Mobility operation execution method and apparatus, mobility operation triggering method and apparatus, and terminal and network device

Abstract

The present disclosure relates to the technical field of communications, and specifically relates to a mobility operation execution method and apparatus, a mobility operation triggering method and apparatus, and a terminal and a network device. The mobility operation execution method comprises: when a trigger condition is met, executing an artificial intelligence (AI)-assisted mobility-related operation for a first object. In the present disclosure, a terminal executes an AI-assisted mobility-related operation for a first object only when a trigger condition can be met. Thus, a trigger condition for an AI-assisted mobility-related operation can be specified, and an object of the AI-assisted mobility-related operation can also be specified, thereby helping ensure that the terminal can smoothly execute the AI-assisted mobility-related operation.

Description

Mobility operation execution, triggering methods and apparatus, terminals and network devices Technical Field

[0001] This disclosure relates to the field of communication technology, and more specifically, to mobility operation execution methods, mobility operation triggering methods, mobility operation execution devices, mobility operation triggering devices, terminals, network devices, communication devices, and storage media. Background Technology

[0002] In current handover mechanisms, handover can be triggered and executed based on reported historical measurement results and / or measurement events, essentially a reactive approach. However, reactive approaches may have issues in some scenarios, such as being more prone to handover failures, radio link failures, ping-pong handovers, throughput loss, or premature / late handovers. Summary of the Invention

[0003] The embodiments of this disclosure provide methods and apparatus for performing and triggering mobility operations, as well as terminals and network devices, to address technical problems in the related art.

[0004] According to a first aspect of the present disclosure, a mobility operation execution method is proposed, which is executed by a terminal, the method comprising: performing an artificial intelligence (AI) assisted mobility-related operation on a first object when a triggering condition is met.

[0005] According to a second aspect of the present disclosure, a mobility operation triggering method is provided, executed by a network device, the method comprising: indicating a triggering condition to a terminal, wherein, when the triggering condition is met, the terminal is triggered to perform an AI-assisted mobility-related operation on a first object.

[0006] According to a third aspect of the present disclosure, a mobility operation execution apparatus is provided, the apparatus comprising: a processing module configured to perform an artificial intelligence (AI)-assisted mobility-related operation on a first object when a triggering condition is met.

[0007] According to a fourth aspect of the present disclosure, a mobility operation triggering device is provided, the device comprising: a sending module configured to indicate a triggering condition to a terminal, wherein, when the triggering condition is met, the terminal is triggered to perform an AI-assisted mobility-related operation on a first object.

[0008] According to a fifth aspect of the present disclosure, a terminal is provided, comprising: one or more processors; wherein the terminal is configured to perform the mobility operation execution method described in the first aspect.

[0009] According to a sixth aspect of the present disclosure, a network device is provided, comprising: one or more processors; wherein the network device is configured to perform the mobility operation triggering method described in the second aspect.

[0010] According to a seventh aspect of the present disclosure, a communication system is provided, including a terminal and a network device, wherein the terminal is configured to implement the mobility operation execution method of the first aspect, and the network device is configured to implement the mobility operation triggering method of the second aspect.

[0011] According to an eighth aspect of the present disclosure, a storage medium is provided that stores instructions, which, when executed on a communication device, cause the communication device to perform the mobility operation execution method of the first aspect and / or the mobility operation triggering method of the second aspect.

[0012] According to a ninth aspect of the present disclosure, a program product is provided that, when executed by a communication device, causes the communication device to perform the mobility operation execution method described in the first aspect and / or the mobility operation triggering method described in the second aspect.

[0013] According to embodiments of this disclosure, the terminal performs AI-assisted mobility-related operations on the first object only when the triggering conditions are met. Accordingly, on the one hand, the triggering conditions for AI-assisted mobility-related operations can be clearly defined, and on the other hand, the object of AI-assisted mobility-related operations can be clearly defined, which helps to ensure that the terminal can smoothly perform AI-assisted mobility-related operations. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0016] Figure 1B is a schematic diagram illustrating an LTM process according to an embodiment of the present disclosure.

[0017] Figure 1C is a schematic diagram illustrating a network device obtaining TA according to an embodiment of the present disclosure.

[0018] Figure 1D is a schematic diagram of a MAC CE according to an embodiment of the present disclosure.

[0019] Figure 2 is an interactive schematic diagram illustrating a mobility operation execution method according to an embodiment of the present disclosure.

[0020] Figure 3 is a schematic block diagram illustrating a mobile operation execution device according to an embodiment of the present disclosure.

[0021] Figure 4 is a schematic block diagram of a mobility operation triggering device according to an embodiment of the present disclosure.

[0022] Figure 5A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure.

[0023] Figure 5B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0024] Embodiments of this disclosure provide methods and apparatus for performing and triggering mobility operations, as well as terminals and network devices.

[0025] In a first aspect, embodiments of this disclosure propose a mobility operation execution method, executed by a terminal, the method comprising: performing an AI-assisted mobility-related operation on a first object when a triggering condition is met.

[0026] In the above embodiments, the terminal performs AI-assisted mobility-related operations on the first object only when the triggering conditions are met. Accordingly, on the one hand, the triggering conditions for AI-assisted mobility-related operations can be clearly defined, and on the other hand, the object of AI-assisted mobility-related operations can be clearly defined, which helps to ensure that the terminal can successfully perform AI-assisted mobility-related operations.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, the satisfaction of the triggering condition includes at least one of the following: receiving a trigger command to perform a synchronization-related operation on the first object; the mobility configuration of the first object has been decoded and / or integrity checked; the measurement result of the first object satisfies the measurement condition.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the triggering command includes at least one of the following: a triggering command for early uplink synchronization of the first object; a triggering command for early downlink synchronization of the first object.

[0029] In conjunction with some embodiments of the first aspect, in some embodiments, the advance synchronization related operations on the first object include at least one of the following: advance synchronization of the first object; advance acquisition of the timing advance TA value of the first object; activation of the transmission configuration indication state (TCI state) of the first object; and fine tracking of the first object.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement result satisfies measurement conditions, including at least one of the following: the measurement result satisfies a Layer 1 / Layer 2 triggered Mobility LTM event; the measurement result satisfies a Layer 3 Radio Resource Management (RRM) event; and the relationship between the measurement result and a threshold value satisfies a first relationship.

[0031] In conjunction with some embodiments of the first aspect, in some embodiments, the AI-assisted mobility-related operations include at least one of the following: collecting first data for training the AI ​​model; collecting second data for prediction by the AI ​​model; performing a prediction operation based on the AI ​​model; and sending the prediction result obtained based on the AI ​​model prediction to a network device.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the first data includes at least one of the following: historical measurement results of the first object; layer 1 measurement results of the first object; layer 3 measurement results of the first object; whether the measurement results meet the measurement conditions; information on whether the measurement results meet the measurement conditions; historical TA values; and current TA values.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, the prediction operation includes at least one of the following: predicting a radio link failure (RLF) for the first object; predicting a beam failure for the first object; predicting a handover failure (HOF) for the first object; predicting a measurement result for the first object; predicting whether the measurement result for the first object satisfies a measurement event; and predicting a TA value for the first object.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, the first object includes at least one of the following: a cell; a beam.

[0035] Secondly, embodiments of this disclosure propose a mobility operation triggering method executed by a network device, the method comprising: indicating a triggering condition to a terminal, wherein, when the triggering condition is met, the terminal is triggered to perform an AI-assisted mobility-related operation on a first object.

[0036] In conjunction with some embodiments of the second aspect, in some embodiments, the satisfaction of the triggering condition includes at least one of the following: receiving a trigger command to perform a synchronization-related operation on the first object; the mobility configuration of the first object has been decoded and / or integrity checked; the measurement result of the first object satisfies the measurement condition.

[0037] In conjunction with some embodiments of the second aspect, in some embodiments, the triggering command includes at least one of the following: a triggering command for early uplink synchronization of the first object; a triggering command for early downlink synchronization of the first object.

[0038] In conjunction with some embodiments of the second aspect, in some embodiments, the advance synchronization related operations on the first object include at least one of the following: advance synchronization of the first object; advance acquisition of the timing advance TA value of the first object; activation of the transmission configuration indication state (TCI state) of the first object; and fine tracking of the first object.

[0039] In conjunction with some embodiments of the second aspect, in some embodiments, the measurement result satisfies measurement conditions, including at least one of the following: the measurement result satisfies a Layer 1 / Layer 2 triggered Mobility LTM event; the measurement result satisfies a Layer 3 Radio Resource Management (RRM) event; the relationship between the measurement result and a threshold value satisfies a first relationship.

[0040] In conjunction with some embodiments of the second aspect, in some embodiments, the AI-assisted mobility-related operations include at least one of the following: collecting first data for training the AI ​​model; collecting second data for prediction by the AI ​​model; performing a prediction operation based on the AI ​​model; and sending the prediction result obtained based on the AI ​​model prediction to a network device.

[0041] In conjunction with some embodiments of the second aspect, in some embodiments, the first data includes at least one of the following: historical measurement results of the first object; layer 1 measurement results of the first object; layer 3 measurement results of the first object; whether the measurement results meet the measurement conditions; information on whether the measurement results meet the measurement conditions; historical TA values; and current TA values.

[0042] In conjunction with some embodiments of the second aspect, in some embodiments, the prediction operation includes at least one of the following: predicting a radio link failure (RLF) for the first object; predicting a beam failure for the first object; predicting a handover failure (HOF) for the first object; predicting a measurement result for the first object; predicting whether the measurement result for the first object satisfies a measurement event; and predicting a TA value for the first object.

[0043] In conjunction with some embodiments of the second aspect, in some embodiments, the first object includes at least one of the following: a cell; a beam.

[0044] Thirdly, embodiments of this disclosure provide a mobility operation execution device, the device comprising: a processing module configured to perform AI-assisted mobility-related operations on a first object when triggering conditions are met.

