Time domain mobility prediction method and device, equipment, chip and storage medium

Abstract

The embodiment of the invention provides a time domain mobility prediction method and device, communication equipment, a chip and a storage medium, and the method comprises the steps that a terminal obtains K measurement instances corresponding to a first downlink reference signal set, the K measurement instances are obtained by measuring downlink reference signals in the first downlink reference signal set at K measurement moments based on the terminal, and the first downlink reference signal set comprises first downlink reference signal subsets of N cells; wherein the K measurement instances are used for acquiring F prediction instances corresponding to a second downlink reference signal set based on a first model, different prediction instances correspond to different prediction moments, and the second downlink reference signal set comprises second downlink reference signal subsets of P cells; k, N, F and P are integers greater than or equal to 1.

Description

A time domain mobility prediction method, device, equipment, chip and storage medium Technical Field

[0001] The embodiments of the present application relate to the field of communication technology, and specifically to a time domain mobility prediction method and apparatus, device, chip, and storage medium. Background Art

[0002] Traditional mobility solutions are measurement-based and reactive. Link quality degradation is detected only after it reaches a certain level, triggering subsequent mobility management processes, such as cell handover. However, this approach often exhibits a certain degree of lag, resulting in long cell handover delays (or untimely cell handovers), which in turn degrades communication performance.

[0003] Summary of the Invention

[0004] Embodiments of the present application provide a time-domain mobility prediction method and apparatus, device, chip, and storage medium.

[0005] In a first aspect, an embodiment of the present application provides a mobility prediction method, the method comprising:

[0006] The terminal obtains K measurement instances corresponding to a first downlink reference signal set, where the K measurement instances are obtained based on measurements of downlink reference signals in the first downlink reference signal set by the terminal at K measurement moments. The first downlink reference signal set includes a first downlink reference signal subset of N cells. The K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on the first model, where different prediction instances correspond to different prediction moments. The second downlink reference signal set includes second downlink reference signal subsets of P cells. K, N, F, and P are integers greater than or equal to 1.

[0007] In a second aspect, an embodiment of the present application provides a mobility prediction method, the method comprising:

[0008] The network device receives K measurement instances corresponding to a first downlink reference signal set sent by the terminal, where the K measurement instances are obtained based on measurements of downlink reference signals in the first downlink reference signal set by the terminal at K measurement moments, and the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on the first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; and K, N, F, and P are integers greater than or equal to 1.

[0009] In a third aspect, an embodiment of the present application provides a mobility prediction device, applied to a terminal, the device comprising:

[0010] An acquisition unit is configured to acquire K measurement instances corresponding to a first downlink reference signal set, where the K measurement instances are obtained based on measurements of downlink reference signals in the first downlink reference signal set by a terminal at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to acquire F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; and K, N, F, and P are integers greater than or equal to 1.

[0011] In a fourth aspect, an embodiment of the present application provides a mobility prediction device, applied to a network device, comprising:

[0012] A receiving unit is configured to receive K measurement instances corresponding to a first downlink reference signal set sent by a terminal, where the K measurement instances are obtained based on measurements of downlink reference signals in the first downlink reference signal set by the terminal at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; and K, N, F, and P are integers greater than or equal to 1.

[0013] In a fifth aspect, an embodiment of the present application provides a communication device, which includes a memory and a processor; wherein the memory is used to store computer-executable instructions; the processor is connected to the memory and is used to implement a method as described in any of the above aspects by executing the computer-executable instructions.

[0014] In a sixth aspect, an embodiment of the present application provides a chip, which includes a processor configured to call and execute a computer program from a memory, so that a device equipped with the chip executes any of the above methods.

[0015] In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by at least one processor, it implements the method as described in any of the above aspects.

[0016] In the technical solution of the embodiment of the present application, the K measurement instances are historical measurement results obtained by the terminal measuring the downlink reference signals in the first downlink reference signal set at K measurement times. Based on the first model, F future prediction instances can be predicted according to the historical measurement results. This mobility solution is a proactive solution with strong foresight. The F prediction instances can be used to trigger the mobility management process in a timely manner, thereby avoiding the degradation of communication performance caused by the lag of the mobility management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] FIG1 is a schematic diagram of an application scenario of an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of an example of downlink beam management in an NR system;

[0020] Figure 3 is a schematic diagram of an example of neuron structure;

[0021] FIG4 is a schematic diagram of an example of a neural network;

[0022] Figure 5 is a schematic diagram of an LSTM network;

[0023] FIG6 is a schematic diagram of an example of a neural network model applicable to an embodiment of the present application;

[0024] FIG7 is another schematic diagram of a neural network model applicable to an embodiment of the present application;

[0025] FIG8 is a schematic diagram of another example of a neural network model applicable to an embodiment of the present application;

[0026] FIG9 is a schematic diagram of a time domain mobility measurement and prediction provided in an embodiment of the present application;

[0027] FIG10 is a schematic diagram of a timing relationship between a measurement instance and a prediction instance provided in an embodiment of the present application;

[0028] FIG11 is another schematic diagram of the timing relationship between the measurement instance and the prediction instance provided in an embodiment of the present application;

[0029] FIG12 is a flow chart of a time domain mobility prediction method according to an embodiment of the present application;

[0030] FIG13 is a schematic diagram of the input-output relationship of the time-domain mobility prediction model provided in an embodiment of the present application;

[0031] FIG14 is a second schematic diagram of the input-output relationship of the time-domain mobility prediction model provided in an embodiment of the present application;

[0032] FIG15 is a third schematic diagram of the input-output relationship of the time-domain mobility prediction model provided in an embodiment of the present application;

[0033] FIG16 is a second flow chart of a time domain mobility prediction method provided in an embodiment of the present application;

[0034] FIG17 is a third flow chart of a time domain mobility prediction method provided in an embodiment of the present application;

[0035] FIG18 is a schematic diagram of the first structure of a mobility prediction device provided in an embodiment of the present application;

[0036] FIG19 is a second schematic diagram of the structure of the mobility prediction device provided in an embodiment of the present application;

[0037] FIG20 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0038] FIG21 is a schematic structural diagram of a chip according to an embodiment of the present application;

[0039] Figure 22 is a schematic block diagram of a communication system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] FIG1 is a schematic diagram of an application scenario of an embodiment of the present application.

[0042] As shown in Figure 1, the communication system 100 may include a terminal (such as the terminal 110 in Figure 1) and a network device (such as the network device 120, the network device 130, and the network device 140 in Figure 1). The network device can communicate with the terminal through the air interface. Exemplarily, the network device 120, the network device 130, and the network device 140 may be located in different cells, or in other words, may serve different cells. For example, in the communication system 100, the network device 120 is located in cell #1, the network device 130 is located in cell #2, and the network device 130 is located in cell #3. Among them, the terminal 110 may be located in one or more cells. For example, in the communication system 100, the terminal 110 is located in cell #1. In this scenario, cell #1 is the serving cell of the terminal 110, and cells #2 and #3 are non-serving cells of the terminal 110.

[0043] In Figure 1, the union of the beams (pairs) represented by the solid line and the dotted line is the full set of beams (pairs) between each cell and the terminal. The beams (pairs) represented by the solid line are the beams (pairs) that the terminal or network equipment needs to measure, and the beams (pairs) represented by the dotted line are the beams (pairs) that the terminal or network equipment does not need to measure but belong to the full set of beams (pairs).

[0044] It should be understood that the embodiments of the present application are only illustrative of the communication system 100, but the embodiments of the present application are not limited thereto. That is, the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: Long Term Evolution (LTE) system, LTE Time Division Duplex (TDD), Universal Mobile Telecommunication System (UMTS), Internet of Things (IoT) system, Narrow Band Internet of Things (NB-IoT) system, enhanced Machine-Type Communications (eMTC) system, 5G communication system (also known as New Radio (NR) communication system), or future communication systems.

[0045] In the communication system 100 shown in Figure 1, network devices (such as network devices 120, 130, and 140) may be access network devices that communicate with terminals (such as terminal 110). Access network devices may provide communication coverage for a specific geographic area and may communicate with terminals (such as UEs) located within the coverage area.

[0046] The network device may be an evolved Node B (eNB or eNodeB) in a Long Term Evolution (LTE) system, or a Next Generation Radio Access Network (NG RAN) device, or a gNB in ​​an NR system, or a radio controller in a Cloud Radio Access Network (CRAN), or the network device may be a macro base station, a micro base station (also known as a small station), a satellite, a Radio Network Controller (RNC), a Node B (NB), a Base Station Controller (BSC), a Base Transceiver Station (BTS), a home base station (e.g., Home Evolved NodeB, or Home Node B, HNB), a Baseband Unit (BBU), an Access Point (AP) in a Wireless Fidelity (WiFi) system, a wireless relay node, a wireless backhaul node, a Transmission Point (TP) or a Transmission and Reception Point (TRP), etc. The network device may also be a relay station, an access point, a vehicle-mounted device, a wearable device, a hub, a switch, a bridge, a router, or a network device in a future evolved public land mobile network (PLMN).

[0047] The terminal may be any terminal, including but not limited to a terminal connected to a network device or other terminal by wire or wireless connection.

[0048] For example, the terminal may refer to an access terminal, user equipment (UE), a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent, or a user apparatus. An access terminal may be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, an IoT device, a satellite handheld terminal, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a wearable device, a terminal in a 5G network or a terminal in a future evolution network, etc.

[0049] The terminal can be used for device-to-device (D2D) communication.

[0050] It should be understood that the specific forms of the terminals and network devices in the embodiments of the present application are not particularly limited and are merely illustrative.

[0051] The wireless communication system 100 may also include a core network device (not shown in FIG1 ) that communicates with the network device. The core network device may be a 5G core network (5G Core, 5GC) device, such as an Access and Mobility Management Function (AMF), an Authentication Server Function (AUSF), a User Plane Function (UPF), or a Session Management Function (SMF). Optionally, the core network device may also be an Evolved Packet Core (EPC) device of an LTE network, such as a Session Management Function + Core Packet Gateway (SMF+PGW-C) device. It should be understood that SMF+PGW-C can simultaneously implement the functions that can be implemented by SMF and PGW-C. During the network evolution process, the above-mentioned core network device may also be called other names, or a new network entity may be formed by dividing the functions of the core network, which is not limited in the embodiments of the present application.

[0052] The functional units in the communication system 100 may also establish connections and implement communication via next generation (NG) network interfaces.

[0053] For example, the terminal establishes an air interface connection with the access network device through the NR interface for transmitting user plane data and control plane signaling; the terminal can establish a control plane signaling connection with the AMF through the NG interface 1 (referred to as N1); the access network device, such as the next generation wireless access base station (gNB), can establish a user plane data connection with the UPF through the NG interface 3 (referred to as N3); the access network device can establish a control plane signaling connection with the AMF through the NG interface 2 (referred to as N2); the UPF can establish a control plane signaling connection with the SMF through the NG interface 4 (referred to as N4); the UPF can exchange user plane data with the data network through the NG interface 6 (referred to as N6); the AMF can establish a control plane signaling connection with the SMF through the NG interface 11 (referred to as N11); the SMF can establish a control plane signaling connection with the PCF through the NG interface 7 (referred to as N7).

[0054] FIG1 exemplarily shows three network devices and one terminal. Optionally, the wireless communication system 100 may include one or more network devices and each network device may include other numbers of terminals within its coverage area, which is not limited in the embodiments of the present application.

[0055] It should be noted that Figure 1 is merely an example of a system applicable to this application. Of course, the methods described in the embodiments of this application can also be applied to other systems. Furthermore, the terms "system" and "network" are often used interchangeably herein. The term "and / or" herein simply describes an association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the associated objects are in an "or" relationship. It should also be understood that the "indication" mentioned in the embodiments of this application can be a direct indication, an indirect indication, or an indication of an association relationship. For example, "A indicates B" can mean that A directly indicates B, for example, B can obtain information through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can obtain information through C; or it can mean that A and B have an association relationship. It should also be understood that the "correspondence" mentioned in the embodiments of this application can mean that there is a direct or indirect correspondence between two objects, or that there is an association relationship between the two objects, or a relationship between an indicator and the indicated, a configuration and the configured, and so on. It should also be understood that the “predefined” or “predefined rules” mentioned in the embodiments of the present application can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in a device (for example, including a terminal and a network device), and the present application does not limit its specific implementation method. For example, predefined can refer to what is defined in the protocol. It should also be understood that in the embodiments of the present application, the “protocol” may refer to a standard protocol in the field of communications, for example, it may include an LTE protocol, an NR protocol, and related protocols used in future communication systems, and the present application does not limit this.

[0056] The traditional mobility solution is based on measurement and is a passive solution, that is, the link quality will not be discovered until it drops to a certain level, thereby triggering the subsequent mobility management process, such as cell switching, but it often has a certain lag. For this reason, the technical solution of the embodiment of the present application is proposed. The technical solution of the embodiment of the present application, the mobility solution based on model prediction, is based on historical measurement results to predict the link performance of multiple cells in the future, so as to select the optimal cell. It is a proactive solution with strong foresight, so it can also save the delay of cell switching and reduce the risk of wireless link failure. Let the terminal directly transition from a cell with better link quality to another cell with better link quality, and will not experience the performance degradation caused by untimely cell switching.

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

[0058] 1. NR beam management

[0059] The first version of the NR system (Rel.15) introduced millimeter wave frequency band communications, which also introduced the beam management mechanism. Beam management can be divided into uplink and downlink beam management. Among them, downlink beam management includes: downlink beam sweeping (Beam Sweeping), UE beam measurement and reporting (Measurement & Reporting), downlink beam indication (Beam Indication) of the network (NW), etc.; uplink beam management includes: uplink beam sweeping (UE sends beam sweeping SRS), NW beam measurement, and uplink beam indication and configuration for UE.

