AI function to the network. The inference results include: the measurement results of the predicted cell, which may be the measurement values ​​of the reference signal or beam of the candidate cell; the identifier of the target cell, which may be a recommended target cell for the terminal to perform cell handover, i.e., the terminal directly hands over to the target cell; and mobility events, including measurement reporting conditions being met, handover failure cells, cell dwell time, radio link failure, etc., where the measurement reporting conditions being met include neighboring cells having higher signal quality than the current cell.

[0110] For example, the terminal adjusts the handover parameters based on the inference results of the AI ​​model or AI function. The inference results include information on whether the handover was successful or failed, and the cell dwell time. The handover parameters can be parameters for conditional handover, where the duration of conditional handover is as close as possible to the predicted time. The handover parameters can also be parameters reported by measurement, where those that are easier to handover successfully are more likely to be reported.

[0111] In some embodiments, the predicted cell measurement results include at least one of the following: the measurement value of the terminal's serving cell in a future time period; the measurement value of the serving cell's neighboring cells in a future time period; and the measurement value of the second cell predicted from the measurement value of the first cell.

[0112] For example, a terminal can predict future cell measurement results, which is called temporal prediction, i.e., predicting the future measurement results of the current or neighboring cells, or predicting the measurement results of unmeasured cells, i.e., using the measurement results of two already measured neighboring cells to predict the measurement results of a third neighboring cell, which is called spatial prediction.

[0113] For example, the terminal performs cell handover based on the inference results of the AI ​​model or AI function. The inference results of the AI ​​model or AI function assist the terminal in performing cell handover. When the terminal selects the target cell for handover based on the inference results of the AI ​​model or AI function, or reports the inference results of the AI ​​model or AI function to the network before handover, or adjusts the handover parameters based on the inference results of the AI ​​model or AI function, it means that the terminal used the AI ​​model or AI function during the cell handover process. In other words, the terminal activated the AI ​​model or AI function during the cell handover process.

[0114] In the above embodiments, if the terminal activates an AI model or AI function during the switching process and a switching failure or ping-pong switching occurs, it indicates that the performance of the AI ​​model or AI function used by the terminal is poor.

[0115] Step 2102: The terminal stores the first information.

[0116] In some embodiments, when the terminal meets a first condition and a second condition during the cell handover process, it stores first information, wherein the first information is used to train a first model, and the first model is used for handover prediction.

[0117] For example, when the AI ​​model or AI function being used by the terminal has poor performance, data is collected. The collected data is used to train and update the AI ​​model or AI function to improve system performance.

[0118] In some embodiments, the first information includes at least one of the following: the inference result of the first model; the index corresponding to the inference result of the first model; the identifier of the first model; the function corresponding to the first model; the application conditions when the inference result of the first model is obtained; the handover failure indication; the ping-pong handover indication; the identifier of the source cell; and the identifier of the target cell.

[0119] In some embodiments, the metrics include at least one of the following: confidence level; accuracy; probability.

[0120] In some embodiments, the application conditions include at least one of the following: the terminal's moving speed; the terminal's battery level; the terminal's power; the terminal's computing power; the terminal's location; the terminal's service type; the terminal's antenna configuration; the terminal's rotation speed; the terminal's storage space; the network device's cell type; the network device's network deployment scenario; the network device's wireless channel quality; the network device's cell frequency; the network device's cell location; the distance between network devices; the network device's antenna configuration; the network device's transmit power; and the network device's digital parameter representation (Numerology).

[0121] In some embodiments, the application conditions can be terminal-side conditions, wherein the terminal's computing power can be measured by the number of floating-point operations per second (FLOPs), the terminal's location can be a geographical location or a location within a cell, the terminal's service type can be audio, video, multimedia, voice, etc., the terminal's antenna configuration can be the number of ports, and the terminal's storage space can be measured in bits.

[0122] In some embodiments, the application conditions can be network-side conditions, wherein the cell type can be macro cell, micro cell, urban dense cell, etc., the network deployment scenario can be indoor or outdoor, the wireless channel quality can be determined by the reference signal receiving power (RSRP), reference signal received quality (RSRQ), signal-to-noise ratio (SINR), etc., and the antenna configuration can be the number of ports, the number of MIMO layers, etc.

[0123] In some embodiments, the index corresponding to the inference result of the first model indicates the degree of certainty that the inference result of the first model matches the true value.

[0124] In some embodiments, the application conditions for obtaining the inference result of the first model represent the conditions for applying the first model to perform inference. The application conditions include terminal-side conditions or network-side conditions. The AI ​​model or AI function can achieve better performance under the application conditions.