[0045] Fourthly, embodiments of this disclosure provide a mobility operation triggering device, the device comprising: a sending module configured to indicate triggering conditions to a terminal, wherein, when the triggering conditions are met, the terminal is triggered to perform an AI-assisted mobility-related operation on a first object.

[0046] Fifthly, embodiments of this disclosure provide a terminal comprising: one or more processors; wherein the terminal is configured to execute the mobility operation execution method described in any one of the optional embodiments of the first aspect.

[0047] In a sixth aspect, embodiments of this disclosure provide a network device comprising: one or more processors; wherein the network device is configured to perform the mobility operation triggering method described in any one of the alternative embodiments of the second aspect.

[0048] In a seventh aspect, embodiments of this disclosure provide a communication system including a terminal and a network device, wherein the terminal is configured to implement the mobility operation execution method according to any one of the first aspect and optional embodiments of the first aspect, and the network device is configured to implement the mobility operation triggering method according to any one of the second aspect and optional embodiments of the second aspect.

[0049] Eighthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform a mobility operation execution method according to any one of the first aspect and optional embodiments of the first aspect, and / or a mobility operation triggering method according to any one of the second aspect and optional embodiments of the second aspect.

[0050] Ninthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the mobility operation execution method according to any one of the first aspect and optional embodiments of the first aspect, and / or the mobility operation triggering method according to any one of the second aspect and optional embodiments of the second aspect.

[0051] In a tenth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the mobility operation execution method according to any one of the first aspect and any one of the optional embodiments of the first aspect, and / or the mobility operation triggering method according to any one of the second aspect and any one of the optional embodiments of the second aspect.

[0052] It is understood that the aforementioned mobility operation execution, triggering device, communication equipment, communication system, storage medium, program product, and computer program are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0053] This disclosure provides embodiments of mobility operation execution and triggering methods and apparatus, terminals, and network devices. In some embodiments, the terms mobility operation execution and triggering methods can be substituted for information processing methods, communication methods, etc.; the terms mobility operation execution and triggering apparatus can be substituted for information processing apparatus, communication apparatus, etc.; and the terms information processing system, communication system, etc., can be substituted for each other.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] In the embodiments of this disclosure, unless otherwise stated, elements expressed in the singular, such as “a,” “an,” “the,” “the,” “the,” “the,” “the,” “the,” “this,” etc., may mean “one and only one,” or “one or more,” “at least one,” etc.

[0058] For example, when using articles such as "a", "an", and "the" in translation, the noun following the article can be understood as either a singular or a plural form.

[0059] In the embodiments disclosed herein, "multiple" refers to two or more.

[0060] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0061] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0062] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0063] The prefixes such as "first" and "second" in the embodiments of this disclosure are only for distinguishing different descriptive objects and do not constitute restrictions on the position, order, priority, number or content of the descriptive objects. For the description of the descriptive objects, please refer to the description in the claims or the context of the embodiments. The use of prefixes should not constitute unnecessary restrictions.

[0064] For example, if the descriptive object is "field," then 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 "level," then 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; there can be one or more. For example, in "first device," the number of "devices" can be one or more. In addition, objects modified by different prefixes can be the same or different. For example, if the descriptive object 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 descriptive object 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.

[0065] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0066] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0067] 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”.

[0068] 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.

[0069] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0070] 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," "serving cell," "carrier," "component carrier," and "bandwidth part (BWP)" can be used interchangeably.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0075] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0076] 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.

[0077] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0078] As shown in Figure 1A, the communication system 100 includes a terminal 101 and a network device 102, wherein the network device includes at least one of the following: an access network device and a core network device.

[0079] In some embodiments, terminal 101 includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

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

[0081] 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).

[0082] 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.

[0083] 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.

[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 FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. Each main body may be physical or virtual. 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 communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0087] In some embodiments, machine learning (ML) algorithms are one of the most important methods for implementing artificial intelligence (AI) technology. Machine learning can obtain models from large amounts of training data, and these models can then be used to predict events. In many fields, models trained using machine learning can achieve very accurate prediction results.

[0088] In some embodiments, in order to support Layer 3 (L3) mobility, the network device can configure RRM (Radio Resource Management) measurements for the terminal, and the network device can trigger a handover based on the measurement results reported by the terminal.

[0089] For example, L3 measurement reporting can include both cell-level and beam-level measurement results. Based on the measurement reports reported by the terminal, the network device can determine the target cell for handover and the optimal beam for the terminal to access. After the target cell and / or beam are confirmed, the network device can send a handover command to the terminal (e.g., "Reconfiguration with sync," which could serve as synchronization information for the target cell), carrying configuration information for the target cell. This configuration information may include bearer configuration, MAC (Media Access Control) configuration, random access configuration, etc. Upon receiving the handover command, the terminal synchronizes with the target cell, then initiates a random access procedure to access the target cell and begins using the target cell's configuration.

[0090] In some embodiments, in the L3 handover mechanism, handover can be triggered and executed based on reported historical measurement results and / or measurement events, which is essentially a responsive approach.

[0091] In macrocell scenarios with low mobility, this responsive solution performs relatively well. However, when the terminal has high mobility, or in high-density deployment scenarios, or when there is mobility for both traditional and future services (such as XR (Extended Reality) services), this responsive solution may have problems, such as being more prone to handover failures, radio link failures, ping-pong handovers, throughput loss, or premature / late handovers.

[0092] For example, conditional handover can be introduced to improve handover robustness. To reduce downtime caused by frequent inter-cell handovers, Layer 1 / Layer 2 Triggered Mobility (LTM) HO has been further introduced. However, these two mechanisms are still insufficient because they are still reactive schemes by design. On the other hand, mechanisms based on AI and / or ML algorithms have the potential to achieve proactive schemes. Therefore, AI-based mobility optimization schemes can be considered, which include prediction of measurement results, prediction of cell-level measurement results, and prediction of beam-level measurement results.

[0093] In some embodiments, the network device may provide one or more candidate configurations for the terminal, wherein a candidate configuration may include the configuration of one or more cells (or cell groups).

[0094] Network devices can subsequently control the terminal to change multiple candidate configurations via L1 (e.g., DCI (Downlink Control Information)) and / or L2 (MAC CE (Control Element)) signaling and / or RRC signaling, for example, changing the working cell (or cell group) from cell (or cell group)-1 to cell (or cell group)-2.

[0095] For example, control signaling can be called cell change control signaling. For example, this process can be called Layer 1 / Layer 2 triggered mobility (L1 / L2-triggered Mobility, LTM) process.

[0096] For example, during LTM, network devices can receive L1 measurement reports sent by terminals. Based on the received L1 measurement results, network devices can send cell switch command signaling to terminals via MAC CE to change the serving cell of terminals.

[0097] Figure 1B is a schematic diagram illustrating an LTM process according to an embodiment of the present disclosure.

[0098] As shown in Figure 1B, the network device can send multiple LTM candidate configurations to the terminal in advance via Radio Resource Control (RRC) signaling. When LTM is triggered, the network device sends a Cell Switch Command (e.g., carried in the MAC CE) to indicate the LTM candidate configuration corresponding to the cell the terminal needs to access. The terminal can then apply the corresponding LTM candidate configuration to access the cell indicated by the network device, thereby completing the serving cell change.

[0099] During this process, network devices send MAC CE based on L1 measurement results to trigger cell switching, which is beneficial for responding to rapid channel changes and triggering switching in a timely manner.

[0100] As shown in Figure 1B, the LTM process supports early uplink synchronization (Early UL synchronization) and early downlink synchronization (Early DL synchronization) for candidate cells. "Early" means that it is performed before the handover process.

[0101] Based on advance synchronization, RACH-less (random access-free) LTM cell switching can be supported during LTM. For example, the TA values ​​of LTM candidate cells can be obtained in advance through two methods: timing advance (TA) acquisition and terminal-based TA measurement. These methods are used to support RACH-less LTM cell switching, such as for advance synchronization. Specifically, performing advance uplink synchronization and advance downlink synchronization on candidate cells to achieve RACH-less LTM cell switching can effectively reduce data interruptions during handover.

[0102] For example, LTM can support Subsequent LTM. When the terminal does not release the LTM candidate configuration after each LTM Cell Switch, it can continue to perform subsequent Cell Switches after mobility execution without RRC reconfiguration or reset. Supporting Subsequent LTM can effectively reduce signaling overhead.

[0103] In some embodiments, network devices and terminals can communicate based on a separate architecture. In the separate architecture, network devices (e.g., access network devices) may include CUs (Centralized Units) and DUs (Distributed Units). In LTM scenarios, intra-CU inter-DU LTM and intra-DU LTM can be implemented. Furthermore, inter-CU LTM, condition-triggered LTM, and event-triggered L1 measurement reporting can also be implemented.

[0104] In some embodiments, L1 measurements of LTM may include network-triggered L1 measurement reporting and event-based L1 measurement reporting.

[0105] Regarding network-triggered L1 measurement reporting, L1 measurements can be enhanced to support both intra-frequency and inter-frequency L1 measurements. For example, L1-RSRP (Reference Signal Receiving Power) measurements based on SSB (Synchronization Signal Block) can be supported. Furthermore, L1 measurements can support semi-persistent and aperiodic reporting on PUSCH (Physical Uplink Shared Channel) and semi-persistent and periodic reporting on PUCCH (Physical Uplink Control Channel).

[0106] For example, the enhancement of L1 measurement includes the following aspects:

[0107] CSI-RS (Channel State Information Reference Signal) measurement;

[0108] L1-SINR (Signal to Interference plus Noise Ratio) measurement;

[0109] Event-triggered L1 measurement reporting;

[0110] Current L1 measurement is enhanced.