[0060] Figure 2 is a schematic diagram of an example of downlink beam management in the NR system. Taking the downlink beam scanning process as an example, it can be divided into three processes, namely P1, P2 and P3 processes. Among them, the P1 process refers to the NW scanning different transmit beams and the UE scanning different receive beams; the P2 process refers to the NW scanning different transmit beams and the UE using the same receive beam; the P3 process refers to the NW using the same transmit beam and the UE scanning different receive beams. Generally, the P1 process can be executed before the P2 and P3 processes. For example, in the example of Figure 2, the execution order can be P1, P2 and P3. Generally, the NW completes the above beam scanning process by sending a downlink reference signal - Synchronization Signal and PBCH Block (SSB) and / or Channel State Information-Reference Signal (CSI-RS).

[0061] The beam reporting mechanism in NR Rel.15 includes: the UE measures multiple transmit beams (P2 process) or transmit-receive beam pairs (P1 process) and reports the L transmit beams with the highest layer 1 reference signal receiving power (L1-Reference Signal Receiving Power, RSRP, L1-RSRP) and their quality to the NW in the form of channel state information (CSI). After decoding the beam information reported by the UE, the NW can consider the downlink transmission channel and signal, and carry the transmission configuration indicator (TCI) state (TCI State) (including the SSB or CSI-RS resource index as a reference for the UE) through medium access control (MAC) and / or downlink control information (DCI) signaling to indicate the beam information to the UE. The UE uses the receive beam corresponding to the transmit beam of the indicated SSB or CSI-RS for downlink reception.

[0062] Accordingly, NR defines three uplink beam scanning processes, namely U1, U2, and U3. Among them, the U1 process includes: the UE scans different transmit beams, and the NW scans different receive beams; the U2 process includes: the UE uses the same transmit beam, and the NW scans different receive beams; the U3 process includes: the UE scans different transmit beams, and the NW uses the same receive beam. For the uplink beam scanning process, since the NW measures the beam from the UE, the UE beam report is not required. The NW can select the appropriate uplink beam from the measured uplink beam or configure it to the UE for uplink transmission. At the same time, the NW can prepare the corresponding receive beam.

[0063] 2. NR inter-cell beam management

[0064] NR's inter-cell beam management is only applicable to downlink scenarios.

[0065] In the beam management of Rel.17, based on the UE's capability reporting, the NW can configure and / or activate the beam management function for non-serving cells for the UE, including beam measurement, beam reporting and beam indication.

[0066] The UE can measure non-serving cells other than the serving cell. Non-serving cells have a different physical cell identifier (PCI) than the serving cell. The UE's capabilities support measuring 1, 2, 3, or 7 non-serving cells. By measuring the SSBs of non-serving cells, the UE can select a downlink beam with better beam quality. In a single beam report, the UE can only report up to four downlink transmit beams from a non-serving cell, including the resource index corresponding to the beam and link quality, such as L1-RSRP.

[0067] The NW can use a unified TCI state to indicate beams from non-serving cells. For example, the NW can configure the reference signal from a non-serving cell (identified by the PCI) in the TCI state. If this TCI state is indicated to the UE, it indicates that the UE will use the downlink beam from that cell.

[0068] 3. NR Layer 1 / Layer 2 Triggered Mobility

[0069] In the layer 1 / layer 2 (L1 / L2) triggered mobility management of Rel.18, the standardization has given a certain degree of enhancement in measurement and reporting compared to the inter-cell beam management in Rel.17. The specific enhancements are as follows: the UE can perform measurements in the serving cell and target cell configured by the NW, and provide the measurement results of up to M×L downlink reference signals in one report. The maximum values ​​of M and L are determined based on the capabilities of the UE. Obviously, for the measurement of a large number of candidate cells, a large amount of downlink reference signal overhead is required, as well as the delay required for the UE to measure these downlink reference signals. After all, the UE often cannot measure multiple beams (pairs) of multiple candidate cells at the same time.

[0070] 4. Neural Network (NN)

[0071] 1) Neuronal structure

[0072] A neural network is a computational model consisting of multiple interconnected neuron nodes. The connections between the nodes represent the weighted values ​​from the input signal to the output signal, which are called weights. Each node performs a weighted summation of different input signals and outputs them through a specific activation function. The neuron structure is shown in Figure 3, where a1, a2, ..., a n and 1 are the inputs of neurons, w1, w2, ..., w n and b represent weights, Sum represents the summation function, f represents the activation function, and t is the output result.

[0073] 2) Deep Neural Networks

[0074] Figure 4 shows a simple neural network. The neural network includes an input layer, a hidden layer, and an output layer. By connecting multiple neurons in different ways, combined with different weights and activation functions, different outputs can be generated, thereby fitting the mapping relationship from input to output. For example, each upper-level node can be connected to all of its lower-level nodes to form a fully connected model. This fully connected model can also be called a deep neural network (DNN). This NN model can be used for spatial-domain DL beam prediction or temporal DL beam prediction.

[0075] 3) Long Short-Term Memory (LSTM) Model

[0076] Figure 5 shows an LSTM model. The LSTM model can be understood as extending K moments as input in time series, which is equivalent to the cascade of K LSTM units. The input of each LSTM unit is Set B k L1-RSRP of beam(pair) in (1≤k≤K), Set B k Represents Set B corresponding to time k.

[0077] It should be noted that Set B k The beam (pair) index can be implicitly input through a fixed ordering of L1-RSRP. After completing the L1-RSRP input for K time steps, the LSTM model can predict the beam prediction instances for the next F time steps. Each beam prediction instance contains the optimal beam (pair) index, the link quality corresponding to the optimal beam (pair) index, and the duration of the optimal beam (pair).

[0078] 5. Model acquisition

[0079] An NN model can be trained through the processes of data set construction, training, verification and testing. In the embodiments of the present application, it is assumed that the NN models have been trained in advance through offline training or online training. It should be noted that offline training and online training are not mutually exclusive. For example, the NW can first obtain a static training result through offline training of the data set, and this process can be called offline training. During the use of the NN by the NW or UE, as the UE further measures and / or reports, the NN model can continue to collect more data and perform real-time online training to optimize the parameters of the NN model, thereby achieving better inference and prediction results.

[0080] 6. Beam management based on artificial intelligence (AI) / machine learning (ML)

[0081] AI / ML-based beam management is the main use case of the Release 18 AI project, and two use cases are defined: spatial-domain DL beam prediction and temporal DL beam prediction.

[0082] Definition of spatial-domain beam prediction (BM-Case 1): Downlink beams in Set A are spatially predicted by measuring beams (pairs) in Set B. Set B is either a subset of Set A or different sets of beams (pairs). Set B can be considered a subset of Set A, while Set A can be considered the full set of beams (pairs).

[0083] Definition of time-domain beam prediction (BM-Case2): The optimal beam (or beam pair) in Set A is predicted F times in the future by measuring the beams (or beam pairs) in Set B K times. Set B can be a subset of Set A or the same as Set A. If Set B is the same as Set A, beam prediction is purely time-domain. If Set B is a subset of Set A, beam prediction is performed in both the spatial and time domains.

[0084] In addition, Set B and Set A can also be two different sets, Set B is a group of a small number of wide beams, and Set A is a group of a large number of narrow beams. The small number of wide beams in Set B can roughly cover the large number of narrow beams in Set A in space.

[0085] The following is a brief introduction to the neural network model applicable to the embodiments of the present application.

[0086] The technical solution of the embodiment of the present application can use a DNN model to predict time domain mobility, or, considering the time domain factors, an LSTM model can also be used to predict time domain mobility. Although the technical solutions of the examples of the present application are all illustrated using the DNN model as an example, they are not limited to this. Other models that can achieve time domain mobility prediction (such as LSTM models) are applicable to the technical solutions of the embodiments of the present application. The following uses the DNN model shown in Figures 6 to 8 as an example to illustrate the implementation of the model.

[0087] Figure 6 is a schematic diagram of an example of a neural network model applicable to an embodiment of the present application. The model is an optimal beam (pair) prediction model, and it can be considered that the model solves a multi-classification problem. The model can be used to fit the relationship between the measurement results of Set B (such as the L1-RSRP of the reference signal / beam (pair) in Set B) and the optimal L beams (pairs) in Set A. Among them, the measurement results of Set B can be used as the input of the model, and the output can be the optimal J beam (pair) indexes selected from the entire set (Set A), that is, the J beams (pairs) with the highest L1-RSRP in Set A. In the example of Figure 6, the number of beams (pairs) in Set B is M, the number of beams (pairs) in Set A is M', J=1, and beam (pair) #2 is the beam (pair) with the highest L1-RSRP, that is, the optimal beam (pair). The label used by the model is the optimal (i.e., the highest L1-RSRP) beam (pair) index measured in the entire set.

[0088] Figure 7 is another schematic diagram of a neural network model applicable to an embodiment of the present application. The model is an optimal beam quality prediction model, which can be understood as a linear regression problem. The relationship between the input and output of the model is: the relationship from the partial subset input L1-RSRP to the optimal L1-RSRP of the K beams (pairs). The input part of the model is the same as the input of the model shown in Figure 6, except that the output of the model is J (J ≥ 1) optimal L1-RSRPs. In the example of Figure 7, the number of beams (pairs) in Set B is M. The labels used by the model are the optimal K L1-RSRPs measured in the entire set, and the corresponding K beam (pair) indexes.

[0089] Figure 8 is another schematic diagram of a neural network model applicable to an embodiment of the present application. The model is an optimal beam quality and optimal beam index prediction model, which can be understood as a linear regression problem. The input part of the model is the same as the input of the model shown in Figure 6, except that the output of the model is the L1-RSRP of all beams (pairs) in the full set (Set A). By sorting the L1-RSRPs of all beams (pairs) from high to low, the optimal J (i.e., Top-J) L1-RSRPs can be selected, and then according to the output position corresponding to the optimal J L1-RSRPs, the corresponding optimal J beam (pair) indexes can be found. Therefore, the model shown in Figure 8 can implement the functions of the two models of Figures 6 and 7.

[0090] As an implementation, the DNN model can be understood as two different models that use the same input (i.e., Set B) and have two different outputs: one output is the index of the optimal J (i.e., Top-J) beams (pairs), and the other is the link quality of the optimal J (i.e., Top-J) beams (pairs), i.e., L1-RSRP, as shown in Figures 6 and 7. As another implementation, the DNN model can implement the functions of both models in Figures 6 and 7 through a single model, as shown in Figure 8.

[0091] It should be understood that the output of the model can also be understood as an inference or prediction result, that is, inference and prediction can represent the same meaning and can be replaced with each other.

[0092] To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The above related technologies can be combined arbitrarily with the technical solutions of the embodiments of the present application as optional solutions, and all of them fall within the scope of protection of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0093] It should be noted that the "beam (pair)" in the embodiments of the present application can refer to a beam, including a transmit beam or a receive beam, or a beam pair, such as a pair of transmit beams and a receive beam. The meaning of the "beam (pair)" in the embodiments of the present application applies to downlink transmission. The "beam (pair)" in the embodiments of the present application can also be referred to as a spatial filter, that is, the terms "beam (pair)" and "spatial filter" are interchangeable.

[0094] It should be noted that the "downlink reference signal resource index" in the embodiments of the present application may also be referred to as a beam (pair) index, a downlink reference signal index, or a spatial filter index. That is, the downlink reference signal resource index, beam (pair) index, downlink reference signal index, and spatial filter index may be interchangeable. For example, the downlink reference signal resource index may be a CSI-RS resource indicator (CRI) or an SSB resource indicator (SSBRI).

[0095] It should be noted that the downlink reference signal in the embodiment of the present application may include: Channel State Information-Reference Signal (CSI-RS) and / or SSB. The measurement result obtained by measuring the downlink reference signal characterizes the link quality. In some embodiments, the link quality may include at least one of the following: Reference Signal Receiving Power (RSRP) (such as L1-RSRP), Signal to Interference plus Noise Ratio (SINR) (such as L1-SINR), Received Signal Strength Indicator (RSSI) (such as L1-RSSI), Reference Signal Receiving Quality (RSRQ) (such as L1-RSRQ).

[0096] First, a first downlink reference signal set and a second downlink reference signal set are defined.

[0097] The first downlink reference signal set is Set B or Set C (referred to as Set B / Set C), and the second downlink reference signal set is Set A. A "downlink reference signal set" can be understood as a set of downlink reference signals, a set of downlink reference signal resources, or a set of beams (pairs). Therefore, a "downlink reference signal set" can also be referred to as a "downlink reference signal resource set" or a "beam (pair) set."

[0098] In some scenarios, Set B can be a subset of Set A. In this case, Set B can be understood as a partial subset of beams (pairs), and Set A can be understood as the full set of beams (pairs). In other scenarios, Set B and Set A can also be two different sets of beams (pairs). For example, Set B can be used as a measurement set, and Set A can be used as a prediction set. In an embodiment of the present application, a neural network model (referred to as the model) can predict the beams in Set A by measuring the beams in Set B.

[0099] In some scenarios, Set B is the measurement set and also the model input set. In other scenarios, Set C is the measurement set, and the optimal M beams (pairs) are selected from Set C as Set B, which is the model input set. In other scenarios, Set B is a subset of Set C.

[0100] In some embodiments, the first downlink reference signal set may include N (N ≥ 1) first downlink reference signal subsets, wherein each first downlink reference signal subset may correspond to a cell. It can be understood that the first downlink reference signal set includes the first downlink reference signal subsets of N cells. As an example, the nth (1 ≤ n ≤ N) first downlink reference signal subset in Set B / Set C may be recorded as Set B n / Set C n For example, the first downlink reference signal subset in Set B / Set C can be recorded as Set B1 / Set C1, with the index of Set B1 / Set C1 being 1; the second first downlink reference signal subset in Set B / Set C can be recorded as Set B2 / Set C2, with the index of Set B2 / Set C2 being 2, and so on. It should be understood that the order of the N first downlink reference signal subsets in Set B / Set C is exemplary and can be changed, and is not limited in the embodiments of the present application.