[0125] Step 2103: The terminal sends the first information to the network device.

[0126] In some embodiments, if the cell handover process of the terminal meets the first condition and the second condition, the terminal sends the first information to the network device.

[0127] In some embodiments, the first model is deployed on a network device. When the terminal meets the first condition and the second condition during the cell handover process, it collects the first information and sends the first information to the network device, which is used by the network device to update the first model during the training of the first model and improve system performance.

[0128] For example, if an AI model or AI function is trained on the network side, and the terminal fails to hand over to a cell or a ping-pong handover occurs while using the AI ​​model or AI function, the terminal collects relevant data and sends the data to the network side for training and updating the AI ​​model or AI function.

[0129] In the above embodiments, the terminal collects first information because the handover fails or a ping-pong handover occurs during the cell handover process due to the use of the first model. The first information is then sent to the network device for the network device to train and update the first model, and the updated first model is used for handover prediction.

[0130] The communication method involved in the embodiments of this disclosure may include at least one of steps 2101 to 2103. For example, step 2101 may be implemented as a standalone embodiment, step 2102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps 2101+2102 and 2101+2102+2103 may be implemented as standalone embodiments, but are not limited thereto.

[0131] 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.

[0132] Figure 3A is a flowchart illustrating a communication method for a terminal according to an embodiment of the present disclosure. This disclosure relates to a communication method, which includes:

[0133] Step 3101: Determine whether the first and second conditions are met.

[0134] The optional implementation of step 3101 can be found in the optional implementation of step 2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0135] Step 3102: Store the first information.

[0136] The optional implementation of step 3102 can be found in the optional implementation of step 2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0137] Step 3103: Send the first message to the network device.

[0138] The optional implementation of step 3103 can be found in the optional implementation of step 2103 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0139] The communication method involved in the embodiments of this disclosure may include at least one of steps 3101 to 3103. For example, step 3101 may be implemented as a standalone embodiment, and step 3102 may be implemented as a standalone embodiment. And so on, but not limited thereto. Steps 3101+3102 and steps 3101+3102+3103 may be implemented as standalone embodiments, but are not limited thereto.

[0140] Figure 3B is a schematic flowchart of a communication method for a terminal provided according to an embodiment of the present disclosure. This disclosure relates to a communication method, which includes:

[0141] Step 3201: Store the first information.

[0142] In some embodiments, the terminal stores first information when the first condition and the second condition are met during the cell handover process, wherein the first information is used to train a first model and the first model is used for handover prediction.

[0143] The optional implementations of step 3201 can be found in steps 2101 and 2102 in Figure 2, the optional implementations of steps 3101 and 3102 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0144] In embodiments of this disclosure, step 3201 may be combined with step 3103 in FIG3A.

[0145] In summary, the communication method proposed in this disclosure stores first information when the terminal's cell handover process meets a first condition and a second condition. This first information is used to train a first model, and the first model is used for handover prediction. This communication method enables the terminal to collect handover data when AI performance is poor, thereby training and updating the AI ​​model based on this data and improving system performance.

[0146] The following describes a communication method provided by an embodiment of this disclosure.

[0147] AI models or AI functions can achieve good performance under specific application conditions, which can be divided into network-side conditions and UE-side conditions.

[0148] Conditions on the UE side can include: UE speed; battery level; power; computing power, which can be measured by FLOPs; location, which can be a geographic location or a location within the cell; service type, such as audio, video, multimedia, voice, etc.; antenna configuration, including the number of ports; rotation speed; and storage space, which can be measured by bits.

[0149] Network-side conditions may include: cell type, such as macro cell, micro cell, dense urban cell; network deployment scenario, such as indoor or outdoor; wireless channel quality, which can be determined by RSRP, RSRQ or SINR; cell frequency; cell location; distance between base stations; antenna configuration, including number of ports and number of MIMO layers; transmit power; and numberology.

[0150] The method includes the following steps:

[0151] Step 1: When the handover process meets specific conditions, the UE stores the first information.

[0152] 1.1 Specific conditions include the AI ​​function being activated during the handover process and meeting at least one of the following: handover failure; ping-pong handover.

[0153] Optionally, the specific condition can be a first condition and a second condition.

[0154] 1.2 AI functionality includes AI models or a collection of multiple AI functions.

[0155] Optionally, the AI ​​model or AI function can be the first model.

[0156] 1.3 The criteria for determining whether AI functionality was activated during the switching process include at least one of the following:

[0157] The UE selects the target cell for handover based on the AI ​​inference results. The UE can select the target cell based on the inferred handover failure or success information, or it can select the target cell based on the predicted cell dwell time. The UE can also select the target cell based on the time period of handover success or failure.