[0111] In the LTM process, the LTM Cell Switch Command can be generated by the S-DU (Source Distribute Unit). In the LTM of the Intra-CU (Centralized Unit), the S-DU makes the Cell switch decision. The S-DU determines the candidate target cell to trigger LTM access based on the L1 measurement results reported by the terminal. Then the S-DU sends the Cell switch command to the terminal.

[0112] Regarding event-based L1 measurement reporting, to reduce measurement reports and improve mobility robustness, event-triggered measurement reporting can be supported. Event-triggered measurement reports can assist network devices in selecting the target beam and / or cell for pre-synchronization, or assist network devices in selecting the target cell and / or corresponding beam when triggering LTM Cell Switching.

[0113] For example, a measurement report can be triggered when the measurement result of L1 meets the following event:

[0114] Event 1 (e.g., denoted as Event LTM2): The beam of the serving cell becomes worse than the absolute threshold.

[0115] Event 2 (e.g., denoted as Event LTM3): The beam of candidate cell becomes the amount of offset better than the beam of serving cell.

[0116] Event 3 (e.g., denoted as Event LTM4): The beam of candidate cell becomes better than the absolute threshold.

[0117] Event 4 (e.g., denoted as Event LTM5): The beam of the serving cell becomes worse than absolute threshold 1, and the beam of the candidate cell becomes better than another absolute threshold 2.

[0118] For example, the serving cell's beam is the current beam, which is the beam indicated by the indicated Transmission Configuration Indication (TCI) state. The candidate cell's beam is any one or more beams configured in the candidate reference signal configuration (or measurement resource configuration).

[0119] For example, event-based L1 measurement reporting can be sent to the network via MAC CE (Control Element).

[0120] For example, measurement events are configured in the serving cell configuration, and the configuration of measurement events is associated with the configuration of measurement resources.

[0121] The following examples illustrate the acquisition of TA (e.g., advance TA acquisition) during mobility.

[0122] In some embodiments, when configured by the network, a UL TA acquisition (e.g., referred to as an early TA) procedure for one or more cells different from the current serving cell can be initiated. If the cell has the same NTA as the current serving cell or NTA=0, an early TA acquisition procedure is not required. The network may request the UE to perform early TA acquisition for candidate cells before cell handover.

[0123] For example, the advance TA acquisition process can be triggered by a PDCCH (Physical Downlink Control Channel) command or implemented through UE-based TA measurement configured by RRC. In the former case, the gNB to which the candidate cell belongs calculates the TA value and sends it to the gNB to which the serving cell belongs. When an LTM cell handover is triggered, the serving cell sends the TA value in the LTM Cell Switching Command MAC CE. In the latter case, the terminal performs TA measurement on the candidate cell after RRC configuration, but the exact timing of the terminal's TA measurement depends on the terminal's implementation. The terminal applies its own measured TA value and performs LTM without random access upon receiving a cell handover command. The network can also send the TA value in the LTM Cell Switching Command MAC CE without prior TA acquisition.

[0124] In some embodiments, the network device can obtain the TA based on a random access procedure. For example, for a random access procedure for an LTM candidate cell used to obtain the UL TA in advance, a CFRA (Contention Free Random Access) triggered by a PDCCH command can be used. The terminal sends MSG1 (a message in the random access procedure) to the cell without monitoring the response to MSG1. In order to support UE power ramp, the terminal can also perform a network-instructed MSG1 retransmission.

[0125] Figure 1C is a schematic diagram illustrating a network device obtaining TA according to an embodiment of the present disclosure.

[0126] As shown in Figure 1C, the network device of the serving cell can assign a preamble to the terminal. When the terminal needs to access a candidate cell, it can send the preamble to the network device of the candidate cell. The candidate cell can determine the TA with the terminal by receiving the preamble.

[0127] The following examples illustrate the activation of the TCI state of candidate cells in LTM.

[0128] In some embodiments, during the LTM process, early downlink synchronization for candidate cells is supported.

[0129] The terminal can activate the TCI state of one or more cells that are different from the current serving cell based on network configuration or other schemes. For example, the TCI state of these cells can be activated in advance before any LTM candidate cell becomes the serving cell. This allows the UE to perform downlink synchronization with these candidate cells in advance, thereby enabling a faster handover to one of the candidate cells when a cell switch is triggered.

[0130] For example, supporting early uplink synchronization can effectively reduce handover interruption time.

[0131] Regarding network-triggered Candidate Cell TCI States Activation / Deactivation, during network-triggered LTM, the network activates / deactivates the TCI states of one or more cells by sending a Candidate Cell TCI States Activation / Deactivation MAC CE. Before receiving a cell handover command, the UE performs DL synchronization with the LTM candidate cells. The UE can activate and deactivate the TCI states of LTM candidate cells, which is triggered by network equipment (e.g., gNB).

[0132] The activation / deactivation of the candidate cell TCI state is illustrated below through several examples.

[0133] The network can activate / deactivate the TCI status of LTM candidate cells configured in candidate TCI status and candidate TCI UL (Uplink) status by sending candidate cell TCI status activation / deactivation MAC CE. The network deactivates the TCI status of an LTM candidate cell by omitting the corresponding TCI status ID (identifier) ​​field in the candidate cell TCI status activation / deactivation MAC CE.

[0134] The MAC entity should satisfy the following:

[0135] If the MAC entity receives a candidate cell TCI state activation / deactivation MAC CE on the serving cell:

[0136] Indicates information to lower layers regarding the activation / deactivation of MAC CE in the candidate cell's TCI state.

[0137] Figure 1D is a schematic diagram of a MAC CE according to an embodiment of the present disclosure.

[0138] For example, the MAC CE used for candidate cell TCI state activation / deactivation can be as shown in Figure 1D, comprising N+2 bytes. It can be defined by a MAC subheader with an extended logical channel ID (eLCID). It has a variable size (i.e., the number of bits it contains) and can include the following fields:

[0139] Candidate Cell ID: This field is used to indicate the identifier of the LTM candidate cell for the MAC CE application, corresponding to the LTM CandidateId specified in TS 38.331[5] minus 1, for example, the field length is 3 bits.

[0140] Pi: This field indicates whether each TCI code point has multiple TCI states or a single TCI state. If Pi is set to 1, the i-th TCI code point includes both DL TCI and UL TCI states. If Pi is set to 0, the i-th TCI code point includes only DL / joint TCI states or UL TCI states. The code point mapped to a TCI state is determined by its sequential position in all TCI state ID fields.

[0141] D / U: This field indicates whether the TCI status ID in the same octet is used for the joint / downlink or uplink TCI status. If this field is set to 1, the TCI status ID in the same octet is used for the joint / downlink TCI status. If this field is set to 0, the TCI status ID in the same octet is used for the uplink TCI status.

[0142] TCI State ID: This field represents the TCI state identified by the ltm DL or the TCI StateId in the JointTCI StateToAddModList or the TC1 UL StateId in the ltm UL TCI StateToAddModList as specified in TS 38.331[5]. Wherein, if D / U is set to 1, a 7-bit TCI state ID is used, that is, the TCI StateId specified in TS 38.331[5]. If D / U is set to 0, the most significant bit of the TCI state ID is treated as a reserved bit, and the remaining 6 bits represent the TCI UL state ID specified in TS 38.331[5]. The maximum number of active TCI states is 16.

[0143] R: Reserved bit, can be set to 0.

[0144] The following examples illustrate AI-based LTM.

[0145] In some embodiments, wireless communication networks can use AI for prediction and inference to improve system performance. Training AI models requires collecting a large amount of data, and the data requirements vary depending on the application scenario. Application scenarios may include mobile communication system processes such as beam management, CSI reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0146] In the use and reasoning of AI, multiple AI models or AI functions may be needed for reasoning and prediction. An AI function implements a specific function and may include one or more AI models.

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

[0148] The conditions on the terminal side may include at least one of the following:

[0149] speed;

[0150] Battery level;

[0151] power;

[0152] Computing power can be measured by FLOPs;

[0153] Location can refer to a geographical location or a location within a residential area;

[0154] The business type can be audio, video, multimedia, voice, etc.

[0155] Antenna configuration, including the number of ports;

[0156] Rotational speed;

[0157] Storage space can be measured in bits.

[0158] The network-side conditions may include at least one of the following:

[0159] Community types, such as macro, micro, and dense urban communities.

[0160] Network deployment scenarios, such as indoors and outdoors;

[0161] Wireless channel quality can be determined by RSRP, RSRQ (Reference Signal Receiving Quality), or SINR.

[0162] The frequency of the cell;

[0163] Location of the residential area;

[0164] Distance between base stations;

[0165] Antenna configuration, including the number of ports and the number of MIMO (multiple input multiple output) layers;

[0166] Transmission power;

[0167] Numerology (which may be called an algorithm).

[0168] For example, network-side conditions can be bound to IDs, with the network indicating these conditions by providing the IDs. The terminal may not know which specific network-side conditions the ID represents. When using AI for inference, the terminal checks if the current network-indicated ID matches the network ID used when collecting AI function / model training data. If they don't match, the terminal determines that the current AI function / model does not meet the network-side conditions. In AI / ML use cases, a Functionality describes a function that the terminal supports; a Functionality can contain one or more AI models.

[0169] Functionality can include the following categories:

[0170] Supported functionalities: refers to functionalities that the UE can indicate by using UE capability information.

[0171] Applicable functionalities: refers to functionalities that the UE is ready to apply for inference.

[0172] Activated functionalities: refers to functionalities already enabled for performing inference.

[0173] Similar to terminal capability reporting, terminals can report the AI ​​functionality they support to the network. For example, AI-based spatial beam prediction is one AI functionality, while AI-based temporal beam prediction is another.

[0174] When an AI function has begun inference and prediction, it is considered an applicable function. Terminals can report applicable functions, and the network selects an AI function from among them for management.