[0101] In some embodiments, the second downlink reference signal set may include P (P ≥ 1) second downlink reference signal subsets, wherein each second downlink reference signal subset may correspond to one cell. It can be understood that the second downlink reference signal set includes the second downlink reference signal subsets of P cells. As an example, the pth (1 ≤ p ≤ P) second downlink reference signal subset in Set A may be recorded as Set A p For example, the first second downlink reference signal subset in Set A can be recorded as Set A1, with an index of 1; the second second downlink reference signal subset in Set A can be recorded as Set A2, with an index of 2; and so on. It should be understood that the order of the P second downlink reference signal subsets in Set A is exemplary and can be changed, and is not limited in the embodiments of the present application.

[0102] As an implementation, the value of N may be equal to the value of P. As another implementation, the value of N may be different from the value of P. For example, the value of N may be much smaller than the value of P. This can significantly reduce the number of downlink reference signals that need to be actually measured, thereby reducing computational overhead and latency.

[0103] Next, define the first model.

[0104] The first model is used for mobility prediction, and the first model can be a neural network model, such as the above-mentioned DNN model, or an LSTM model. The technical solution of the embodiment of the present application can use a DNN model for mobility prediction, or, considering the time domain factors, an LSTM model can also be used for mobility prediction. Although the technical solutions of the examples of the present application are all illustrated using the DNN model as an example, they are not limited to this. Other models that can realize time domain mobility prediction (such as the LSTM model) are applicable to the technical solutions of the embodiments of the present application.

[0105] The first model is a trained model that can use the measurement results of limited historical beams (pairs) to select the optimal cell and the optimal beam (pair) within the optimal cell from the global perspective (multiple cells and all corresponding beams (pairs)).

[0106] As an implementation method, the first model is a model on the terminal side (also referred to as a model on the UE side), or in other words, the first model is a model deployed on the terminal side. The first model can implement beam prediction in the time domain, and the beam prediction in the time domain can be a beam prediction in the pure time domain, or a beam prediction in the time domain and the space domain. As another implementation method, the first model is a model on the network device side (also referred to as a model on the NW side), or in other words, the first model is a model deployed on the network device side. The first model can implement beam prediction in the time domain, and the beam prediction in the time domain can be a beam prediction in the pure time domain, or a beam prediction in the time domain and the space domain.

[0107] The input of the first model is K (K ≥ 1) measurement instances, where different measurement instances correspond to different measurement moments. Each measurement instance is obtained by the terminal measuring the downlink reference signal in the first downlink reference signal set, that is, the measurement instance can be understood as the terminal's measurement result of Set B or partial measurement result of Set C (such as the measurement results of the optimal M beams (pairs) in Set C).

[0108] The output of the first model is F (F≥1) prediction instances, different prediction instances correspond to different prediction moments, where each prediction instance contains the optimal L (L≥1) cell indices among P (P≥1) cells, and / or the optimal J (J≥1) beam (pair) indices in each optimal area, and / or the link quality corresponding to each optimal beam (pair) index.

[0109] This application does not limit the specific network structure of the first model. The neural network model can also be called an AI model or an ML model.

[0110] For example, FIG9 is a schematic diagram of a time domain mobility measurement and prediction provided by an embodiment of the present application. In FIG9 , the coverage range of TRP1 is cell #1, the coverage range of TRP2 is cell #2, and the coverage range of TRP3 is cell #3. Assume that the UE roams in multiple cells (cell #1, cell #2, cell #1) at a certain speed, and the trajectory of the UE movement is shown as the dotted line. The UE performs measurements at K (K≥1) measurement moments to obtain K measurement instances. The first model predicts the future F (F≥1) prediction instances based on the K measurement instances. Each prediction instance includes the optimal L (L≥1) cell indexes, and / or the optimal J (J≥1) beam (pair) indexes in each optimal cell, and / or the link quality corresponding to each optimal beam (pair), such as L1-RSRP. Figure 9 illustrates two measurement instances (i.e., K=2) and two prediction instances (i.e., F=2). In Figure 9, the UE performs two measurements on the measurement set (i.e., Set B / Set C) in cell #1, obtaining two measurement instances; two prediction instances are predicted for the prediction set (i.e., Set A) based on these two measurement instances using the first model; the reference signal actually measured by the UE is represented by a solid ellipse, and the reference signal that is not measured but in the prediction set (i.e., Set A) is represented by a dotted ellipse.

[0111] In the embodiment of the present application, the measurement time corresponding to the measurement instance and the prediction time corresponding to the prediction instance may satisfy a certain temporal relationship.

[0112] In some implementations, the measurement moments corresponding to the K measurement instances are within a first time period, and the prediction moments corresponding to the F prediction instances are within a second time period, and the second time period does not overlap with the first time period.

[0113] For example, Figure 10 illustrates a temporal relationship between measurement instances and prediction instances (referred to as a Type 1 temporal relationship). During time T1, the terminal performs K measurements on Set B / Set C, obtaining K measurement instances. Based on these K measurement instances, F prediction instances can be predicted within time T2.

[0114] In other implementations, the measurement moments corresponding to the K measurement instances are located within the K first time periods, and the prediction moments corresponding to the F prediction instances are located within the last first time period of the K first time periods.

[0115] For example, Figure 11 illustrates another timing relationship between measurement instances and prediction instances (which may be referred to as a Type 2 timing relationship). Within K T1 time periods, the terminal performs K measurements on Set B / Set C, resulting in K measurement instances, one T1 time period within each T1 time period. Based on the K measurement instances, F prediction instances can be predicted for the last T1 time period of the K T1 time periods.

[0116] FIG12 is a flow chart of a time domain mobility prediction method provided in an embodiment of the present application. As shown in FIG12 , the method includes the following steps:

[0117] Step 1201: The terminal obtains K measurement instances corresponding to a first downlink reference signal set, where the K measurement instances are obtained based on the terminal measuring the downlink reference signals in the first downlink reference signal set at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; K, N, F, and P are integers greater than or equal to 1.

[0118] Here, each measurement moment may correspond to an index, called a measurement moment index; each measurement instance may correspond to an index, called a measurement instance index. In some descriptions, "measurement moment index" and "measurement instance index" may be used interchangeably.

[0119] Here, each prediction moment can correspond to an index, called a prediction moment index; each prediction instance can correspond to an index, called a prediction instance index. In some descriptions, "prediction moment index" and "prediction instance index" can be used interchangeably.

[0120] For ease of description, in the examples described below, the first downlink reference signal set is referred to as Set B / Set C, and the second downlink reference signal set is referred to as Set A. The first downlink reference signal set may also be referred to as a measurement set, and the second downlink reference signal set may also be referred to as a prediction set. For details about Set A, Set B, and Set C, refer to the aforementioned description. The downlink reference signal (or downlink reference signal resource) may include, for example, a CSI-RS (or a CSI-RS resource) and / or an SSB (or an SSB resource).

[0121] Configuration of downlink reference signal sets

[0122] The network device may configure the first downlink reference signal set and / or the second downlink reference signal set for the terminal. The network device sends first configuration information and / or second configuration information to the terminal; the terminal receives the first configuration information and / or second configuration information sent by the network device; the first configuration information is used to configure the first downlink reference signal set; and the second configuration information is used to configure the second downlink reference signal set.

[0123] As an implementation manner, the first configuration information and / or the second configuration information is carried in RRC signaling.

[0124] In some embodiments, the first configuration information is further used to configure first time domain information corresponding to the first downlink reference signal set, where the first time domain information is used to determine K measurement moments corresponding to the K measurement instances. In some embodiments, the first time domain information includes at least one of the following: a measurement period, a first offset, and a number K of measurement moments.

[0125] Exemplarily, the network device configures Set B / Set C for the terminal using RRC signaling. Set B / Set C includes N (N≥1) first downlink reference signal subsets. Each first downlink reference signal subset may correspond to a cell. As an example, the nth (1≤n≤N) first downlink reference signal subset in Set B / Set C may be recorded as Set B. n / Set C n The downlink reference signals (or downlink reference signal resources) in Set B / Set C may include, for example: CSI-RS (or CSI-RS resources) and / or SSB (or SSB resources) for mobile. The network device may also use RRC signaling to configure the measurement period, offset, and number of measurement moments K of Set B / Set C, so that the terminal can know at which moments to perform measurements, and then store the measurement results as input to the first model. For Set B n / Set C n For example, the Set B corresponding to the measurement time k (1≤k≤K) n / Set C n It can be recorded as Set B n,k / Set C n,k For example, Set B corresponding to measurement time 1 n / Set C n It can be recorded as Set B n,1 / Set C n,1 , Set B n,1 / Set C n,1 The index is {n,1}, and the Set B corresponding to measurement time 2 n / Set C nIt can be recorded as Set B n,2 / Set C n,2 , Set B n,2 / Set C n,2 The index of is {n,2}, and so on.

[0126] In some embodiments, the second configuration information is further used to configure second time domain information corresponding to the second downlink reference signal set, and the second time domain information is used to determine F prediction moments corresponding to the F prediction instances. In some embodiments, the second time domain information includes at least one of the following: a prediction period, a second offset, and a number F of prediction moments.

[0127] Exemplarily, the network device configures Set A for the terminal using RRC signaling, where Set A includes P (P ≥ 1) second downlink reference signal subsets, each of which may correspond to one cell. As an example, the pth (1 ≤ p ≤ P) second downlink reference signal subset in Set A may be recorded as Set A p The downlink reference signal (or downlink reference signal resource) in Set A may include, for example, CSI-RS (or CSI-RS resource) and / or SSB (or SSB resource) for mobile. The network device may also use RRC signaling to configure the measurement period, offset, and number of measurement moments F of Set A so that the terminal can know at which moments to perform prediction. p For example, the Set A corresponding to the prediction time f (1≤f≤F) p It can be recorded as Set A p,f , for example, Set A corresponding to prediction time 1 p It can be recorded as Set A p,1 , Set A p,1 The index is {p,1}, and the Set A corresponding to the prediction time 2 p It can be recorded as Set A p,2 , Set A p,2 The index of is {p,2}, and so on.

[0128] In some implementations, the first configuration information and / or the second configuration information are related to capability information of the terminal. The network device configures the first downlink reference signal set and / or the second downlink reference signal set for the terminal based on the capability information of the terminal.

[0129] Terminal capability reporting

[0130] Different terminals have different measurement and / or prediction capabilities, so the terminals need to report their measurement and / or prediction capabilities.

[0131] In some implementations, the terminal sends the terminal's capability information to the network device, and the network device receives the terminal's capability information sent by the terminal. The terminal's capability information includes at least one of the following:

[0132] First information, the first information is used to indicate whether the terminal supports measurement and / or prediction of time domain mobility;

[0133] The second information is used to indicate at least one of the following: a maximum number of cells supported for measurement by the terminal on all downlink carrier components (CCs) or bandwidth parts (BWPs); a maximum number of configured first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; and a maximum number of first downlink reference signal subsets supported for measurement by the terminal on all downlink CCs or BWPs.

[0134] The third information is used to indicate at least one of the following: a maximum number of predicted cells supported by the terminal on all downlink CCs or BWPs; a maximum number of configured second downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; and a maximum number of predicted first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs.

[0135] The fourth information is used to indicate at least one of the following: the maximum number of cells supported for measurement by the terminal on each downlink CC or BWP; the maximum number of configured first downlink reference signal subsets supported by the terminal on each downlink CC or BWP; the maximum number of first downlink reference signal subsets supported for measurement by the terminal on each downlink CC or BWP;

[0136] The fifth information is used to indicate at least one of the following: the maximum number of predicted cells supported by the terminal on each downlink CC or BWP; the maximum number of configured second downlink reference signal subsets supported by the terminal on each downlink CC or BWP; the maximum number of predicted second downlink reference signal subsets supported by the terminal on each downlink CC or BWP;

[0137] Sixth information, where the sixth information is used to indicate a maximum number of downlink reference signals supported for measurement by the terminal in each first downlink reference signal subset;

[0138] Seventh information, where the seventh information is used to indicate a maximum number of predicted downlink reference signals supported by the terminal in each second downlink reference signal subset;

[0139] The eighth information is used to indicate the number of measurement moments supported by the terminal;

[0140] Ninth information: The ninth information is used to indicate the number of prediction moments supported by the terminal.

[0141] The above-mentioned first information indication capability can be recorded as capability 1. Similarly, the second information indication capability can be recorded as capability 2, the third information indication capability can be recorded as capability 3, the fourth information indication capability can be recorded as capability 4, the fifth information indication capability can be recorded as capability 5, the sixth information indication capability can be recorded as capability 6, the seventh information indication capability can be recorded as capability 7, the eighth information indication capability can be recorded as capability 8, and the ninth information indication capability can be recorded as capability 9.

[0142] For example, capability 1 can be understood as whether the terminal supports measurement and / or prediction of time domain mobility of the terminal side model. Alternatively, capability 1 can be understood as whether the terminal supports measurement and / or prediction of time domain mobility of the network side model.

[0143] Exemplarily, capability 2 can be understood as the maximum number of cell measurements supported by the terminal on all downlink CCs or BWPs, or the maximum number of first downlink reference signal subsets that the terminal supports being configured on all downlink CCs or BWPs, or the maximum number of first downlink reference signal subsets that can be measured simultaneously on all downlink CCs or BWPs.