[0158] Before handover, the UE reports the results of AI inference to the network. The results of AI inference can be predicted cell measurement results, handover target cell, or mobility events. Among them, the UE can predict future cell measurement results, i.e., spatial prediction, or predict the measurement results of unmeasured cells, i.e., spatial prediction. Mobility events include measurement reporting conditions being met, handover failure cells, cell dwell time, radio link failure, etc.

[0159] The UE adjusts the handover parameters based on the AI ​​inference results. The UE can adjust the handover condition parameters or the measurement and reporting parameters based on the handover failure or success and the cell dwell time.

[0160] 1.4 The first information includes at least one of the following:

[0161] The inference results obtained by AI;

[0162] The metrics corresponding to the AI ​​reasoning results indicate the degree of certainty that the AI's reasoning results match the true values. These metrics can be confidence, accuracy, or probability.

[0163] The AI ​​function used can be indicated by the AI ​​model ID or AI function ID;

[0164] The application conditions for AI to obtain inference results can be either UE-side conditions or network-side conditions.

[0165] Switching failure indication;

[0166] Ping-Pong Switching Instruction;

[0167] Switch the source cell and target cell IDs.

[0168] Optionally, if the cell handover process of the terminal meets the first condition and the second condition, the first information is stored. The first information is used to train the first model, and the first model is used for handover prediction.

[0169] Step 2: The UE reports the first information to the network.

[0170] Optionally, the terminal sends first information to the network device, wherein the first model is deployed on the network device.

[0171] In the embodiments disclosed herein, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations in other embodiments.

[0172] This disclosure also provides 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., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0173] 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.

[0174] 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).

[0175] [Corrected according to Details 91 09.05.2024] Figure 4 is a schematic diagram of the structure of a terminal provided according to an embodiment of the present disclosure. As shown in Figure 4, the terminal 4100 includes a processing module 4101. In some embodiments, the processing module 4101 is used to store first information when the cell handover process of the terminal meets a first condition and a second condition, wherein the first information is used to train a first model, and the first model is used for handover prediction. Optionally, the processing module is used to execute at least one of the communication steps (e.g., steps 2101, 2102, 3101, 3102, 3201, but not limited thereto) performed by the terminal 4100 in any of the above methods, which will not be described in detail here.

[0176] In some embodiments, the terminal further includes a transceiver module for performing at least one of the steps of receiving and / or sending (e.g., step 2103, step 3103, but not limited thereto), which will not be described in detail here.

[0177] 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.

[0178] Figure 5A is a schematic diagram of the structure of a communication device 5100 provided according to an embodiment of this disclosure. The communication device 5100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 5100 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.

[0179] As shown in Figure 5A, the communication device 5100 includes one or more processors 5101. The processor 5101 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. Optionally, the communication device 5100 can be used to execute any of the above methods. Optionally, one or more processors 5101 can be used to invoke instructions to cause the communication device 5100 to execute any of the above methods.

[0180] In some embodiments, the communication device 5100 further includes one or more transceivers 5102. When the communication device 5100 includes one or more transceivers 5102, the transceiver 5102 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps 2103, 3103, 4101, but not limited thereto), and the processor 5101 performs at least one of other steps (e.g., steps 2101, 2102, 3101, 3102, 3201, but not limited thereto). In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., can be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0181] In some embodiments, the communication device 5100 further includes one or more memories 5103 for storing data. Optionally, all or part of the memories 5103 may be located outside the communication device 5100. In optional embodiments, the communication device 5100 may include one or more interface circuits 5104. Optionally, the interface circuits 5104 are connected to the memories 5102, and the interface circuits 5104 can be used to receive data from the memories 5102 or other devices, and can be used to send data to the memories 5102 or other devices. For example, the interface circuits 5104 can read data stored in the memories 5102 and send the data to the processor 5101.

[0182] In some embodiments, the processor 5101 may store a computer program 5105, which runs on the processor 5101 and enables the communication device 5000 to perform the methods described in the above method embodiments. The computer program 5105 may be embedded in the processor 5101, in which case the processor 5101 may be implemented in hardware.

[0183] The communication device 5100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 5100 described in this disclosure is not limited thereto, and the structure of the communication device 5100 may not be limited by FIG. 5A. 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.

[0184] Figure 5B is a schematic diagram of the structure of chip 5200 according to an embodiment of this disclosure. For cases where the communication device 5100 can be a chip or a chip system, please refer to the schematic diagram of chip 5200 shown in Figure 5B, but it is not limited thereto.