[0175] For example, a terminal determines whether an AI function is available based on the following conditions: an available AI model has been obtained, the network conditions for the AI ​​model are met, and the terminal conditions are met. When all three conditions are met, the corresponding AI function is determined to be available.

[0176] If the availability of AI functionality changes, the terminal can report whether the AI ​​functionality is available or unavailable to the network. The network can also instruct the terminal to report whether the AI ​​functionality is available or unavailable.

[0177] The management of AI models or functions includes activating, deactivating, and switching them. The network can monitor the performance of AI; if performance degrades, it is necessary to replace the AI ​​function or AI model, or deactivate the AI ​​function.

[0178] If an activated AI function becomes unavailable, the terminal needs to revert to a mode where AI is not enabled, and simultaneously send an instruction to the network. The network can then activate other AI functions or switch between them.

[0179] To better achieve the integration of AI and mobility (such as LTM), the scope of application of AI and mobility integration can be expanded, for example, by supporting AI-based LTM enhancements.

[0180] The following examples illustrate AI-based LTM prediction.

[0181] In some embodiments, the following functions may be supported for LTM and L1 measurements:

[0182] AI / ML based L1 measurement for LTM and event prediction;

[0183] L1 measurement prediction, including intra-frequency and inter-frequency, can be applied to terminal-side and network-side models.

[0184] L1 measurement events prediction can be applied to terminal-side models;

[0185] LTM event prediction for L1 measurement report;

[0186] LTM event prediction for Conditional LTM evaluation;

[0187] HO failure (the HO is triggered by LTM, handover failure triggered by LTM) prediction, for example, can be applied to terminal-side models;

[0188] The following examples illustrate the early acquisition of AI-based Timing Advance (TA).

[0189] In some embodiments, non-AI LTM supports obtaining the TA value of LTM candidate cells before performing LTM.

[0190] For example, there are two ways to obtain the TA value:

[0191] One method is to obtain the TA based on Early RACH;

[0192] Another method is to obtain the TA based on UE measurements.

[0193] The TA acquisition method based on Early RACH requires the UE to send a preamble to the candidate target cell and obtain the candidate cell TA value through the network side. The TA acquisition method based on terminal measurement needs to consider whether the serving cell and the cell under test are synchronized. Moreover, the above schemes can only obtain the TA of the candidate cell at the current time of the terminal, and the TA may become invalid when the terminal accesses the candidate cell. Therefore, it is worth considering supporting AI-based candidate cell TA prediction.

[0194] In some embodiments, the potential benefits and advantages of AI / ML-assisted mobility in network-triggered L3 handover are studied and evaluated, considering the following aspects:

[0195] AI / ML based RRM measurement and event prediction;

[0196] Cell-level measurement prediction, including intra-frequency and inter-frequency cell-level measurement prediction, is applicable to terminal-side and network-side models, for example.

[0197] Inter-cell Beam-level measurement prediction for L3 Mobility, for example, applicable to terminal-side and network-side models;

[0198] HO failure / RLF prediction (prediction of handover failure / wireless line failure), for example, applicable to terminal-side models;

[0199] Measurement events prediction, for example, is applicable to terminal-side models.

[0200] In some embodiments, in beam management, methods for triggering AI data collection can be investigated, such as periodic data collection or event-triggered data collection, which includes events based on wireless conditions, such as:

[0201] Focus on the following three types of radio condition event-based logging:

[0202] L3 serving cell measurement based (e.g., X1 / X2 similar to A1 / A2);

[0203] Beam-based events (e.g., the beam becomes the top-1 beam and the number of measurements is less than the configured value);

[0204] L1 beam level measurement.

[0205] As illustrated in the preceding examples, AI-assisted mobility-related operations can be performed, such as predicting travel time (TA) and measurement results based on AI models (which can be referred to as ML models, but will be described as AI models hereinafter). However, since there are many types of AI-assisted mobility-related operations, and the objects targeted by these operations are diverse, not all terminals can perform AI-assisted mobility-related operations on every type of object under the same conditions. Therefore, it is necessary to refine the execution conditions and objects of AI-assisted mobility-related operations.

[0206] Figure 2 is an interactive schematic diagram illustrating a mobility operation execution method according to an embodiment of the present disclosure.

[0207] In some embodiments, mobility operations can be performed by a terminal.

[0208] As shown in Figure 2, the mobility operation method may include the following steps:

[0209] In step S201, the network device may indicate the triggering conditions to the terminal.

[0210] In step S202, if the triggering condition is met, the terminal performs AI-assisted mobility-related operations on the first object.

[0211] It should be noted that the triggering condition can be indicated by the network device as shown in Figure 2, or it can be specified by a predefined rule (e.g., protocol agreement). If the triggering condition is specified by a predefined rule, the above step S201 can be omitted.

[0212] In some embodiments, the first object includes, but is not limited to, at least one of the following: a cell; a beam.

[0213] For example, the object in the embodiments of this disclosure can also be referred to as granularity. AI-assisted mobility-related operations include predicting measurement results based on AI models. When the object is a cell, predictions can be made based on the AI ​​model for the cell (or at the cell level) to obtain prediction results, such as predicting measurement results like the signal quality of the cell. When the object is a beam, predictions can be made based on the AI ​​model for the beam (or at the beam level) to obtain prediction results, such as predicting measurement results like the signal quality of the beam.

[0214] For example, there may be one or more triggering conditions. For different terminals, the triggering conditions may be the same, partially the same, or different.

[0215] For example, there may be one or more first objects. For different terminals, the first objects may be the same, partially the same, or different.

[0216] For example, for the terminal in this embodiment of the disclosure, the triggering condition may be indicated by the network device or determined based on predefined rules (e.g., protocol agreements). For example, the network device includes, but is not limited to, the base station corresponding to the terminal's serving cell, the terminal's serving DU, and the terminal's serving CU.

[0217] For example, for the terminal in this embodiment of the disclosure, the first object may be indicated by the network device or determined based on predefined rules (e.g., protocol agreement).

[0218] According to embodiments of this disclosure, the terminal performs AI-assisted mobility-related operations on the first object only when the triggering conditions are met. Accordingly, on the one hand, the triggering conditions for AI-assisted mobility-related operations can be clearly defined, and on the other hand, the object of AI-assisted mobility-related operations can be clearly defined, which helps to ensure that the terminal can smoothly perform AI-assisted mobility-related operations.

[0219] In some embodiments, when the triggering condition is met, the terminal is triggered to perform an AI-assisted mobility-related operation on the first object. This can be done by triggering the terminal to perform the AI-assisted mobility-related operation on the first object only once, or by triggering the terminal to perform the AI-assisted mobility-related operation on the first object multiple times (e.g., a specific number of times), or by triggering the terminal to perform the AI-assisted mobility-related operation on the first object periodically, or by triggering the terminal to perform the AI-assisted mobility-related operation on the first object periodically until a specific number of times is reached.

[0220] It should be noted that the AI-assisted mobility-related operations performed on the first object as described in the above embodiments are mainly for situations where the triggering conditions are met. However, performing AI-assisted mobility-related operations on the first object is not limited to situations where the triggering conditions are met. For example, when the terminal performs AI-assisted mobility-related operations on the first object, it does not need to consider whether the triggering conditions are met.

[0221] For example, the terminal can periodically perform AI-assisted mobility-related operations on the first object. The specific period can be specified by predefined rules (such as protocol agreements) or indicated by the network device.

[0222] For example, a network device can send execution instruction information to a terminal, which instructs the terminal to perform AI-assisted mobility-related operations on a first object. The terminal can then perform AI-assisted mobility-related operations on the first object based on the instruction information.

[0223] In some embodiments, the triggering conditions met may or may not be related to the AI-assisted mobility-related operations performed by the terminal on the first object, and this disclosure does not limit this.

[0224] Taking the existence of a relationship as an example, for instance, if triggering condition #1 is met, the terminal can perform AI-assisted mobility-related operation #1 on the first object; for instance, if triggering condition #2 is met, the terminal can perform AI-assisted mobility-related operation #2 on the first object.

[0225] Accordingly, when a specific trigger condition is met, the terminal can execute AI-assisted mobility-related operations associated with that trigger condition, so as to ensure that the AI ​​model used for AI-assisted mobility-related operations can be used to make predictions under the specific trigger condition.

[0226] In some embodiments, the triggering condition includes at least one of the following:

[0227] A trigger command to perform synchronization-related operations on the first object is received;

[0228] The mobility configuration of the first object has undergone decoding and / or integrity checks;

[0229] The measurement results of the first object meet the measurement conditions.

[0230] In some embodiments, the network device may send a trigger command for synchronization-related operations to the terminal.

[0231] For example, the trigger command includes at least one of the following:

[0232] The trigger command for early uplink synchronization of the first object can also be called the early RACH trigger command or the early TA acquisition trigger command.

[0233] A command to trigger early downlink synchronization for the first object.

[0234] Mobility trigger command;

[0235] In some embodiments, the triggering command for mobility may include at least one of the following: LTM Cell Switch Command, RRCReconfiguration.

[0236] In some embodiments, the mobility triggering command is used to trigger access to a first object, such as an LTM target cell (e.g., a cell among candidate cells) or a beam of an LTM target cell. In such cases, the mobility triggering command may include an LTM Cell Switch Command. The LTM Cell Switch Command may indicate the first object. For example, if the first object is an LTM target cell, it may indicate the identifier of the LTM target cell, or it may indicate the identifier of a candidate configuration corresponding to the LTM target cell. For example, if the first object is a beam of an LTM target cell, it may indicate the identifier of the beam of the LTM target cell.

[0237] For example, the type of trigger command can include at least one of the following:

[0238] Physical layer (PHY) signaling;

[0239] MAC CE;

[0240] Radio Resource Control (RRC) signaling.