[0144] Exemplarily, capability 3 can be understood as the maximum number of cell predictions supported by the terminal on all downlink CCs or BWPs, or the maximum number of second downlink reference signal subsets that the terminal supports being configured on all downlink CCs or BWPs, or the maximum number of second downlink reference signal subsets that can be predicted simultaneously on all downlink CCs or BWPs.

[0145] Exemplarily, capability 4 can be understood as the maximum number of cell measurements supported by the terminal on each downlink CC or BWP, or the maximum number of first downlink reference signal subsets supported by the terminal on each downlink CC or BWP, or the maximum number of first downlink reference signal subsets that can be measured simultaneously on each downlink CC or BWP.

[0146] Exemplarily, capability 5 can be understood as the maximum number of cell predictions supported by the terminal on each downlink CC or BWP, or the maximum number of second downlink reference signal subsets that the terminal supports being configured on each downlink CC or BWP, or the maximum number of second downlink reference signal subsets that can be predicted simultaneously on each downlink CC or BWP.

[0147] Illustratively, capability 6 may understand how many downlink reference signals the terminal supports measuring at most in each first downlink reference signal subset.

[0148] Exemplarily, capability 7 may understand how many downlink reference signals the terminal supports predicting at most in each second downlink reference signal subset.

[0149] For example, capability 8 may understand how many measurement moments the terminal supports, that is, the value of K.

[0150] For example, capability 9 may be used to understand how many prediction moments the terminal supports, that is, the value of F.

[0151] In some embodiments, the capability information of the terminal includes the above-mentioned first information. Optionally, when the first information indicates that the terminal supports measurement and / or prediction of time domain mobility, the capability information of the terminal may include at least one of the above-mentioned second to ninth information.

[0152] In some implementations, the capability information of the terminal includes at least one of the first to ninth information described above.

[0153] Time domain mobility measurements

[0154] At each of the K measurement moments, the terminal measures the downlink reference signals (or downlink reference signal resources) in the first downlink reference signal set, such as SSB (or SSB resources) and / or CSI-RS (or CSI-RS resources for mobile) through the physical layer. The nth (1≤n≤N) first downlink reference signal subset measured at the kth (1≤k≤K) measurement moment may be denoted as Set B. n,k / Set C n,k , Set B n,k / Set C n,k The index is {n, k}, where n represents the first downlink reference signal subset index and k represents the measurement time index. N first downlink reference signal subsets may correspond to N cells, and each first downlink reference signal subset may correspond to a cell. Thus, the first downlink reference signal subset index may correspond to a cell index. Exemplarily, the cell index may be a physical cell identifier (PCI), a PCI index, or a cell configuration index. In other words, the first downlink reference signal subset index, PCI, PCI index, and cell configuration index may be interchangeable.

[0155] It should be noted that the N cells may include one current serving cell (eg, SpCell) and (N-1) candidate cells. The current serving cell may also be understood as a candidate cell. Based on this understanding, the N cells may be described as N candidate cells.

[0156] Model inputs and outputs

[0157] The first model is used for mobility prediction, and the first model can be a neural network model, such as the above-mentioned DNN model, or an LSTM model. The technical solution of the embodiment of the present application can use a DNN model for mobility prediction, or, considering the time domain factors, an LSTM model can also be used for mobility prediction. Although the technical solutions of the examples of the present application are all illustrated using the DNN model as an example, they are not limited to this. Other models that can realize time domain mobility prediction (such as the LSTM model) are applicable to the technical solutions of the embodiments of the present application.

[0158] 1) Model Input

[0159] The input of the first model includes K measurement instances. The input of K measurement instances can be in the following two ways:

[0160] The first method: K measurement instances include K×N groups of link qualities.

[0161] The second method: K measurement instances include: K×N indexes and K×N groups of link qualities, and the K×N indexes and K×N link qualities have a corresponding relationship; each index in the K×N indexes corresponds to a measurement time index and a cell index.

[0162] Here, for a measurement instance, the terminal measures N downlink reference signal subsets in the first downlink reference signal set to obtain N groups of link qualities, where each downlink reference signal subset corresponds to a group of link qualities. Assuming that a downlink reference signal subset includes M (M ≥ 1) downlink reference signals, then each group of link qualities includes M link qualities. For K measurement instances, K×N groups of link qualities can be obtained, where each group of link qualities includes M link qualities. For example, the terminal measures all Set B n,k / Set C n,k (1≤n≤N, 1≤k≤K), and obtain K×N groups of link quality, where the nth (1≤n≤N) first downlink reference signal subset measured at the kth (1≤k≤K) measurement time is recorded as Set B n,k / Set C n,k , Set B n,k / Set C n,k The index is {n, k}, where n represents the first downlink reference signal subset index and k represents the measurement time index.

[0163] Here, each link quality corresponds to a downlink reference signal resource index (such as CRI or SSBRI), a first downlink reference signal subset index (such as n index), and a measurement time index (such as k index). The first downlink reference signal subset index can also be replaced by a cell index (such as PCI, PCI index, cell configuration index, etc.). In other words, the first downlink reference signal subset index and the cell index (such as PCI, PCI index, cell configuration index, etc.) can be replaced with each other.

[0164] In the first method described above, as shown in Case 1 in FIG. 13 to FIG. 15 , K×N sets of link qualities (i.e., all Set B n,k / Set C n,k The order is related to the index corresponding to the link quality, which includes: a downlink reference signal resource index, a first downlink reference signal subset index, and a measurement time index. In the first approach described above, since the index corresponding to the link quality does not need to be input into the first model, the amount of model input data can be reduced.

[0165] In the second approach described above, as shown in Case 2 in FIG. 13 to FIG. 15 , the input of the first model includes K×N groups of link qualities corresponding to K×N indexes (ie, all Set B n,k / Set C n,k Each index in the K×N indexes corresponds to a measurement time index (such as k index) and a cell index (such as PCI, PCI index, cell configuration index, etc.), wherein the cell index can also be replaced by the first downlink reference signal subset index (such as n index). For the second method mentioned above, since the index corresponding to the link quality is input to the first model, different Set B can be flexibly selected. n,k / Set C n,k The combination serves as the input to the model.

[0166] 1) Model output

[0167] The output of the first model includes F prediction instances. In some embodiments, each of the F prediction instances includes at least one of the following: the indexes of the best L cells among the P cells; the indexes of the best J downlink reference signal resources among each of the best cells; and the link quality corresponding to each of the best downlink reference signal resource indexes. In some embodiments, each prediction instance also includes the index of the predicted time instant corresponding to the prediction instance.

[0168] Exemplarily, each prediction instance includes a prediction time index, and the best L (L≥1) cell indices among P (P≥1) cells, and / or the best J (J≥1) beam (pair) indices in each optimal area, and / or the link quality corresponding to each optimal beam (pair) index.

[0169] The output of F prediction instances can be in the following two ways:

[0170] Method 1: The first model includes two models, Model 1 and Model 2. The output of Model 1 includes the optimal L cell indices among the P cells corresponding to each prediction instance in the F prediction instances, and the optimal J downlink reference signal resource indices in each optimal cell. The output of Model 2 includes the link quality corresponding to each optimal beam (pair) index corresponding to each prediction instance in the F prediction instances.

[0171] Second approach: The first model is a single model. The output of this model includes the best L cell indices among the P cells corresponding to each prediction instance in the F prediction instances, the best J downlink reference signal resource indices in each best cell, and the link quality corresponding to each best beam (pair) index.

[0172] In the first approach described above, as shown in Figure 13 , Model 1 infers the optimal L cell indices and the optimal J downlink reference signal resource indices for all predicted times f (1≤f≤F) . As shown in Figure 14 , Model 2 infers the link quality corresponding to each optimal beam (pair) index in each optimal cell at predicted time f (1≤f≤F).

[0173] In the second approach, as shown in Figure 15, the model predicts all of Set A p,f (1≤p≤P, 1≤f≤F). For each of the F prediction instances, select the best L cell indices, the best J downlink reference signal resource indices in each best cell, and the link quality corresponding to each best beam (pair) index, sorted by link quality from high to low.

[0174] In the embodiment of the present application, the first model responsible for mobility prediction can be deployed on the terminal side or on the network device side. The technical solutions of the embodiment of the present application are respectively described below in combination with these two deployment methods.

[0175] The model is located on the terminal side

[0176] In some implementations, the first model in the above solution is located at the terminal side. Here, after the terminal obtains K measurement instances, it predicts F prediction instances based on the local first model.

[0177] In some implementations, the terminal sends F prediction instances to the network device.

[0178] In some embodiments, the reporting manner of the F prediction instances includes at least one of the following: periodic reporting, semi-persistent reporting, aperiodic reporting, and layer 1 and / or layer 2 based mobility event reporting.

[0179] 1) Periodic / semi-continuous / non-periodic reporting

[0180] In some embodiments, for periodic reporting or semi-continuous reporting, before the terminal sends F prediction instances to the network device, the network device sends third configuration information to the terminal, and the terminal receives the third configuration information sent by the network device. The third configuration information is used to configure the reporting period and reporting offset corresponding to the periodic reporting or semi-continuous reporting.

[0181] In some implementations, the third configuration information is carried in RRC signaling.

[0182] Here, the terminal periodically or semi-continuously reports the prediction instances corresponding to the F prediction moments based on the reporting period and reporting offset configured by the network device and the number of prediction moments F. For periodic reporting, once the terminal obtains the measurement configuration (such as the first configuration information and / or the second configuration information) and reporting configuration (such as the third configuration information) sent by the network device, it will periodically report the prediction instances. For semi-continuous reporting, after obtaining the measurement configuration and reporting configuration, the terminal also needs to obtain an instruction to activate the configuration. After obtaining the instruction to activate the configuration, the terminal periodically reports the prediction instances.

[0183] In some implementations, for aperiodic reporting, before the terminal sends F prediction instances to the network device, the terminal receives first downlink control information (DCI) sent by the network device, where the first DCI is used to trigger the aperiodic reporting.

[0184] Here, for non-periodic reporting, after obtaining the measurement configuration (such as the first configuration information and / or the second configuration information mentioned above) and the reporting configuration (such as the third configuration information mentioned above), the terminal also needs to obtain an instruction to trigger measurement and reporting. The network device can trigger the terminal to perform measurement and reporting through DCI (i.e., trigger instruction). After obtaining the trigger instruction, the terminal measures K measurement instances and reports F prediction instances.

[0185] 2) Mobility event reporting based on layer 1 and / or layer 2

[0186] In some implementations, before the terminal sends the F prediction instances to the network device, the terminal determines that a layer 1 and / or layer 2 mobility event is triggered and sends a reporting request to the network device; the terminal receives a second DCI sent by the network device, where the second DCI is used to schedule a physical uplink shared channel (PUSCH) for reporting. Further, the terminal uses the PUSCH to send the F prediction instances.

[0187] Exemplarily, at some or all of the prediction moments f (1≤f≤F), the output of the first model on the terminal side triggers a mobility event based on layer 1 and / or layer 2, and the terminal sends a reporting request to the network device, where the reporting request is used to request resources for mobility reporting; the network device agrees to the terminal's reporting request, sends a DCI, and schedules a PUSCH through the DCI; the terminal carries the mobility-related reporting content, i.e., the prediction instance, through the PUSCH scheduled by the DCI.

[0188] In some embodiments, the above-mentioned layer 1 and / or layer 2 mobility events include at least one of the following:

[0189] The first event is: at some or all of the F predicted moments, the predicted link performance of the current serving cell is better than the first threshold or weaker than the second threshold;

[0190] The second event is: at some or all of the F prediction moments, the predicted link performance of the current serving cell is better or weaker than the predicted link performance of the candidate cell plus an offset;

[0191] The third event is: at some or all of the F predicted moments, the predicted link performance of the candidate cell is better than the third threshold or weaker than the fourth threshold;

[0192] The fourth event is: at some or all of the F prediction moments, the predicted link performance of the current serving cell is weaker than the fifth threshold, and the predicted link performance of the candidate cell is better than the sixth threshold.

[0193] In the above solution, the current cell is one of the P cells, and the candidate cell is a cell other than the current cell in the P cells. The current cell can also be called the current serving cell, and the current cell can also be understood as a candidate cell in the mobility management process.

[0194] The terminal compares the above-defined layer 1 and / or layer 2 mobility events and the prediction results of the first model to determine whether the layer 1 and / or layer 2 mobility events occur (or whether they are triggered), and thus autonomously chooses whether to report F prediction instances.

[0195] For example, at a certain prediction time f, when the optimal cell output by the first model is still the current serving cell of the terminal, the terminal does not need to report the prediction instance, nor does it need to trigger subsequent mobility operations.

[0196] For example, at a certain prediction time f, when the optimal cell output by the first model is a candidate cell and its performance is better than the current serving cell by an offset (such as 6dB), the terminal can determine that the second event has occurred, triggering the terminal to make a reporting request. The reporting request can be carried by uplink control information (UCI), and the network device schedules PUSCH for the reporting request through DCI, and the terminal uses the PUSCH to send the prediction instance.

[0197] Exemplarily, within all prediction moments f, when the optimal cell output by the first model is a candidate cell and its performance is better than a specific threshold (such as L1-RSRP is higher than -50dBm), the terminal can determine that the third event has occurred, triggering the terminal to make a reporting request. The reporting request can be carried by UCI, and the network device schedules PUSCH for the reporting request through DCI, and the terminal uses the PUSCH to send the prediction instance.