[0185] Chip 5200 includes one or more processors 5201. Chip 5200 is used to perform any of the methods described above.

[0186] In some embodiments, chip 5200 further includes one or more interface circuits 5202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 5200 further includes one or more memories 5203 for storing data. Optionally, all or part of the memories 5203 may be located outside of chip 5200. Optionally, interface circuit 5202 is connected to memory 5203, and interface circuit 5202 can be used to receive data from memory 5203 or other devices, and interface circuit 5202 can be used to send data to memory 5203 or other devices. For example, interface circuit 5202 can read data stored in memory 5203 and send the data to processor 5201.

[0187] In some embodiments, the interface circuit 5202 performs at least one of the communication steps such as sending and / or receiving in the above-described method (e.g., steps 2103, 3103, and 4101, but not limited thereto). The interface circuit 5202 performing the communication steps such as sending and / or receiving in the above-described method refers, for example, to the interface circuit 5202 performing data interaction between the processor 5201, the chip 5200, the memory 5203, or the transceiver device. In some embodiments, the processor 5201 performs at least one of other steps (e.g., steps 2101, 2102, 3101, 3102, and 3201, but not limited thereto).

[0188] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0189] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 5100, cause the communication device 5100 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.

[0190] This disclosure also provides a program product that, when executed by the communication device 5100, causes the communication device 5100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0191] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method, characterized in that, The method is executed by a terminal, and the method includes: If the cell handover process of the terminal meets the first condition and the second condition, the first information is stored, wherein the first information is used to train the first model, and the first model is used for handover prediction.

2. The method according to claim 1, characterized in that, The first condition is: the first model is used during the cell handover process; the second condition includes at least one of the following: the cell handover process fails; the cell handover process involves ping-pong handover.

3. The method according to claim 1 or 2, characterized in that, The method further includes: In the event of the first event, determine whether the cell handover process satisfies the first condition.

4. The method according to claim 3, characterized in that, The first event includes at least one of the following: The terminal switches according to the reasoning result of the first model; The terminal sends the inference results of the first model to the network device; The terminal adjusts the switching parameters based on the inference results of the first model.

5. The method according to claim 4, characterized in that, The reasoning result includes at least one of the following: Measurement results of the predicted community; The target community's signage; Successful handover information for the target cell; Handover failure message for the target cell; The terminal's dwell time in the target cell; The time period during which a successful switch to the target cell was possible; The time period during which switching to the target cell failed; Mobility events.

6. The method according to claim 5, characterized in that, The predicted cell measurement results include at least one of the following: The measurement value of the serving cell of the terminal in a future time period; Measurements of neighboring cells of the serving cell in future time periods; The measurement values ​​of the second cell are predicted based on the measurement values ​​of the first cell.

7. The method according to any one of claims 1 to 6, characterized in that, The first information includes at least one of the following: The reasoning results of the first model; The metrics corresponding to the inference results of the first model; The identifier of the first model; The functions corresponding to the first model; Application conditions for obtaining the reasoning results of the first model; Switching failure indication; Ping-Pong Switching Instructions; The signage of the source community; The target community's signage.

8. The method according to claim 7, characterized in that, The indicator includes at least one of the following: Confidence level; Accuracy; possibility.

9. The method according to claim 7 or 8, characterized in that, The application conditions include at least one of the following: The terminal's moving speed; The terminal's battery level; The power of the terminal; The terminal's computing power; The location of the terminal; The service type of the terminal; The antenna configuration of the terminal; The rotational speed of the terminal; The terminal's storage space; Community type of network equipment; The network deployment scenarios of the network devices; The wireless channel quality of the network device; The frequency of the cell where the network device is located; The location of the cell of the network device; The distance between network devices; Antenna configuration of the network device; The transmission power of the network device; The digital parameters of the network device are represented by Numerology.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: The first information is sent to a network device, wherein the first model is deployed on the network device.

11. A terminal, characterized in that, Includes a processing module for: If the cell handover process of the terminal meets the first condition and the second condition, the first information is stored, wherein the first information is used to train the first model, and the first model is used for handover prediction.

12. A communication device, wherein, include: transceiver; Memory; The processor is connected to the transceiver and the memory respectively, and is configured to control the wireless signal transmission and reception of the transceiver by executing computer-executable instructions on the memory, and is capable of implementing the method of any one of claims 1-10.

13. A computer storage medium, wherein, The computer storage medium stores computer-executable instructions; when the computer-executable instructions are executed by a processor, they can implement the method of any one of claims 1-10.

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