[0241] Taking the PHY command as an example, the PHY command may include DCI (Downlink Control Information). In this case, the network device sends DCI to the terminal, triggering the terminal to perform an early uplink synchronization operation on the first object, which can be called PDCCH ordered RACH.

[0242] For example, DCI can directly or indirectly indicate to the terminal the first object of mobility that needs to be synchronized in advance (also known as advance RACH or advance TA). For example, DCI can indicate the Random Access Occasion (RO) of cell #1 to the terminal. The terminal can determine that DCI indicates RO for cell #1, thereby determining that the first object includes cell #1, and thus perform AI-assisted mobility-related operations on cell #1, such as predicting the measurement results of cell #1 based on an AI model.

[0243] In some embodiments, the advance synchronization operation on the first object includes at least one of the following:

[0244] Perform advance synchronization on the first object (e.g., advance uplink synchronization, advance downlink synchronization);

[0245] Obtain the timing information of the first object in advance, such as the TA value;

[0246] Activate the TCI state of the first object;

[0247] Perform fine tracking on the first object.

[0248] For example, consider the triggering signaling including MAC CE. For example, MAC CE can activate MAC CE (Candidate Cell TCI States Activation MAC CE) for candidate cell TCI states. If the first object is a cell, the first object is the cell indicated by MAC CE. If the first object is a beam, the first object is the beam corresponding to the TCI State activated by MAC CE.

[0249] It should be noted that network devices can activate the TCI state of the first object through Candidate Cell TCI States Activation MAC CE (e.g., denoted as MAC CE#1), and network devices can also activate the TCI state of the first object through candidate cell TCI states MAC CE (Candidate Cell TCI States Deactivation MAC CE (e.g., denoted as MAC CE#2)).

[0250] For example, when the terminal determines the activation of the TCI state of cell #1 based on MAC CE#1, where the cell indicated by MAC CE#1 is cell #1 and the beam corresponding to the TCI state activated by MAC CE#1 is beam #1, the terminal can determine that the first object includes beam #1 of cell #1. Then the terminal can perform AI-assisted mobility-related operations for beam #1, such as predicting the measurement results for beam #1.

[0251] If the terminal subsequently receives MAC CE#2, and the cell indicated by MAC CE#2 is cell#1, and the beam corresponding to the TCI state deactivated by MAC CE#2 is beam#1, the terminal can determine that beam#1 of cell#1 is deactivated, thereby stopping the execution of AI-assisted mobility-related operations for beam#1, such as stopping the prediction of the prediction result for beam#1.

[0252] In some embodiments, the network device may send mobility configuration of a first object to the terminal. For example, the mobility configuration may include the configuration of LTM candidate cells, conditions in conditional LTM, and events that trigger measurement reports (e.g., at least one of events 1 to 4 in the previous embodiments). However, it is not limited to these configurations and may include any mobility-related configuration.

[0253] After receiving the first object mobility configuration, the terminal may perform one of the following operations:

[0254] Decode the mobility configuration, for example, in ways including but not limited to ASN.1 decoding;

[0255] Perform an integrity check on the mobility configuration.

[0256] For example, decoding can be done in advance (decoding before mobility is performed); integrity verification can be done in advance (integrity verification before mobility is performed).

[0257] For example, the terminal can determine whether the mobility configuration of the first object has been decoded. If it has been decoded, it can determine that the triggering conditions are met, and thus perform AI-assisted mobility-related operations on the first object.

[0258] For example, the terminal can determine whether the mobility configuration of the first object has been checked for completeness. If the completeness check has been performed, it can determine that the triggering conditions are met, and thus perform AI-assisted mobility-related operations on the first object.

[0259] For example, the terminal can determine whether the mobility configuration of the first object has been decoded and whether a completion check has been performed. If it has been decoded and a completion check has been performed, it can determine that the triggering conditions are met, and thus perform AI-assisted mobility-related operations on the first object.

[0260] In some embodiments, the terminal can measure the first object to obtain measurement results, and then determine whether the measurement results meet the measurement conditions (e.g., the measurement conditions may be referred to as wireless conditions).

[0261] If the measurement results for the first object meet the measurement conditions, the terminal can perform AI-assisted mobility-related operations on the first object. For example, if the first object is cell #2, which needs to be handed over, the AI-assisted mobility-related operations performed by the terminal on the first object may include predicting handover failure (HOF) for the first object, such as predicting whether handover to cell #2 will result in an HOF.

[0262] In some embodiments, the measurement results satisfy measurement conditions, including at least one of the following:

[0263] The measurement result satisfies the LTM event;

[0264] The measurement result satisfies the Layer 3 (L3) event;

[0265] The measurement results satisfy the mobility execution conditions.

[0266] For example, a measurement result satisfying an LTM event (also known as an L1 event) can include the measurement result satisfying the LTM event once, or the measurement result satisfying the LTM event multiple times within a time window. Here, satisfying the LTM event multiple times within a time window can also be referred to as satisfying the entry condition or the exit condition of the LTM event.

[0267] For example, a measurement result satisfying an LTM event (also known as an L1 event) can include the measurement result satisfying the LTM event once, or the measurement result satisfying the LTM event multiple times within a time window. Here, satisfying the LTM event multiple times within a time window can also be referred to as satisfying the entry condition or the exit condition of the LTM event.

[0268] For example, mobility execution conditions may include at least one of the following:

[0269] Execution conditions for conditional switching CHO

[0270] Execution conditions of Conditional LTM

[0271] For example, an execution condition can be associated with one or more L3 events or one or more LTM events.

[0272] The relationship between the measurement results and the threshold values ​​satisfies the first relationship.

[0273] For example, an LTM event may include at least one of the events 1 to 4 in the preceding embodiments.

[0274] For example, an L3 RRM event can include at least one of the following:

[0275] Event 5 (e.g., denoted as Event A1): Serving becomes better than absolute threshold;

[0276] Event 6 (e.g., denoted as Event A2): Serving becomes worse than absolute threshold;

[0277] Event 7 (e.g., denoted as Event A3): Neighbour becomes a better amount of offset than PCell / PSCell.

[0278] Event 8 (e.g., denoted as Event A4): Neighbour becomes better than absolute threshold;

[0279] Event 9 (e.g., denoted as Event A5): PCell / PSCell becomes worse than absolute threshold1 AND Neighbour / SCell becomes better than another absolute threshold2 (the primary cell and / or the primary and / or secondary cells are worse than absolute threshold1, while the neighboring cell is better / or the secondary cell (SCell) is better than absolute threshold2);

[0280] Event 10 (e.g., denoted as Event A6): Neighbour becomes a better amount of offset than SCell.

[0281] Event 11 (e.g., denoted as Event D1): Distance between UE and a reference location referenceLocation1 becomes larger than the configured threshold distance ThreshFromReference1 and distance between UE and a reference location referenceLocation2 becomes shorter than the configured threshold distance ThreshFromReference2.

[0282] Event 12 (e.g., denoted as Event D2): The distance between the UE and the serving cell moving reference location determined based on movingReferenceLocation and its corresponding satellite ephemeris and epoch time broadcast in SIB19 becomes larger than the configured threshold distance ThreshFromReference1, and the distance between the UE and a moving reference location determined based on referenceLocation and its corresponding satellite ephemeris and epoch time for the neighbor cell provided in the associated MeasObjectNR becomes shorter than the configured threshold distance ThreshFromReference2.

[0283] Event 13 (e.g., denoted as CondEvent A3): The conditional reconfiguration candidate becomes a better amount of offset than the PCell / PSCell.

[0284] Event 14 (e.g., denoted as CondEvent A4): Conditional reconfiguration candidate becomes better than absolute threshold where condEventA4 can also be used for current PSCell (i.e., in case it is configured as candidate PSCell for CondEvent A4 evaluation) for CHO with candidate SCG(s) case.

[0285] Event 15 (e.g., denoted as CondEvent A5): PCell / PSCell becomes worse than absolute threshold1 AND Conditional reconfiguration candidate becomes better than another absolute threshold2.

[0286] Event 16 (e.g., denoted as CondEvent D1): Distance between UE and a reference location referenceLocation1 becomes larger than the configured threshold distance ThreshFromReference1 and distance between UE and a reference location referenceLocation2 of conditional reconfiguration candidate becomes shorter than the configured threshold distance ThreshFromReference2.

[0287] Event 17 (e.g., denoted as CondEvent D2): The distance between the UE and the serving cell moving reference location determined based on movingReferenceLocation and its corresponding satellite ephemeris and epoch time broadcast in SIB19 becomes larger than the configured threshold distance ThreshFromReference1, and the distance between the UE and a moving reference location determined based on referenceLocation and its corresponding satellite ephemeris and epoch time for the conditional reconfiguration candidate provided in the associated MeasObjectNR becomes shorter than the configured threshold distance ThreshFromReference2.

[0288] Event 18 (e.g., denoted as CondEvent T1): Time measured at UE becomes more than the configured threshold t1-Threshold but is less than t1-Threshold+duration.

[0289] Event 19 (e.g., denoted as Event X1): Serving L2 U2N Relay UE becomes worse than absolute threshold 1 AND NR Cell becomes better than another absolute threshold 2.

[0290] Event 20 (e.g., denoted as Event X2): Serving L2 U2N Relay UE becomes worse than absolute threshold;

[0291] Event 21 (e.g., denoted as Event I1): Interference becomes higher than the absolute threshold;

[0292] Event 22 (e.g., denoted as Event H1): Aerial UE altitude becomes higher than a threshold;

[0293] Event 23 (e.g., denoted as Event H2): Aerial UE altitude becomes lower than a threshold;

[0294] Event 24 (e.g., denoted as Event A3H1): Neighbour becomes offset better than SpCell and the Aerial UE altitude becomes higher than a threshold.

[0295] Event 25 (e.g., denoted as Event A3H2): Neighbour becomes offset better than SpCell and the Aerial UE altitude becomes lower than a threshold.