[0198] In the embodiment of the present application, for the prediction results of the first model (i.e., F prediction instances), each prediction instance contains the prediction results from all Set A. p,f (1≤p≤P, 1≤f≤F), and / or the optimal cell index selected from the optimal cell (beam (pair) index, and / or the link quality corresponding to the optimal cell (beam (pair) index), the terminal reports the prediction result to the network device in one or more reports. Among them, for the optimal cell index, the terminal can report the cell index of the optimal cell or the Set A corresponding to the optimal cell. p，f The cell index may be a PCI, a PCI index, or a configuration index of a candidate cell. For the optimal beam (pair) index of the optimal cell, the downlink reference signal resource index in the NR system may be used, such as SSBRI or CRI. For link quality, L1-RSRP is used in some examples, but other link quality indicators such as L1-SINR, L1-RSSI, L1-RSRQ, or CQI are not excluded.

[0199] In order to carry out time domain prediction reporting of mobility, the technical solution of the embodiment of the present application proposes the following reporting format:

[0200] A) In some embodiments, the reporting granularity of F prediction instances is one prediction instance.

[0201] In some embodiments, the above-mentioned report has a first format, and the first format includes: a predicted time index, L cell indexes, X1 downlink reference signal resource indexes, and X1 link qualities; wherein the L cell indexes are the best L cell indexes among the P cells; the X1 downlink reference signal resource indexes include the best J downlink reference signal resource indexes in each of the L cells; the X1 link qualities have a corresponding relationship with the X1 downlink reference signal resource indexes.

[0202] Here, the cell index may be a PCI, a PCI index, a configuration index of a candidate cell, or an index of Set A (ie, a p index).

[0203] Here, the downlink reference signal resource index may also be replaced by a beam (pair) index, a downlink reference signal index, or a spatial filter index.

[0204] Here, the X1 link qualities can be reported in a differential manner, or in a non-differential manner. Wherein, in the case of differential reporting, the X1 link qualities are represented by a reference link quality and X1-1 differential values. For example, the reference link quality can be the best link quality among the X1 link qualities, a differential value and a reference link quality can determine a link quality, and X1-1 differential values ​​and a reference link quality can determine X1-1 link quality. Furthermore, the above-mentioned reporting format may also include a downlink reference signal resource index corresponding to the reference link quality. In the case of non-differential reporting, the X1 link qualities are directly represented by an X1 link quality.

[0205] As an example, the reporting format used in the prediction example is given below. It should be noted that the downlink reference signal resource index (CRI or SSBR) or beam (pair) index can also be replaced by a downlink reference signal index or a spatial filter index.

[0206] Table 1 below shows the reporting format for the prediction instance corresponding to the f-th prediction moment. The x1 link qualities are reported differentially. In Table 1, the reporting format includes the prediction moment index, i.e., the f-value. Furthermore, for the L optimal cell indices at the prediction moment, their PCI, PCI index, candidate cell configuration index, or corresponding Set A index (i.e., p-value) can be directly reported. For the optimal beam (pair) index, if it is for a downlink transmit beam, it can be represented by a conventional CRI or SSBRI. If it is for a downlink transmit / receive beam pair, it can be represented by a beam (pair) index.

[0207] Table 1: Mobility prediction report at the fth prediction moment

[0208] B) In some embodiments, the reporting granularity of F prediction instances is F prediction instances.

[0209] Here, in order to save the number of reports, the terminal may report F prediction instances to the network device in one report.

[0210] In some embodiments, the above-mentioned report has a second format, and the second format includes F groups of information, and the F groups of information have a corresponding relationship with the F prediction instances; each group of information in the F groups of information includes: a prediction time index, L cell indexes, X1 downlink reference signal resource indexes, and X1 link qualities; or, each group of information in the F groups of information includes: L cell indexes, X1 downlink reference signal resource indexes, and X1 link qualities; wherein the L cell indexes are the optimal L cell indexes among the P cells; the X1 downlink reference signal resource indexes include the optimal J downlink reference signal resource indexes in each cell among the L cells; and the X1 link quality has a corresponding relationship with the X1 downlink reference signal resource index.

[0211] Here, the cell index may be a PCI, a PCI index, a configuration index of a candidate cell, or an index of Set A (ie, a p index).

[0212] Here, the downlink reference signal resource index may also be replaced by a beam (pair) index, a downlink reference signal index, or a spatial filter index.

[0213] Here, the X1 link qualities can be reported in a differential manner, or in a non-differential manner. Wherein, in the case of differential reporting, the X1 link qualities are represented by a reference link quality and X1-1 differential values. For example, the reference link quality can be the best link quality among the X1 link qualities, a differential value and a reference link quality can determine a link quality, and X1-1 differential values ​​and a reference link quality can determine X1-1 link quality. Furthermore, the above-mentioned reporting format may also include a downlink reference signal resource index corresponding to the reference link quality. In the case of non-differential reporting, the X1 link qualities are directly represented by an X1 link quality.

[0214] As an example, the reporting format used in the prediction example is given below. It should be noted that the downlink reference signal resource index (CRI or SSBR) or beam (pair) index can also be replaced by a downlink reference signal index or a spatial filter index.

[0215] The following Table 2 shows the reporting format for the prediction instances corresponding to the F prediction moments, and the link quality is reported in a differential manner. The prediction instances corresponding to the F prediction moments can be reported with or without the [prediction moment index], which does not affect the network device's understanding of the prediction results. In Table 2, the reporting format includes F groups of information, each group of information representing a prediction instance. For each prediction instance, its content may include the [prediction moment index] (i.e., the f value), or it may not include the [prediction moment index]. The content of each prediction instance is similar to that in Table 1 above.

[0216] Table 2: Reporting of mobility predictions at F prediction moments

[0217] The model is located on the network device side

[0218] In some implementations, the first model in the above solution is located on the network device. Here, after the terminal obtains K measurement instances, the terminal sends the K measurement instances to the network device, and the network device receives the K measurement instances sent by the terminal. The network device predicts F predicted instances based on the local first model.

[0219] In some implementations, the reporting manner of the K measurement instances includes at least one of the following: periodic reporting, semi-persistent reporting, and aperiodic reporting.

[0220] 1) Periodic / semi-continuous reporting

[0221] In some embodiments, for periodic reporting or semi-continuous reporting, before the terminal sends K measurement instances to the network device, the network device sends fourth configuration information to the terminal, and the terminal receives the fourth configuration information sent by the network device. The fourth configuration information is used to configure the reporting period and reporting offset corresponding to the periodic reporting or semi-continuous reporting.

[0222] In some implementations, the fourth configuration information is carried in RRC signaling.

[0223] Here, the terminal periodically or semi-continuously reports the measurement instances corresponding to K measurement moments based on the reporting period and reporting offset configured by the network device and the number of measurement moments K. For periodic reporting, once the terminal obtains the measurement configuration (such as the first configuration information and / or the second configuration information) and reporting configuration (such as the fourth configuration information) sent by the network device, it will periodically report the measurement instance. For semi-continuous reporting, after obtaining the measurement configuration and reporting configuration, the terminal also needs to obtain an instruction to activate the configuration. After obtaining the instruction to activate the configuration, the terminal periodically reports the measurement instance.

[0224] 2) Non-periodic reporting

[0225] In some implementations, for aperiodic reporting, before the terminal sends K measurement instances to the network device, the network device sends a third DCI to the terminal, and the terminal receives the third DCI sent by the network device, where the third DCI is used to trigger aperiodic reporting.

[0226] Here, for non-periodic reporting, after obtaining the measurement configuration (such as the first configuration information and / or the second configuration information mentioned above) and the reporting configuration (such as the fourth configuration information mentioned above), the terminal also needs to obtain an instruction to trigger measurement and reporting. The network device can trigger the terminal to perform measurement and reporting through DCI (i.e., trigger instruction). After obtaining the trigger instruction, the terminal performs measurement and reporting of K measurement instances.

[0227] In the embodiment of the present application, for the measurement results of the first model (ie, K measurement instances), each measurement instance contains the values ​​from all Set B. n,k / Set C n,k The terminal reports the link quality measured in (1≤n≤N, 1≤k≤K) to the network device in one or more reports. In some examples, L1-RSRP is used as an example of link quality, but other link quality indicators such as L1-SINR, L1-RSSI, L1-RSRQ, or CQI are not excluded.

[0228] In order to carry out time domain measurement reporting of mobility, the technical solution of the embodiment of the present application proposes the following reporting format:

[0229] I) In some implementations, the reporting granularity of K measurement instances is one measurement instance.

[0230] Here, for K measurement instances, the network device may configure the terminal to report after each measurement. This reporting method reduces the load of each report, but requires K reports.

[0231] In some embodiments, the report has a third format, which includes a measurement time index and X2 link qualities, where the X2 link qualities include link qualities of M downlink reference signals for each of N cells, where M is a positive integer. Here, the report does not include the N cell indexes.

[0232] In some embodiments, the report has a fourth format, which includes: a measurement time index, N cell indices, and X2 link qualities; wherein the X2 link qualities include link qualities of M downlink reference signals of each of the N cells, where M is a positive integer. Here, the report includes N cell indices.

[0233] The format of reporting the X2 link qualities adopts a differential reporting format or a non-differential reporting format.

[0234] As an implementation manner, in a non-differential reporting format, the X2 link qualities are represented by X2 link qualities, as shown in Table 3 below.

[0235] As another implementation, in a differential reporting format, the X2 link qualities are divided into N groups of link qualities, each group of link qualities being represented by a reference link quality and M-1 differential values, as shown in Tables 5 and 9 below. Exemplarily, the reference link quality may be the best link quality among the M link qualities, a differential value and a reference link quality may determine one link quality, and M-1 differential values ​​and a reference link quality may determine M-1 link qualities. Furthermore, the above reporting format may also include a downlink reference signal resource index corresponding to the reference link quality.

[0236] As another implementation, in a differential reporting format, X2 link qualities are represented by a reference link quality and X2-1 differential values, as shown in Table 7 below. Exemplarily, the reference link quality may be the best link quality among the X2 link qualities, a differential value and a reference link quality may determine a single link quality, and X2-1 differential values ​​and a reference link quality may determine X2-1 link qualities. Furthermore, the above reporting format may also include a downlink reference signal resource index corresponding to the reference link quality.

[0237] II) In some embodiments, the reporting granularity of K measurement instances is K measurement instances.

[0238] Here, the network device may configure the terminal to report K measurement instances together after K measurements. This reporting method causes a larger load for one report, but only one report is required.

[0239] In some embodiments, the report has a fifth format, which includes: K groups of information, each of which corresponds to K measurement instances; each of the K groups of information includes: X2 link qualities; the X2 link qualities include link qualities of M downlink reference signals for each of N cells, where M is a positive integer. The report does not include the N cell indexes.

[0240] In some implementations, the report has a sixth format, which includes N cell indexes and K groups of information, or includes N cell indexes, K measurement instance indexes, and K groups of information; the K groups of information correspond to the K measurement instance indexes; each of the K groups of information includes X2 link qualities; the X2 link qualities include link qualities of M downlink reference signals for each of the N cells, where M is a positive integer. Here, the report includes N cell indexes and / or K measurement instance indexes.

[0241] The format of reporting the X2 link qualities adopts a differential reporting format or a non-differential reporting format.

[0242] As an implementation manner, in a non-differential reporting format, the X2 link qualities are represented by X2 link qualities, as shown in Table 4 below.

[0243] As another implementation, in a differential reporting format, K×X2 link qualities are divided into K×N groups of link qualities, each group of link qualities being represented by a reference link quality and M-1 differential values, as shown in Tables 6 and 10 below. Exemplarily, the reference link quality may be the best link quality among the M link qualities, a differential value and a reference link quality may determine one link quality, and M-1 differential values ​​and a reference link quality may determine M-1 link qualities. Furthermore, the above reporting format may also include a downlink reference signal resource index corresponding to the reference link quality.

[0244] As another implementation, in a differential reporting format, K×X2 link qualities are represented by a reference link quality and K×X2-1 differential values, as shown in Table 8 below. For example, the reference link quality may be the best link quality among the K×X2 link qualities, a differential value and a reference link quality may determine a link quality, and K×X2-1 differential values ​​and a reference link quality may determine K×X2-1 link qualities. Furthermore, the above reporting format may also include a downlink reference signal resource index corresponding to the reference link quality.

[0245] III) In some implementations, the reporting granularity of the K measurement instances is the measurement result of one cell in one measurement instance.

[0246] In some embodiments, the above-mentioned report has a sixth format, which includes: a measurement time index, a cell index, and M link qualities; wherein the M link qualities include the link qualities of M downlink reference signals of the cell corresponding to the cell index, and M is a positive integer.

[0247] The format of reporting the M link qualities is a differential reporting format or a non-differential reporting format.

[0248] As an implementation manner, in a non-differential reporting format, the M link qualities are represented by M link qualities.

[0249] As another implementation, in a differential reporting format, M link qualities are represented by a reference link quality and M-1 differential values, as shown in Table 11 below. Exemplarily, the reference link quality may be the best link quality among the M link qualities, a differential value and a reference link quality may determine one link quality, and M-1 differential values ​​and a reference link quality may determine M-1 link qualities. Furthermore, the above reporting format may also include a downlink reference signal resource index corresponding to the reference link quality.

[0250] As an example, the reporting format used in the measurement example is given below. It should be noted that the downlink reference signal resource index (CRI or SSBR) or beam (pair) index can also be replaced by a downlink reference signal index or a spatial filter index.

[0251] The following Table 3 shows the reporting format of the measurement instance corresponding to the k-th measurement moment, and the link quality is reported in a non-differential manner. In Table 3, the reporting format includes the measurement moment index (i.e., k value) and N×M link qualities. In the k-th measurement instance, the link quality can be reported from small to large according to the corresponding downlink reference signal resource index (i.e., SSBRI or CRI or beam (pair) index), so the downlink reference signal resource index can be omitted. Furthermore, the downlink reference signal resource index can first be reported from small to large according to the n index (i.e., cell index), and then reported from small to large according to the k index (i.e., measurement moment index).