[0296] Event 26 (e.g., denoted as Event A4H1): Neighbour becomes better than threshold1 and the Aerial UE altitude becomes higher than a threshold2.

[0297] Event 27 (e.g., denoted as Event A4H2): Neighbour becomes better than threshold1 and the Aerial UE altitude becomes lower than a threshold2.

[0298] Event 28 (e.g., denoted as Event A5H1): SpCell becomes worse than threshold1 and neighbor becomes better than threshold2 and the Aerial UE altitude becomes higher than a threshold3.

[0299] Event 29 (e.g., denoted as Event A5H2): SpCell becomes worse than threshold1 and neighbor becomes better than threshold2 and the Aerial UE altitude becomes lower than a threshold3.

[0300] For example, the relationship between the measurement result and the threshold value satisfies the first relation:

[0301] The measurement results include at least one of the following: RSRP, RSRQ, SINR;

[0302] The first relationship includes at least one of the following: the measurement result is greater than the threshold, the measurement result is equal to the threshold, or the measurement result is less than the threshold;

[0303] The measurement results include at least one of the following: measurement results for the serving cell, measurement results for the current beam, measurement results for the best beam in the serving cell, measurement results for a candidate cell (e.g., an LTM candidate cell), and measurement results for a candidate beam.

[0304] In some embodiments, the AI-assisted mobility-related operations include at least one of the following:

[0305] Collect the first data for training AI models;

[0306] Collect secondary data for AI model prediction (or inference);

[0307] Perform predictive operations based on AI models;

[0308] The prediction results obtained based on the AI ​​model are sent to the network device.

[0309] In some embodiments, the AI ​​model may be deployed on a terminal, or the AI ​​model may be deployed on a network device.

[0310] For example, when an AI model is deployed on a network device, the terminal performing AI-assisted mobility-related operations may include the terminal collecting first data for training the AI ​​model, and then the terminal sending the collected first data to the network device so that the network device can build a training set, and then the network device can train the initial model based on the training set to obtain the required AI model.

[0311] For example, when an AI model is deployed on a network device, the terminal performing AI-assisted mobility-related operations may include the terminal collecting second data for AI model prediction, and then the terminal can send the collected second data to the network device, the network device can input the second data into the AI ​​model, and the AI ​​model can output prediction results based on the second data.

[0312] For example, when the AI ​​model is deployed on a terminal, the terminal may perform AI-assisted mobility-related operations, which may include the terminal collecting second data for AI model prediction, and / or the terminal performing prediction operations based on the AI ​​model (e.g., inputting the second model into the AI ​​model to obtain the output).

[0313] For example, when an AI model is deployed on a terminal, the terminal performing AI-assisted mobility-related operations may include sending prediction results based on the AI ​​model to network devices. These prediction results may include current predictions based on the AI ​​model, as well as predictions from the past.

[0314] In some embodiments, the first data includes at least one of the following:

[0315] Historical measurement results for the first object;

[0316] Measurement results for layer 1 of the first object;

[0317] Layer 3 measurement results for the first object;

[0318] Information regarding whether the measurement results meet the measurement conditions;

[0319] The time during which the measurement results meet the measurement conditions;

[0320] Historical TA values;

[0321] Current TA value.

[0322] It should be noted that the second data may include at least one of the following: historical measurement results of the first object; layer 1 measurement results of the first object; layer 3 measurement results of the first object; information on whether the measurement results meet the measurement conditions; the time when the measurement results meet the measurement conditions; historical TA value; current TA value.

[0323] In addition, the first data and the second data are not limited to the types of data described in the above embodiments, but may also include conditions such as speed, battery level, power, location, and antenna configuration described in the previous embodiments, which will not be repeated here.

[0324] In some embodiments, the prediction operation includes at least one of the following:

[0325] For the first object, predict radio link failure (RLF), such as whether it is an RLF, the probability of an RLF, and the time of the RLF (moment, duration, etc.).

[0326] Predict beam failure for the first object, such as whether beam failure occurs, the probability of beam failure, and the time of beam failure (moment, duration, etc.).

[0327] For the first object, predict the handover failure HOF, such as whether it is HOF, the probability of HOF, and the time of HOF (moment, duration, etc.);

[0328] Predict the measurement results for the first object, such as L1 measurement results and L3 measurement results;

[0329] Predict the satisfaction of the measurement event by the measurement result of the first object, such as whether the measurement event is satisfied, the probability of satisfying the measurement event, and the time (moment, duration, etc.) when the measurement event is satisfied;

[0330] Predict the TA value of the first object.

[0331] In some embodiments, the AI ​​model described above may be related to mobility. For example, the AI ​​model may be used to predict mobility-related information, and the information predicted by the above prediction operation may be related to mobility.

[0332] For example, mobility may include at least one of the following: L3 handover, L1 handover, CHO, LTM, Conditional LTM, CPA (Conditional PSCell Addition), CPC (Conditional PSCell Change), Subsequent LTM, Subsequent CPAC (Conditional PSCell Addition or Change), and Subsequent Conditional LTM.

[0333] Taking LTM as an example, the AI ​​model may be related to LTM, including information used by the AI ​​model to predict LTM-related information. For example, if the prediction operation includes predicting the measurement result of a first object, the predicted measurement result can be used by the network device to determine whether to trigger the terminal to execute LTM; if the prediction operation includes predicting the TA value of the first object, the predicted TA value can be used by the network device to perform advance synchronization on LTM candidate cells.

[0334] For example, the prediction operation includes predicting the RLF for a first object, and the terminal can send the prediction result of the RLF prediction for the first object to the network device.

[0335] For example, the prediction operation includes predicting beam failure for the first object, and the terminal can send the prediction result of beam failure for the first object to the network device;

[0336] For example, the prediction operation includes predicting the handover failure (HOF) for the first object, and the terminal can send the prediction result of the HOF prediction for the first object to the network device;

[0337] For example, the prediction operation includes predicting the measurement result for the first object, and the terminal can send the predicted measurement result for the first object to the network device;

[0338] For example, the prediction operation includes predicting whether the measurement results for the first object satisfy the measurement event, and the terminal can send the prediction result of the measurement results for the first object satisfying the measurement event to the network device;

[0339] For example, the prediction operation includes predicting the TA value of the first object, and the terminal can send the prediction result of the predicted TA value of the first object to the network device.

[0340] The communication method involved in the embodiments of this disclosure may include at least one of steps S201 to S202. For example, step S201 may be implemented as a standalone embodiment, step S202 may be implemented as a standalone embodiment, and step S201+S202 may be implemented as a standalone embodiment, but is not limited thereto.

[0341] In some embodiments, steps S201 and S202 may be performed in an alternate order or simultaneously.

[0342] In some embodiments, step S201 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0343] In some embodiments, step S202 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0344] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0345] The technical solutions of this disclosure will be illustrated by several further embodiments below.

[0346] Example 1: When a terminal receives an early uplink synchronization trigger command sent by a network device (e.g., serving cell, serving DU), the terminal performs AI-assisted mobility-related operations on the first candidate cell (or the first beam of the candidate cell) indicated in the trigger command.

[0347] Example 1.1: The trigger command for early uplink synchronization can also be called the trigger command for early RACH, or the trigger command for early TA acquisition, etc.

[0348] Example 1.2: The trigger command can be any one or more of PHY, MAC CE, and RRC commands. For example, it can be PHY signaling, such as DCI. This process can also be called PDCCH ordered RACH. The DCI directly or indirectly indicates the first candidate cell (or first beam) for mobility to be acquired through advance synchronization / RACH / TA. For example, it can be indicated by indicating the first candidate cell RACH RO.

[0349] Example 1.3: After the terminal receives this trigger command, the terminal needs to perform early uplink synchronization for the corresponding first candidate cell / beam.

[0350] For example, in Embodiment 1, the terminal performs AI-assisted mobility-related operations on the first candidate cell (or the first beam of the first candidate cell) indicated in the trigger command, that is, the terminal performs corresponding operations on the first candidate cell (or the first beam of the first candidate cell) for performing early uplink synchronization / early uplink TA acquisition.

[0351] Example 2: When a terminal receives a first message sent by a network device (e.g., serving cell, serving DU), wherein the first message can be used to trigger downlink synchronization in advance for one or more first candidate cells and / or first beams, and / or to activate one or more TCI states of one or more first candidate cells and / or to trigger the network side to perform fine tracking on one or more first candidate cells and / or first beams, and / or to obtain timing information, the terminal performs AI-assisted mobility-related operations on one or more first candidate cells (or one or more first beams) indicated in the first message.

[0352] Example 2.1: The trigger command can be any one or more of PHY, MAC CE, and RRC commands. For example, it can be MAC CE, where the first message can be Candidate Cell TCI States Activation MAC CE, the first candidate cell and the first beam refer to the first candidate cell indicated in the Candidate Cell TCI States Activation MAC CE, and the beam corresponding to the TCI state is the first beam.

[0353] Example 2.2: After the terminal receives this first message, the terminal needs to perform early downlink synchronization for the corresponding first candidate cell and / or first beam, and / or activate the corresponding first beam in advance, and / or perform fine tracking for the corresponding first candidate cell / first beam, and / or obtain the timing information of the corresponding first candidate cell and / or first beam.

[0354] In addition, network devices can also send a second message (e.g., Candidate Cell TCI state deactivation MAC CE) to deactivate the TCI state of candidate cells, and stop the relevant operations for these candidate cells and / or their beams if they are performing related AI-assisted mobility-related operations.

[0355] Example 3: When a terminal receives a mobility configuration sent by a network device (e.g., serving cell, serving DU), the terminal performs advance ASN.1 decoding and / or integrity checks on the mobility configuration indicated therein based on the configuration, and performs AI-assisted mobility-related operations on the candidate cells corresponding to these candidate configurations.