[0252] Table 3: Measurement result reporting for the kth measurement instance (without L1-RSRP difference)

[0253] In the following Table 4, the reporting format of the measurement instances corresponding to K measurement moments (i.e., all K measurement instances) is given, and the link quality is reported in a non-differential manner. In Table 4, each measurement instance needs to include the link quality of N cells, and the link quality of each cell includes the link quality of M downlink reference signals. Therefore, the terminal needs to report a total of K×N×M link qualities. In Table 4, the measurement moment index may not be included. In this case, the measurement instances corresponding to each measurement moment need to be arranged in sequence in the reporting format; or the measurement moment index may be included. In this case, the measurement instances corresponding to each measurement moment can be flexibly arranged in the reporting format.

[0254] Table 4: Measurement result reporting for all K measurement instances (without L1-RSRP differential)

[0255] Table 5 below shows the reporting format for the measurement instance corresponding to the kth measurement moment. Link quality is reported differentially. Table 5 differs from Table 3 in that link quality is reported differentially. For each cell's M link qualities, the highest link quality among the M link qualities is used as the reference link quality, and the downlink reference signal resource index (such as SSBRI or CRI) corresponding to the reference link quality is marked. Other link qualities are differentially reported in a fixed order of downlink reference signal resource index resources (e.g., from small to large). Differential reporting here refers to reporting only the difference between the other link qualities and the reference link quality.

[0256] Table 5: Measurement result report for the kth measurement instance (with L1-RSRP difference)

[0257] Table 6 below shows the reporting format for the measurement instances corresponding to K measurement moments (i.e., all K measurement instances). Link quality is reported using a differential method. Table 6 differs from Table 5 in that it includes the contents of the K measurement instances, while the contents of each measurement instance are similar to those in Table 5. Furthermore, Table 6 may not include the measurement moment index, in which case the measurement instances corresponding to each measurement moment need to be arranged sequentially in the reporting format. Alternatively, it may include the measurement moment index, in which case the measurement instances corresponding to each measurement moment can be flexibly arranged in the reporting format.

[0258] Table 6: Measurement result reporting for all K measurement instances (with L1-RSRP differential)

[0259] Table 7 below shows the reporting format for the measurement instance corresponding to the kth measurement time. Link quality is reported using a differential method. Table 7 differs from Table 5 in that the highest link quality among all cell link qualities is selected as the reference link quality, and the downlink reference signal resource index (such as SSBRI or CRI) corresponding to this reference link quality is marked. In addition, the cell index corresponding to this reference link quality is also marked. Other link qualities are differentially reported in a fixed order of downlink reference signal resource indexes (e.g., from smallest to largest). Differential reporting here refers to reporting only the difference between the other link qualities and the reference link quality.

[0260] Table 7: Measurement result report for the kth measurement instance (with L1-RSRP difference)

[0261] Table 8 below shows the reporting format for the measurement instances corresponding to K measurement moments (i.e., all K measurement instances). Link quality is reported using a differential method. Table 8 differs from Table 7 in that it includes the content of K measurement instances, while the content of each measurement instance is similar to that of Table 7. Furthermore, Table 8 must include the measurement moment index (i.e., the k value) corresponding to the reference link quality.

[0262] Table 8: Measurement result reporting for all K measurement instances (with L1-RSRP differential)

[0263] Table 9 below shows the reporting format for the measurement instance corresponding to the kth measurement time. Link quality is reported using a differential method. Table 9 differs from Table 5 in that it includes N cell indices, while the quality information is similar to that in Table 5. Table 9 does not include the measurement time index, although it can include it. The reporting format shown in Table 9 allows the terminal to flexibly report partial or complete measurement results. Specifically, the terminal can report a subset of cell indices and the link quality corresponding to each cell in that subset.

[0264] Table 9: Measurement result report for the kth measurement instance (with L1-RSRP difference)

[0265] Table 10 below shows the reporting format for measurement instances corresponding to K measurement moments (i.e., all K measurement instances). Link quality is reported using a differential method. Table 10 differs from Table 9 in that it includes K measurement instances, each with similar content to Table 9. Furthermore, Table 10 also includes a measurement moment index. In this case, the measurement instances corresponding to each measurement moment can be flexibly arranged in the reporting format. Of course, Table 10 can also exclude the measurement moment index. In this case, the measurement instances corresponding to each measurement moment are arranged sequentially in the reporting format.

[0266] Table 10: Measurement result reporting for all K measurement instances (with L1-RSRP differential)

[0267] The following Table 11 shows the reporting format of the measurement results of a cell in a measurement instance corresponding to one measurement moment, and the link quality is reported in a differential manner. The reporting format includes the cell index, the measurement moment index, and the corresponding link quality, and the M link qualities are reported in a differential manner. Using the reporting format shown in Table 11, the terminal can only report the measurement result of one first downlink reference signal subset in each report, and the network device needs to collect multiple reports from the terminal as input to the first model. Of course, considering the flexibility of the model on the network device side, the network device side does not necessarily need to wait until the measurement results of all first downlink reference signal subsets are collected before making a prediction.

[0268] Table 11: Measurement results reported for a single downlink reference signal subset

[0269] In some embodiments, after the network device obtains F prediction instances, the network device sends a cell switching command (Cell Switch Command, CSC) to the terminal, and the terminal receives the cell switching command sent by the network device. The cell switching command is used to instruct the terminal to switch to the target cell, and the target cell is determined based on some or all of the F prediction instances.

[0270] Here, the F prediction instances obtained by the network device can be the F prediction instances sent by the terminal to the network device in the above-mentioned scheme (refer to the scheme in which the above-mentioned model is located on the terminal side), or they can be the F prediction instances predicted by the network device based on the K measurement instances obtained (refer to the scheme in which the above-mentioned model is located on the network device side).

[0271] Exemplarily, the network device may use a cell handover command based on a unified TCI state, which is carried by a MAC CE. The unified TCI state may be a downlink / joint TCI state (DL / joint TCI state), which includes a cell index (such as PCI, PCI index, cell configuration index, etc.), which may indicate the target cell to which the terminal is to be handed over.

[0272] In some implementations, after the terminal obtains K measurement instances and predicts F prediction instances, the terminal sends a cell switch request (Cell Switch reQuest, CSQ) to the network device. The cell switch request carries a target prediction instance, and the target prediction instance is determined based on the F prediction instances.

[0273] Here, after the terminal obtains K measurement instances and predicts F prediction instances, it may not report the F prediction instances, but instead send a cell handover request to the network device based on the F prediction instances. The cell handover request is carried by the MAC CE. Exemplarily, the cell handover request may include at least one of the following information: a prediction time index (i.e., f value); a cell index of the target cell, such as PCI, PCI index, candidate cell configuration index, or p index; a downlink reference signal resource index corresponding to the target cell, such as CRI / SSBRI or beam (pair) index; and a link quality corresponding to the downlink reference signal resource index, such as L1-RSRP. After receiving the cell handover request, the network device only needs to send a confirmation command to the terminal, and the terminal can complete the subsequent handover operation.

[0274] It should be noted that for beam-level mobility, the result of the optimal beam predicted by the model is evaluated according to the link quality corresponding to the beam, that is, the highest L1-RSRP. For cell mobility, for example, a single beam of a cell is optimal (the quality of other beams is poor), which does not guarantee that the cell is the optimal cell for the UE. A common method for selecting the optimal cell is to perform a weighted average of the link qualities of multiple beams in the cell to obtain the optimal cell (ranked from high to low by the mean L1-RSRP). In the technical solution of the embodiment of the present application, the model used to predict the optimal cell also refers to the selection mechanism of the optimal cell, namely cell-level mobility.

[0275] The technical solution of the embodiment of the present application proposes a model-based mobility prediction technology, which uses the measurement results of multiple cells at multiple historical measurement times as the input of the model, and predicts (outputs) the optimal cell at multiple future prediction times, and / or the optimal beam (pair) under the cell, and / or the link quality corresponding to the beam (pair), thereby realizing mobility management from passive to active and reducing the overhead and delay of downlink mobility measurement.

[0276] The technology of the embodiments of the present application is illustrated below with reference to specific application examples. In the following application examples, the terminal is referred to as UE and the network device is referred to as NW.

[0277] It should be noted that the model input set can be either measurement set B or generated from measurement set C. In other words, Set B itself can be a measurement set, or it can be generated from measurement set C. That is, Set B is the optimal multiple beams (pairs) and their link qualities measured in Set C. In the following application examples, Set B can be a measurement set or generated from measurement set C.

[0278] Application Example 1

[0279] In this application example, the model is deployed on the UE side, so that the UE can use the measurement results of the downlink reference signal as input to the model for inference.

[0280] FIG16 is a flow chart of a method for predicting time-domain mobility. As shown in FIG16 , the method includes the following steps:

[0281] Step 1601: The UE reports its capability information to the NW.

[0282] Here, the UE capability information may refer to the aforementioned related solutions.

[0283] Step 1602: The NW configures Set A and Set B for the UE.

[0284] Here, the NW may configure Set A and Set B for the UE by referring to the aforementioned related solutions.

[0285] Step 1603: The UE performs measurement according to the configuration of the NW and obtains K measurement instances.

[0286] Here, the contents of the K measurement instances can refer to the aforementioned related solutions. In Figure 16, the kth (1≤k≤K) measurement instance among the K measurement instances is called measurement instance #k. Among them, measurement instance #k contains the measurement results corresponding to Set B_1,k, Set B_2,k,..., Set B_N,k respectively. Here, Set B_n,k (1≤n≤N, 1≤k≤K) is the same as Set B in the above solution. n,k Represent the same meaning.

[0287] Step 1604: The UE-side model predicts, based on the measurement instance, the optimal L (L≥1) cell indices, the optimal J (J≥1) beam (pair) indices in each optimal area, and the link quality corresponding to each optimal beam (pair) index.

[0288] Here, the UE-side model performs predictions corresponding to F prediction moments. Specifically, the UE-side model requires K historical measurement instances as input, so as to predict measurement instances at subsequent F prediction moments.

[0289] Step 1605: The UE reports a prediction instance or a cell handover request to the NW.

[0290] Here, the prediction instance content or cell switching request can refer to the aforementioned related solutions. In Figure 16, the fth (1≤f≤F) prediction instance among the F prediction instances is called prediction instance #f. Among them, prediction instance #f contains the prediction results corresponding to Set A_1,f, Set A_2,f,..., Set A_P,f respectively. Here, Set A_p,f (1≤p≤P, 1≤f≤F) is the same as Set A in the above solution. p,f Represent the same meaning.

[0291] Step 1606: The NW sends a cell switching command or confirmation command to the UE.

[0292] Here, the cell switching command or confirmation command can refer to the aforementioned related solutions.

[0293] It should be noted that in FIG16 , step 1606 is indicated by a dotted line because the optimal cell predicted by the model may be the current serving cell, and therefore cell switching is not required, so step 1606 is an optional step.

[0294] It should be noted that in Figure 16, the ellipse represents the measurement set Set B n,k , where n (1≤n≤N) represents the index of the first downlink reference signal subset (i.e., measurement subset), and k (1≤k≤K) represents the measurement time index. The dotted ellipse indicates that the UE does not perform measurement but is included in the prediction set Set A p，f , where p (1≤p≤P) represents the index of the second downlink reference signal subset (ie, the prediction subset), and f (1≤f≤F) represents the prediction time index.

[0295] It should be noted that the number of prediction subsets P is not necessarily equal to the number of measurement subsets N. For example, when P = 16 and N = 8, the UE only measures 8 measurement subsets, but can infer the optimal cell and / or the optimal beam (pair) index corresponding to the optimal cell and / or the link quality corresponding to the optimal beam (pair) index from 16 prediction subsets (e.g., a prediction subset of 1 serving cell and 15 candidate cells). Of course, a typical deployment is that P equals N, that is, the number of prediction subsets is the same as the number of measurement subsets, and each prediction subset / measurement subset corresponds to a cell.

[0296] It should be noted that, for a cell, Set B and Set A may be the same (ie, only time domain prediction), or Set B and Set A may be different (ie, spatial and time domain prediction).

[0297] It should be noted that for beam-level mobility, the result of the optimal beam predicted by the model is evaluated according to the link quality corresponding to the beam, that is, the highest L1-RSRP. For cell mobility, for example, a single beam of a cell is optimal (the quality of other beams is poor), which does not guarantee that the cell is the optimal cell for the UE. A common method for selecting the optimal cell is to perform a weighted average of the link qualities of multiple beams in the cell to obtain the optimal cell (ranked from high to low by the mean L1-RSRP). In the technical solution of the embodiment of the present application, the model used to predict the optimal cell also refers to the selection mechanism of the optimal cell, namely cell-level mobility.

[0298] Application Example 2

[0299] In this application example, the model is deployed on the NW side. The UE measures time-domain mobility, namely, Set B, and reports the measurement results to the NW. The model deployed on the NW side can then predict time-domain mobility.

[0300] FIG17 is a flow chart of a method for predicting time-domain mobility. As shown in FIG17 , the method includes the following steps:

[0301] Step 1701: The UE reports its capability information to the NW.

[0302] Here, the UE capability information may refer to the aforementioned related solutions.

[0303] Step 1702: The NW configures Set A and Set B for the UE.

[0304] Here, the NW may configure Set A and Set B for the UE by referring to the aforementioned related solutions.

[0305] Step 1703: The UE performs measurement according to the configuration of the NW and obtains K measurement instances.

[0306] Here, the contents of the K measurement instances can refer to the aforementioned related solutions. In Figure 17, the kth (1≤k≤K) measurement instance among the K measurement instances is called measurement instance #k. Among them, measurement instance #k contains the measurement results corresponding to Set B_1,k, Set B_2,k,..., Set B_N,k respectively. Here, Set B_n,k (1≤n≤N, 1≤k≤K) is the same as Set B in the above solution. n,k Represent the same meaning.

[0307] Step 1704: The UE may report a measurement instance to the NW each time it obtains a measurement instance, or the UE may obtain K measurement instances and then report K measurement instances to the NW.

[0308] Step 1705: The NW-side model predicts, based on the measurement instances, the optimal L (L≥1) cell indices, the optimal J (J≥1) beam (pair) indices in each optimal area, and the link quality corresponding to each optimal beam (pair) index.

[0309] Here, the content of the prediction instance can refer to the above-mentioned related scheme. In Figure 17, the fth (1≤f≤F) prediction instance among the F prediction instances is called prediction instance #f. Among them, prediction instance #f contains the prediction results corresponding to Set A_1,f, Set A_2,f,..., Set A_P,f respectively. Here, Set A_p,f (1≤p≤P, 1≤f≤F) is the same as Set A in the above scheme. p,f Represent the same meaning.

[0310] Step 1706: The NW sends a cell switching command to the UE.

[0311] Here, the cell switching command or confirmation command can refer to the aforementioned related solutions.

[0312] It should be noted that in FIG17 , step 1706 is indicated by a dotted line because the optimal cell predicted by the model may be the current serving cell, and therefore cell switching is not required, so step 1706 is an optional step.

[0313] It should be noted that in Figure 17, the ellipse represents the measurement set Set B n,k , where n (1≤n≤N) represents the index of the first downlink reference signal subset (i.e., measurement subset), and k (1≤k≤K) represents the measurement time index. The dotted ellipse indicates that the UE does not perform measurement but is included in the prediction set Set A p，f , where p (1≤p≤P) represents the index of the second downlink reference signal subset (ie, the prediction subset), and f (1≤f≤F) represents the prediction time index.

[0314] It should be noted that the number of prediction subsets P is not necessarily equal to the number of measurement subsets N. For example, when P = 16 and N = 8, the UE only measures 8 measurement subsets, but the NW can infer the optimal cell and / or the optimal beam (pair) index corresponding to the optimal cell and / or the link quality corresponding to the optimal beam (pair) index from 16 prediction subsets (such as a prediction subset of 1 serving cell and 15 candidate cells). Of course, a typical deployment method is that P equals N, that is, the number of prediction subsets is the same as the number of measurement subsets, and each prediction subset / measurement subset corresponds to a cell.

[0315] It should be noted that, for a cell, Set B and Set A may be the same (ie, only time domain prediction), or Set B and Set A may be different (ie, spatial and time domain prediction).

[0316] It should be noted that for beam-level mobility, the result of the optimal beam predicted by the model is evaluated according to the link quality corresponding to the beam, that is, the highest L1-RSRP. For cell mobility, for example, a single beam of a cell is optimal (the quality of other beams is poor), which does not guarantee that the cell is the optimal cell for the UE. A common method for selecting the optimal cell is to perform a weighted average of the link qualities of multiple beams in the cell to obtain the optimal cell (ranked from high to low by the mean L1-RSRP). In the technical solution of the embodiment of the present application, the model used to predict the optimal cell also refers to the selection mechanism of the optimal cell, namely cell-level mobility.

[0317] The preferred embodiments of the present application are described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the specific details in the above embodiments. Within the technical concept of the present application, the technical solution of the present application can be subjected to a variety of simple modifications, and these simple modifications all fall within the scope of protection of the present application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present application will no longer describe the various possible combinations separately. For another example, the various different embodiments of the present application can also be arbitrarily combined, as long as they do not violate the idea of ​​the present application, they should also be regarded as the contents disclosed in the present application. For another example, under the premise of no conflict, the various embodiments and / or the technical features in each embodiment described in the present application can be arbitrarily combined with the prior art, and the technical solution obtained after the combination should also fall within the scope of protection of the present application.

[0318] It should also be understood that in the various method embodiments of the present application, the sequence numbers of the above-mentioned processes do not imply a precedence in the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. In addition, in the embodiments of the present application, the terms "downlink," "uplink," and "sidelink" are used to indicate the transmission direction of signals or data, where "downlink" is used to indicate the first direction of transmission of signals or data from a site to a user equipment in a cell, "uplink" is used to indicate the second direction of transmission of signals or data from a user equipment in a cell to a site, and "sidelink" is used to indicate the third direction of transmission of signals or data from user equipment 1 to user equipment 2. For example, "downlink signal" indicates that the transmission direction of the signal is the first direction. In addition, in the embodiments of the present application, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three relationships can exist. Specifically, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0319] FIG18 is a schematic diagram of the first structure of a mobility prediction device provided in an embodiment of the present application, which is applied to a terminal. As shown in FIG18 , the device includes:

[0320] An acquisition unit 1801 is configured to acquire K measurement instances corresponding to a first downlink reference signal set, where the K measurement instances are obtained based on measurements by the terminal of downlink reference signals in the first downlink reference signal set at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to acquire F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; and K, N, F, and P are integers greater than or equal to 1.

[0321] In some embodiments, the apparatus further includes: a receiving unit 1802 for receiving first configuration information and / or second configuration information sent by a network device; the first configuration information is used to configure the first downlink reference signal set; the second configuration information is used to configure the second downlink reference signal set.

[0322] In some embodiments, the first configuration information is further used to configure first time domain information corresponding to the first downlink reference signal set, and the first time domain information is used to determine the K measurement moments corresponding to the K measurement instances; and / or, the second configuration information is further used to configure second time domain information corresponding to the second downlink reference signal set, and the second time domain information is used to determine the F prediction moments corresponding to the F prediction instances.

[0323] In some embodiments, the first time domain information includes at least one of the following: a measurement period, a first offset, and the number of measurement moments K; and / or the second time domain information includes at least one of the following: a prediction period, a second offset, and the number of prediction moments F.

[0324] In some implementations, the first configuration information and / or the second configuration information is related to capability information of the terminal.

[0325] In some implementations, the apparatus further includes: a sending unit 1803, configured to send the capability information of the terminal to the network device, where the capability information of the terminal includes at least one of the following:

[0326] first information, where the first information is used to indicate whether the terminal supports measurement and / or prediction of time domain mobility;

[0327] second information, the second information being used to indicate at least one of the following: a maximum number of cells supported for measurement by the terminal on all CCs or BWPs; a maximum number of configured first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; and a maximum number of first downlink reference signal subsets supported for measurement by the terminal on all downlink CCs or BWPs;

[0328] third information, the third information being used to indicate at least one of the following: a maximum number of predicted cells supported by the terminal on all downlink CCs or BWPs; a maximum number of configured second downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; and a maximum number of predicted first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs;

[0329] Fourth information, the fourth information is used to indicate at least one of the following: a maximum number of cells supported for measurement by the terminal on each downlink CC or BWP; a maximum number of configured first downlink reference signal subsets supported by the terminal on each downlink CC or BWP; and a maximum number of first downlink reference signal subsets supported for measurement by the terminal on each downlink CC or BWP.

[0330] Fifth information, the fifth information is used to indicate at least one of the following: a maximum number of predicted cells supported by the terminal on each downlink CC or BWP; a maximum number of configured second downlink reference signal subsets supported by the terminal on each downlink CC or BWP; a maximum number of predicted second downlink reference signal subsets supported by the terminal on each downlink CC or BWP;

[0331] Sixth information, where the sixth information is used to indicate a maximum number of downlink reference signals supported for measurement by the terminal in each first downlink reference signal subset;

[0332] seventh information, where the seventh information is used to indicate a maximum number of predicted downlink reference signals supported by the terminal in each second downlink reference signal subset;

[0333] Eighth information, where the eighth information is used to indicate the number of measurement moments supported by the terminal;

[0334] Ninth information, the ninth information is used to indicate the number of prediction moments supported by the terminal.

[0335] In some embodiments, the K measurement instances include:

[0336] K×N groups of link qualities; or,

[0337] K×N indexes and K×N groups of link qualities, the K×N indexes and the K×N link qualities have a corresponding relationship; each index in the K×N indexes corresponds to a measurement time index and a cell index.

[0338] In some embodiments, each of the F prediction instances includes at least one of the following:

[0339] The best L cell indexes among the P cells;

[0340] The best J downlink reference signal resource indices in each best cell;

[0341] The link quality corresponding to each optimal downlink reference signal resource index;

[0342] L, J are positive integers.

[0343] In some implementations, each prediction instance further includes: a prediction time index corresponding to the prediction instance.

[0344] In some implementations, the first model is located at the terminal side.

[0345] In some implementations, the sending unit 1803 is configured to send the F prediction instances to a network device.

[0346] In some embodiments, the reporting manner of the F prediction instances includes at least one of the following: periodic reporting, semi-persistent reporting, aperiodic reporting, and layer 1 and / or layer 2 based mobility event reporting.

[0347] In some implementations, for the periodic reporting or the semi-persistent reporting, the receiving unit 1802 is configured to receive third configuration information sent by a network device, where the third configuration information is used to configure a reporting period and a reporting offset corresponding to the periodic reporting or the semi-persistent reporting.

[0348] In some implementations, for the aperiodic reporting, the receiving unit 1802 is configured to receive a first DCI sent by a network device, where the first DCI is used to trigger the aperiodic reporting.

[0349] In some embodiments, the layer 1 and / or layer 2 mobility events include at least one of the following:

[0350] A first event, wherein the first event is that at some or all of the F predicted moments, the predicted link performance of the current serving cell is better than the first threshold or weaker than the second threshold;

[0351] A second event, wherein the second event is that at some or all of the F predicted moments, the predicted link performance of the current serving cell is better or weaker than the predicted link performance of the candidate cell plus an offset;

[0352] a third event, wherein the third event is: at some or all of the F predicted moments, the predicted link performance of the candidate cell is better than the third threshold or weaker than the fourth threshold;

[0353] a fourth event, wherein: at some or all of the F predicted moments, the predicted link performance of the current serving cell is weaker than the fifth threshold, and the predicted link performance of the candidate cell is better than the sixth threshold;

[0354] The current cell is one of the P cells, and the candidate cell is one of the P cells except the current cell.

[0355] In some embodiments, for the mobility event reporting based on layer 1 and / or layer 2, the sending unit 1803 is used to determine that the mobility event of layer 1 and / or layer 2 is triggered and send a reporting request to the network device; the receiving unit 1802 is used to receive the second DCI sent by the network device, and the second DCI is used to schedule the PUSCH for reporting.

[0356] In some implementations, the reporting granularity of the F prediction instances is one prediction instance.

[0357] In some embodiments, the report has a first format, the first format including: a predicted time index, L cell indexes, X1 downlink reference signal resource indexes, and X1 link qualities;

[0358] The L cell indexes are the best L cell indexes among the P cells; the X1 downlink reference signal resource indexes include the best J downlink reference signal resource indexes in each of the L cells; and the X1 link qualities correspond to the X1 downlink reference signal resource indexes.

[0359] In some embodiments, the reporting granularity of the F prediction instances is F prediction instances.

[0360] In some embodiments, the reporting has a second format, the second format including F groups of information, the F groups of information corresponding to the F prediction instances;

[0361] Each of the F groups of information includes: a predicted time index, L cell indexes, X1 downlink reference signal resource indexes, and X1 link qualities; or,

[0362] Each group of information in the F groups of information includes: L cell indexes, X1 downlink reference signal resource indexes and X1 link qualities;

[0363] The L cell indexes are the best L cell indexes among the P cells; the X1 downlink reference signal resource indexes include the best J downlink reference signal resource indexes in each of the L cells; and the X1 link qualities correspond to the X1 downlink reference signal resource indexes.

[0364] In some implementations, the first model is located on the network device side.

[0365] In some implementations, the sending unit 1803 is configured to send the K measurement instances to a network device.

[0366] In some implementations, the reporting manner of the K measurement instances includes at least one of the following: periodic reporting, semi-persistent reporting, and aperiodic reporting.

[0367] In some embodiments, for the periodic reporting or the semi-continuous reporting, the receiving unit 1802 is used to receive fourth configuration information sent by the network device, and the fourth configuration information is used to configure the reporting period and reporting offset corresponding to the periodic reporting or the semi-continuous reporting.

[0368] In some implementations, for the aperiodic reporting, the receiving unit 1802 is configured to receive a third DCI sent by a network device, where the third DCI is used to trigger the aperiodic reporting.

[0369] In some implementations, the reporting granularity of the K measurement instances is one measurement instance.

[0370] In some embodiments, the report has a third format, the third format including: a measurement time index and X2 link qualities;

[0371] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0372] In some embodiments, the report has a fourth format, the fourth format including: a measurement time index, N cell indices, and X2 link qualities;

[0373] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0374] In some implementations, the reporting granularity of the K measurement instances is K measurement instances.

[0375] In some implementations, the report has a fifth format, the fifth format including: K groups of information, the K groups of information corresponding to the K measurement instances; each group of information in the K groups of information including: X2 link qualities;

[0376] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0377] In some embodiments, the report has a sixth format, the sixth format including: N cell indexes and K groups of information, or the sixth format including: N cell indexes, K measurement instance indexes, and K groups of information; the K groups of information have a corresponding relationship with the K measurement instance indexes; each group of information in the K groups of information includes: X2 link qualities;

[0378] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0379] In some implementations, the format of the X2 link qualities in the report adopts a differential reporting format or a non-differential reporting format.

[0380] In some implementations, for a case where the reporting granularity is one measurement instance, in the differential reporting format,

[0381] The X2 link qualities are represented by a reference link quality and X2-1 differential values; or,

[0382] The X2 link qualities are divided into N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

[0383] In some implementations, for a case where the reporting granularity is K measurement instances, in the differential reporting format,

[0384] K×X2 link qualities are represented by a reference link quality and K×X2-1 differential values; or,

[0385] The K×X2 link qualities are divided into K×N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

[0386] In some implementations, in the non-differential reporting format, the X2 link qualities are represented by X2 link qualities.