[0356] Example 4: The terminal triggers AI-assisted mobility-related operations based on radio conditions (also known as measurement conditions). When the first candidate cell (or the first beam of the candidate cell) and / or the serving cell (serving beam, or the beam of the serving cell) meet the corresponding radio conditions, the terminal performs AI-assisted mobility-related operations on these cells (beams) that meet the conditions. The radio conditions include one or more of the following:

[0357] Example 4.1: The L1 LTM event is satisfied, or the entry condition (or exit condition) of the L1 LTM event is satisfied. You can refer to conditions 1 to 4 above, which will not be repeated here.

[0358] Example 4.2: The L3 RRM event is satisfied, or the entry condition (or exit condition) of the L3 RRM event is satisfied. The L3 RRM event can be referred to as events 5 to 29 above, which will not be repeated here.

[0359] Example 4.3: Conditions based on RSRP / RSRQ / SINR thresholds configured on the network side, including any one or more of the following conditions:

[0360] The RSRP and / or RSRQ and / or SINR measured for the serving cell are less than the threshold;

[0361] The RSRP and / or RSRQ and / or SINR obtained from the current beam measurement are less than the threshold;

[0362] The RSRP and / or RSRQ and / or SINR obtained from the optimal beam measurement of the serving cell are less than the threshold;

[0363] The RSRP and / or RSRQ and / or SINR measured for the candidate cell are less than the threshold;

[0364] The RSRP and / or RSRQ and / or SINR obtained from the candidate beam measurements are less than the threshold.

[0365] Example 5: In addition to triggering the terminal to perform AI-assisted mobility-related operations as described in Examples 1 to 4 above, the terminal can also trigger related operations through any one or more of the following methods:

[0366] Example 5.1: The terminal periodically triggers related operations, and the period can be configured by the network.

[0367] Example 5.2: When the terminal receives the relevant indication information sent by the network side, it performs the corresponding operation on the cell / beam contained in the indication information.

[0368] Example 5.3: The related operations triggered by the methods described in Examples 1 to 4 can be triggered once or multiple times periodically. That is, after the terminal meets the conditions in Examples 1 to 4, it performs a related operation once, and then starts the related operation multiple times according to the configured period until the number of times configured by the terminal is reached.

[0369] Example 6: The terminal performs AI-assisted mobility-related operations including:

[0370] Example 6.1: For the network-side AI model (NW-sided model):

[0371] The terminal initiates training data collection. The terminal collects training data related to the first candidate cell (or the first beam of the first candidate cell) and / or training data related to the serving cell (or serving beam, or other beams of the serving cell). For example, the terminal can send the training data to the network to assist the training of the AI ​​model on the network side.

[0372] The terminal initiates inference data collection. The terminal collects inference data related to the first candidate cell (or the first beam of the candidate cell) and / or inference data related to the serving cell (or serving beam, or other beams of the serving cell). For example, the terminal can send training data to the network to assist the inference of the AI ​​model on the network side.

[0373] Example 6.2: For the AI ​​model on the terminal side (terminal-sided model);

[0374] The terminal initiates AI inference. For example, the terminal may perform corresponding AI inference on the first candidate cell (or the first beam of the first candidate cell) and / or the terminal may perform corresponding AI inference on the serving cell (or the serving beam, or other beams of the serving cell).

[0375] The terminal can report the inference results obtained in Embodiment 2 to the network, or the terminal can also report the inference results of the corresponding AI inference performed on the first candidate cell (or the first beam of the first candidate cell) and / or the corresponding AI inference performed on the serving cell (or serving beam, or other beams of the serving cell). For example, if the terminal starts AI inference based on other methods (e.g., periodically), it can start reporting the AI ​​prediction results based on the method described in Embodiment 2.

[0376] Example 7: The training data to be collected in the data collection described in Example 6 includes any one or more of the following:

[0377] Example 7.1: Serving cell / beam, measurement results of the first candidate cell and / or the first beam, such as L1 measurement results, L3 measurement results.

[0378] Example 7.2: Historical measurement results of serving cell / beam, first candidate cell and / or first beam.

[0379] Example 7.3: For AI-based L1 event prediction, it also includes the serving cell and / or beam, the satisfaction status of the events corresponding to the first candidate cell / first beam, such as whether the event is satisfied, the satisfaction time, etc.

[0380] Example 7.4: For TA prediction, it also includes the corresponding serving cell and / or beam, the current TA value and / or historical TA value of the first candidate cell and / or the first beam.

[0381] Example 8: The terminal initiating AI inference as described in Example 6 includes any one or more of the following situations:

[0382] Example 8.1: Terminal initiates RLF prediction / beam failure prediction for serving cell / beam.

[0383] Example 8.2: The terminal initiates HOF prediction for the first candidate cell and / or the first beam. The terminal predicts whether HOF will occur when accessing the first candidate cell and / or the first beam through LTM, and / or the time and probability of sending HOF.

[0384] Example 8.3: The terminal initiates measurement result prediction for the serving cell / beam and / or the first candidate cell and / or the first beam, such as time-domain measurement result prediction, to predict the future measurement results of the serving cell and / or beam and / or the first candidate cell and / or the first beam. The measurement result prediction includes L1 measurement result prediction and / or L3 measurement result prediction.

[0385] Example 8.4: The terminal initiates measurement event prediction for the serving cell / beam and / or the first candidate cell and / or the first beam, predicting the satisfaction status of the corresponding measurement events, such as whether they are satisfied and the satisfaction time; wherein the measurement event prediction includes L1 measurement event prediction and / or L3 measurement event prediction.

[0386] Example 8.5: The terminal initiates TA prediction for the first candidate cell and / or the first beam, predicting the TA of the first candidate cell / first beam.

[0387] The specific prediction configuration and prediction output parameters mentioned above can depend on the network configuration.

[0388] Example 9: The terminal startup prediction result reporting described in Example 6 includes any one or more of the following situations:

[0389] Example 9.1: The terminal reports the serving cell and / or beam RLF prediction and / or beam failure prediction results.

[0390] Example 9.2: HOF prediction results of the terminal for the first candidate cell and / or the first beam.

[0391] Example 9.3: Measurement results of the terminal's prediction of the serving cell and / or beam and / or first candidate cell and / or first beam.

[0392] Example 9.4: The terminal's prediction of measurement events for the serving cell and / or beam and / or first candidate cell and / or first beam.

[0393] Example 9.5: Terminal prediction results for the TA of the first candidate cell / first beam.

[0394] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0395] In some embodiments, the terms "uplink", "uplink", and "physical uplink" can be used interchangeably, as can the terms "downlink", "downlink", and "physical downlink", as well as the terms "sidelink", "sidelink", "sidelink communication", "sidelink communication", "direct connection", "direct link", "direct communication", and "direct link communication".

[0396] In some embodiments, the terms “downlink control information (DCI),” “downlink (DL) assignment,” “DL DCI,” “uplink (UL) grant,” and “UL DCI” can be used interchangeably.

[0397] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, as can terms such as "physical uplink shared channel (PUSCH)" and "UL data".

[0398] In some embodiments, the terms "synchronization signal (SS)," "synchronization signal block (SSB)," "reference signal (RS)," "pilot," and "pilot signal" can be used interchangeably.

[0399] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0400] In some embodiments, the terms "component carrier (CC)," "cell," "frequency carrier," and "carrier frequency" can be used interchangeably.

[0401] In some embodiments, “get,” “obtain,” “receive,” “transmit,” “bidirectional transmission,” and “send and / or receive” can be used interchangeably and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining through self-processing, or autonomous implementation, among other meanings.

[0402] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0403] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0404] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (boolean), or by a comparison of numerical values ​​(e.g., a comparison with a predetermined value), but is not limited thereto.

[0405] Corresponding to the aforementioned embodiments of the mobility operation execution method and mobility operation triggering method, this disclosure also provides embodiments of the mobility operation execution device and the mobility operation triggering device.

[0406] Figure 3 is a schematic block diagram illustrating a mobility operation execution device according to an embodiment of the present disclosure. For example, the mobility operation execution device may be disposed in, and / or adapted to, a terminal. As shown in Figure 3, the mobility operation execution device includes: a processing module 301.

[0407] In some embodiments, the processing module is configured to perform AI-assisted mobility-related operations on a first object when a triggering condition is met.

[0408] In some embodiments, the fulfillment of the triggering condition includes at least one of the following: receiving a trigger command to perform a synchronization-related operation on the first object; the mobility configuration of the first object has been decoded and / or integrity checked; the measurement result of the first object meets the measurement condition.

[0409] In some embodiments, the triggering command includes at least one of the following: a triggering command for early uplink synchronization of the first object; a triggering command for early downlink synchronization of the first object.

[0410] In some embodiments, the advance synchronization related operation for the first object includes at least one of the following: advance synchronization of the first object; advance acquisition of the timing advance TA value of the first object; activation of the transmission configuration indication state (TCI state) of the first object; and fine tracking of the first object.

[0411] In some embodiments, the measurement result satisfies measurement conditions, including at least one of the following: the measurement result satisfies a Layer 1 / Layer 2 triggered Mobility LTM event; the measurement result satisfies a Layer 3 Radio Resource Management (RRM) event; the relationship between the measurement result and the threshold value satisfies a first relationship.

[0412] In some embodiments, the AI-assisted mobility-related operations include at least one of the following: collecting first data for training the AI ​​model; collecting second data for prediction by the AI ​​model; performing a prediction operation based on the AI ​​model; and sending the prediction results obtained based on the AI ​​model prediction to a network device.

[0413] In some embodiments, the first data includes at least one of the following: historical measurement results of the first object; layer 1 measurement results of the first object; layer 3 measurement results of the first object; whether the measurement results meet the measurement conditions; information on whether the measurement results meet the measurement conditions; historical TA value; current TA value.

[0414] In some embodiments, the prediction operation includes at least one of the following: predicting a radio link failure (RLF) for the first object; predicting a beam failure for the first object; predicting a handover failure (HOF) for the first object; predicting a measurement result for the first object; predicting whether the measurement result for the first object satisfies the measurement event; and predicting the TA value of the first object.

[0415] In some embodiments, the first object includes at least one of the following: a cell; a beam.

[0416] Figure 4 is a schematic block diagram illustrating a mobility operation triggering device according to an embodiment of the present disclosure. For example, the mobility operation triggering device can be applied to and / or installed in a network device. As shown in Figure 4, the mobility operation triggering device includes: a sending module 401.

[0417] In some embodiments, the sending module is configured to indicate a triggering condition to the terminal, wherein, if the triggering condition is met, the terminal is triggered to perform an AI-assisted mobility-related operation on a first object.

[0418] In some embodiments, the fulfillment of the triggering condition includes at least one of the following: receiving a trigger command to perform a synchronization-related operation on the first object; the mobility configuration of the first object has been decoded and / or integrity checked; the measurement result of the first object meets the measurement condition.

[0419] In some embodiments, the triggering command includes at least one of the following: a triggering command for early uplink synchronization of the first object; a triggering command for early downlink synchronization of the first object.

[0420] In some embodiments, the advance synchronization related operation for the first object includes at least one of the following: advance synchronization of the first object; advance acquisition of the timing advance TA value of the first object; activation of the transmission configuration indication state (TCI state) of the first object; and fine tracking of the first object.

[0421] In some embodiments, the measurement result satisfies measurement conditions, including at least one of the following: the measurement result satisfies a Layer 1 / Layer 2 triggered Mobility LTM event; the measurement result satisfies a Layer 3 Radio Resource Management (RRM) event; the relationship between the measurement result and the threshold value satisfies a first relationship.

[0422] In some embodiments, the AI-assisted mobility-related operations include at least one of the following: collecting first data for training the AI ​​model; collecting second data for prediction by the AI ​​model; performing a prediction operation based on the AI ​​model; and sending the prediction results obtained based on the AI ​​model prediction to a network device.

[0423] In some embodiments, the first data includes at least one of the following: historical measurement results of the first object; layer 1 measurement results of the first object; layer 3 measurement results of the first object; whether the measurement results meet the measurement conditions; information on whether the measurement results meet the measurement conditions; historical TA value; current TA value.

[0424] In some embodiments, the prediction operation includes at least one of the following: predicting a radio link failure (RLF) for the first object; predicting a beam failure for the first object; predicting a handover failure (HOF) for the first object; predicting a measurement result for the first object; predicting whether the measurement result for the first object satisfies the measurement event; and predicting the TA value of the first object.

[0425] In some embodiments, the first object includes at least one of the following: a cell; a beam.

[0426] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0427] 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.

[0428] 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.

[0429] 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).

[0430] Figure 5A is a schematic diagram of the structure of the communication device 5100 proposed in 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.

[0431] 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.

[0432] 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 (e.g., steps S201, S202, but not limited thereto) in the above method, such as sending and / or receiving, while the processor 5101 performs at least one of other steps (e.g., steps S201, S202, 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, sending unit, transmitter, sending circuit, etc., can be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0433] 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 5103 and can be used to receive data from the memories 5103 or other devices, and to send data to the memories 5103 or other devices. For example, the interface circuits 5104 can read data stored in the memories 5103 and send the data to the processor 5101.

[0434] 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.

[0435] 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.

[0436] Chip 5200 includes one or more processors 5201. Chip 5200 is used to perform any of the methods described above.

[0437] 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.

[0438] In some embodiments, the interface circuit 5202 performs at least one of the communication steps (e.g., steps S201, S202, but not limited thereto) in the above-described method, such as sending and / or receiving. For example, the interface circuit 5202 performing the communication steps (e.g., sending and / or receiving) in the above-described method refers 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 S201, S202, but not limited thereto).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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 method for performing mobility operations, characterized in that, The method, executed by a terminal, includes: If the triggering conditions are met, perform AI-assisted mobility-related operations on the first object.

2. The method according to claim 1, characterized in that, The triggering condition includes at least one of the following: A trigger command to perform synchronization-related operations on the first object is received; The mobility configuration of the first object has undergone decoding and / or integrity checks; The measurement results of the first object meet the measurement conditions.

3. The method according to claim 2, characterized in that, The triggering command includes at least one of the following: A trigger command to perform early uplink synchronization on the first object; A command to trigger early downlink synchronization for the first object.

4. The method according to any one of claims 2 to 3, characterized in that, The aforementioned pre-synchronization operation on the first object includes at least one of the following: The first object is synchronized in advance; Obtain the advance timing TA value of the first object in advance; Activate the Transmission Configuration Indicator (TCI) state of the first object; Fine tracking is performed on the first object.

5. The method according to claim 2, characterized in that, The measurement result satisfies the measurement conditions, including at least one of the following: The measurement results satisfy the mobility LTM event triggered by layer 1 / layer 2; The measurement results satisfy the Layer 3 Radio Resource Management (RRM) event; The relationship between the measurement results and the threshold values ​​satisfies the first relationship.

6. The method according to any one of claims 2 to 5, characterized in that, The AI-assisted mobility-related operations include at least one of the following: Collect the first data for training AI models; Collect secondary data for AI model predictions; Perform predictive operations based on AI models; The prediction results obtained based on the AI ​​model are sent to the network device.

7. The method according to claim 6, characterized in that, The first data includes at least one of the following: Historical measurement results for the first object; Measurement results for layer 1 of the first object; Layer 3 measurement results for the first object; Does the measurement result meet the measurement conditions? Information regarding whether the measurement results meet the measurement conditions; Historical TA values; Current TA value.

8. The method according to claim 6, characterized in that, The prediction operation includes at least one of the following: Predicted Radio Link Failure (RLF) for the first object; Beam prediction for the first object failed; The first object is predicted to fail a handover (HOF). Predict the measurement results for the first object; Predict how well the measurement results for the first object satisfy the measurement event; Predict the TA value of the first object.

9. The method according to any one of claims 1 to 8, characterized in that, The first object includes at least one of the following: residential community; Beam.

10. A mobility operation triggering method, characterized in that, Performed by a network device, the method includes: The terminal is instructed to trigger conditions, wherein, if the trigger conditions are met, the terminal is triggered to perform AI-assisted mobility-related operations on the first object.

11. The method according to claim 10, characterized in that, The condition for triggering is satisfied includes at least one of the following: A trigger command to perform synchronization-related operations on the first object is received; The mobility configuration of the first object has undergone decoding and / or integrity checks; The measurement results of the first object meet the measurement conditions.

12. The method according to claim 11, characterized in that, The trigger command includes at least one of the following: A trigger command to perform early uplink synchronization on the first object; A command to trigger early downlink synchronization for the first object.

13. The method according to any one of claims 11 to 12, characterized in that, The aforementioned pre-synchronization operation on the first object includes at least one of the following: The first object is synchronized in advance; Obtain the advance timing TA value of the first object in advance; Activate the Transmission Configuration Indicator (TCI) state of the first object; Fine tracking is performed on the first object.

14. The method according to claim 11, characterized in that, The measurement result satisfies the measurement conditions, including at least one of the following: The measurement results satisfy the mobility LTM event triggered by layer 1 / layer 2; The measurement results satisfy the Layer 3 Radio Resource Management (RRM) event; The relationship between the measurement results and the threshold values ​​satisfies the first relationship.

15. The method according to any one of claims 11 to 14, characterized in that, The AI-assisted mobility-related operations include at least one of the following: Collect the first data for training AI models; Collect secondary data for AI model predictions; Perform predictive operations based on AI models; The prediction results obtained based on the AI ​​model are sent to the network device.

16. The method according to claim 15, characterized in that, The first data includes at least one of the following: Historical measurement results for the first object; Measurement results for layer 1 of the first object; Layer 3 measurement results for the first object; Does the measurement result meet the measurement conditions? Information regarding whether the measurement results meet the measurement conditions; Historical TA values; Current TA value.

17. The method according to claim 15, characterized in that, The prediction operation includes at least one of the following: Predicted Radio Link Failure (RLF) for the first object; Beam prediction for the first object failed; The first object is predicted to fail a handover (HOF). Predict the measurement results for the first object; Predict how well the measurement results for the first object satisfy the measurement event; Predict the TA value of the first object.

18. The method according to any one of claims 10 to 17, characterized in that, The first object includes at least one of the following: residential community; Beam.

19. A mobile operation execution device, characterized in that, The device includes: The processing module is configured to perform AI-assisted mobility-related operations on the first object when the triggering conditions are met.

20. A mobility operation triggering device, characterized in that, The device includes: The sending module is configured to indicate a triggering condition to the terminal, wherein, when the triggering condition is met, the terminal is triggered to perform an AI-assisted mobility-related operation on the first object.

21. A terminal, characterized in that, include: One or more processors; The terminal is used to execute the mobility operation execution method according to any one of claims 1 to 9.

22. A network device, characterized in that, include: One or more processors; The network device is used to execute the mobility operation triggering method according to any one of claims 10 to 18.

23. A communication system, characterized in that, The device includes a terminal and a network device, wherein the terminal is configured to implement the mobility operation execution method according to any one of claims 1 to 9, and the network device is configured to implement the mobility operation triggering method according to any one of claims 10 to 18.

24. A storage medium storing instructions, characterized in that, When the instruction is executed on the communication device, the communication device performs the mobility operation execution method according to any one of claims 1 to 9, and / or the mobility operation triggering method according to any one of claims 10 to 18.

25. A program product, characterized in that, When the above-mentioned program product is executed by a communication device, the communication device performs the mobility operation execution method according to any one of claims 1 to 9, and / or the mobility operation triggering method according to any one of claims 10 to 18.

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