[0387] In some implementations, the reporting granularity of the K measurement instances is the measurement result of one cell in one measurement instance.

[0388] In some embodiments, the report has a sixth format, the sixth format including: a measurement time index, a cell index, and M link qualities;

[0389] The M link qualities include link qualities of M downlink reference signals of the cell corresponding to the cell index, and M is a positive integer.

[0390] In some implementations, the format of the M link qualities in the report adopts a differential reporting format or a non-differential reporting format.

[0391] In some implementations, in the differential reporting format, the M link qualities are represented by a reference link quality and M-1 differential values.

[0392] In some implementations, in the non-differential reporting format, the M link qualities are represented by M link qualities.

[0393] In some implementations, the receiving unit 1802 is configured to receive a cell switching command sent by the network device, where the cell switching command is used to instruct the terminal to switch to a target cell, where the target cell is determined based on some or all of the F prediction instances.

[0394] In some implementations, the sending unit 1803 is configured to send a cell switching request to the network device, where the cell switching request carries a target prediction instance, and the target prediction instance is determined based on the F prediction instances.

[0395] In some implementations, the measurement times corresponding to the K measurement instances and the prediction times corresponding to the F prediction instances satisfy the following temporal relationship:

[0396] The measurement moments corresponding to the K measurement instances are within a first time period, and the prediction moments corresponding to the F prediction instances are within a second time period, and the second time period does not overlap with the first time period; or

[0397] The measurement moments corresponding to the K measurement instances are located within the K first time periods, and the prediction moments corresponding to the F prediction instances are located within the last first time period of the K first time periods.

[0398] Those skilled in the art should understand that the relevant description of the above-mentioned mobility prediction device in the embodiment of the present application can be understood with reference to the relevant description of the time domain mobility prediction method in the embodiment of the present application.

[0399] FIG19 is a second schematic diagram of the structure of a mobility prediction device provided in an embodiment of the present application, which is applied to a network device. As shown in FIG19 , the device includes:

[0400] A receiving unit 1901 is configured to receive K measurement instances corresponding to a first downlink reference signal set sent by a terminal, where the K measurement instances are obtained based on measurements by the terminal of downlink reference signals in the first downlink reference signal set at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; and K, N, F, and P are integers greater than or equal to 1.

[0401] In some embodiments, the apparatus further includes: a sending unit 1902, configured to send the first configuration information and / or the second configuration information to the terminal;

[0402] The first configuration information is used to configure the first downlink reference signal set; the second configuration information is used to configure the second downlink reference signal set.

[0403] In some embodiments, the first configuration information is further used to configure first time domain information corresponding to the first downlink reference signal set, where the first time domain information is used to determine K measurement times corresponding to the K measurement instances; and / or,

[0404] The second configuration information is further used to configure second time domain information corresponding to the second downlink reference signal set, and the second time domain information is used to determine F prediction moments corresponding to the F prediction instances.

[0405] In some embodiments, the first time domain information includes at least one of the following: a measurement period, a first offset, a number K of measurement moments; and / or,

[0406] The second time domain information includes at least one of the following: a prediction period, a second offset, and the number F of prediction moments.

[0407] In some implementations, the first configuration information and / or the second configuration information is related to capability information of the terminal.

[0408] In some implementations, the receiving unit 1901 is configured to receive capability information of the terminal sent by the terminal, where the capability information of the terminal includes at least one of the following:

[0409] first information, where the first information is used to indicate whether the terminal supports measurement and / or prediction of time domain mobility;

[0410] second information, the second information being used to indicate at least one of the following: a maximum number of cells supported for measurement by the terminal on all downlink CCs or BWPs; a maximum number of configured first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; and a maximum number of first downlink reference signal subsets supported for measurement by the terminal on all downlink CCs or BWPs;

[0411] third information, the third information being used to indicate at least one of the following: a maximum number of predicted cells supported by the terminal on all downlink CCs or BWPs; a maximum number of configured second downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; and a maximum number of predicted first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs;

[0412] Fourth information, the fourth information is used to indicate at least one of the following: a maximum number of cells supported for measurement by the terminal on each downlink CC or BWP; a maximum number of configured first downlink reference signal subsets supported by the terminal on each downlink CC or BWP; and a maximum number of first downlink reference signal subsets supported for measurement by the terminal on each downlink CC or BWP.

[0413] Fifth information, the fifth information is used to indicate at least one of the following: a maximum number of predicted cells supported by the terminal on each downlink CC or BWP; a maximum number of configured second downlink reference signal subsets supported by the terminal on each downlink CC or BWP; a maximum number of predicted second downlink reference signal subsets supported by the terminal on each downlink CC or BWP;

[0414] Sixth information, where the sixth information is used to indicate a maximum number of downlink reference signals supported for measurement by the terminal in each first downlink reference signal subset;

[0415] seventh information, where the seventh information is used to indicate a maximum number of predicted downlink reference signals supported by the terminal in each second downlink reference signal subset;

[0416] Eighth information, where the eighth information is used to indicate the number of measurement moments supported by the terminal;

[0417] Ninth information, the ninth information is used to indicate the number of prediction moments supported by the terminal.

[0418] In some embodiments, the K measurement instances include:

[0419] N×K groups of link qualities; or,

[0420] N×K indexes and N×K groups of link qualities, wherein the N×K indexes and the N×K link qualities have a corresponding relationship; each of the N×K indexes corresponds to a cell index and a measurement time index.

[0421] In some embodiments, each of the F prediction instances includes at least one of the following:

[0422] The best L cell indexes among the P cells;

[0423] The best J downlink reference signal resource indices in each best cell;

[0424] The link quality corresponding to each optimal downlink reference signal resource index;

[0425] L, J are positive integers.

[0426] In some implementations, each prediction instance further includes: a prediction time index corresponding to the prediction instance.

[0427] In some implementations, the reporting manner of the K measurement instances includes at least one of the following: periodic reporting, semi-persistent reporting, and aperiodic reporting.

[0428] In some implementations, the sending unit 1902 is configured to send fourth configuration information to the terminal, where the fourth configuration information is used to configure a reporting period and a reporting offset corresponding to the periodic reporting or the semi-persistent reporting.

[0429] In some implementations, the sending unit 1902 is configured to send a third DCI to the terminal, where the third DCI is used to trigger the aperiodic reporting.

[0430] In some implementations, the reporting granularity of the K measurement instances is one measurement instance.

[0431] In some embodiments, the report has a third format, the third format including: a measurement time index and X2 link qualities;

[0432] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0433] In some embodiments, the report has a fourth format, the fourth format including: a measurement time index, N cell indices, and X2 link qualities;

[0434] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0435] In some implementations, the reporting granularity of the K measurement instances is K measurement instances.

[0436] In some implementations, the report has a fifth format, the fifth format including: K groups of information, the K groups of information corresponding to the K measurement instances; each group of information in the K groups of information including: X2 link qualities;

[0437] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0438] In some embodiments, the report has a sixth format, the sixth format including: N cell indexes and K groups of information, or the sixth format including: N cell indexes, K measurement instance indexes, and K groups of information; the K groups of information have a corresponding relationship with the K measurement instance indexes; each group of information in the K groups of information includes: X2 link qualities;

[0439] The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

[0440] In some implementations, the format of the X2 link qualities in the report adopts a differential reporting format or a non-differential reporting format.

[0441] In some implementations, for a case where the reporting granularity is one measurement instance, in the differential reporting format,

[0442] The X2 link qualities are represented by a reference link quality and X2-1 differential values; or,

[0443] The X2 link qualities are divided into N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

[0444] In some implementations, for a case where the reporting granularity is K measurement instances, in the differential reporting format,

[0445] K×X2 link qualities are represented by a reference link quality and K×X2-1 differential values; or,

[0446] The K×X2 link qualities are divided into K×N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

[0447] In some implementations, in the non-differential reporting format, the X2 link qualities are represented by X2 link qualities.

[0448] In some implementations, the reporting granularity of the K measurement instances is the measurement result of one cell in one measurement instance.

[0449] In some embodiments, the report has a sixth format, the sixth format including: a measurement time index, a cell index, and M link qualities;

[0450] The M link qualities include link qualities of M downlink reference signals of the cell corresponding to the cell index, and M is a positive integer.

[0451] In some implementations, the format of the M link qualities in the report adopts a differential reporting format or a non-differential reporting format.

[0452] In some implementations, in the differential reporting format, the M link qualities are represented by a reference link quality and M-1 differential values.

[0453] In some implementations, in the non-differential reporting format, the M link qualities are represented by M link qualities.

[0454] In some implementations, the sending unit 1902 is configured to send a cell switching command to the terminal, where the cell switching command is used to instruct the terminal to switch to a target cell, where the target cell is determined based on some or all of the F prediction instances.

[0455] In some implementations, the measurement times corresponding to the K measurement instances and the prediction times corresponding to the F prediction instances satisfy the following temporal relationship:

[0456] The measurement moments corresponding to the K measurement instances are within a first time period, and the prediction moments corresponding to the F prediction instances are within a second time period, and the second time period does not overlap with the first time period; or

[0457] The measurement moments corresponding to the K measurement instances are located within the K first time periods, and the prediction moments corresponding to the F prediction instances are located within the last first time period of the K first time periods.

[0458] Those skilled in the art should understand that the relevant description of the above-mentioned mobility prediction device in the embodiment of the present application can be understood with reference to the relevant description of the time domain mobility prediction method in the embodiment of the present application.

[0459] Figure 20 is a schematic diagram of a communication device 2000 provided in an embodiment of the present application. The communication device can be a terminal or a network device. The communication device 2000 shown in Figure 20 includes a processor 2010, which can call and execute a computer program from a memory to implement the method in the embodiment of the present application.

[0460] Optionally, as shown in FIG20 , the communication device 2000 may further include a memory 2020. The processor 2010 may call and execute a computer program from the memory 2020 to implement the method in the embodiment of the present application.

[0461] The memory 2020 may be a separate device independent of the processor 2010 , or may be integrated into the processor 2010 .

[0462] Optionally, as shown in FIG20 , the communication device 2000 may further include a transceiver 2030 , and the processor 2010 may control the transceiver 2030 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices.

[0463] The transceiver 2030 may include a transmitter and a receiver. The transceiver 2030 may further include an antenna, and the number of antennas may be one or more.

[0464] Optionally, the communication device 2000 may specifically be a network device in an embodiment of the present application, and the communication device 2000 may implement the corresponding processes implemented by the network device in each method in the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0465] Optionally, the communication device 2000 may specifically be a mobile terminal / terminal of an embodiment of the present application, and the communication device 2000 may implement the corresponding processes implemented by the mobile terminal / terminal in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0466] Figure 21 is a schematic structural diagram of a chip according to an embodiment of the present application. The chip 2100 shown in Figure 21 includes a processor 2110, which can call and run a computer program from a memory to implement the method according to the embodiment of the present application.

[0467] Optionally, as shown in FIG21 , the chip 2100 may further include a memory 2120. The processor 2110 may call and execute a computer program from the memory 2120 to implement the method in the embodiment of the present application.

[0468] The memory 2120 may be a separate device independent of the processor 2110 , or may be integrated into the processor 2110 .

[0469] Optionally, the chip 2100 may further include an input interface 2130. The processor 2110 may control the input interface 2130 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips.

[0470] Optionally, the chip 2100 may further include an output interface 2140. The processor 2110 may control the output interface 2140 to communicate with other devices or chips, and specifically, may output information or data to other devices or chips.

[0471] Optionally, the chip can be applied to the network device in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the network device in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0472] Optionally, the chip can be applied to the mobile terminal / terminal in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the mobile terminal / terminal in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0473] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0474] FIG22 is a schematic block diagram of a communication system 2200 provided in an embodiment of the present application. As shown in FIG22 , the communication system 2200 includes a terminal 2210 and a network device 2220 .

[0475] Among them, the terminal 2210 can be used to implement the corresponding functions implemented by the terminal in the above method, and the network device 2220 can be used to implement the corresponding functions implemented by the network device in the above method. For the sake of brevity, they are not repeated here.

[0476] It should be understood that the processor of the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by hardware integrated logic circuits in the processor or software instructions. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0477] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0478] It should be understood that the above-mentioned memories are exemplary but not restrictive. For example, the memories in the embodiments of the present application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM RAM (DR RAM), etc. In other words, the memories in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0479] An embodiment of the present application also provides a computer-readable storage medium for storing a computer program.

[0480] Optionally, the computer-readable storage medium can be applied to the network device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of the present application. For the sake of brevity, they are not repeated here.

[0481] Optionally, the computer-readable storage medium can be applied to the mobile terminal / terminal in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the mobile terminal / terminal in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0482] An embodiment of the present application also provides a computer program product, including computer program instructions.

[0483] Optionally, the computer program product can be applied to the network device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of the present application. For the sake of brevity, they are not repeated here.

[0484] Optionally, the computer program product can be applied to the mobile terminal / terminal in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the mobile terminal / terminal in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0485] The embodiment of the present application also provides a computer program.

[0486] Optionally, the computer program can be applied to the network device in the embodiments of the present application. When the computer program runs on a computer, the computer executes the corresponding processes implemented by the network device in the various methods of the embodiments of the present application. For the sake of brevity, they are not described here.

[0487] Optionally, the computer program can be applied to the mobile terminal / terminal in the embodiments of the present application. When the computer program runs on the computer, the computer executes the corresponding processes implemented by the mobile terminal / terminal in the various methods of the embodiments of the present application. For the sake of brevity, they are not repeated here.

[0488] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0489] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0490] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0491] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0492] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0493] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A mobility prediction method, the method comprising: The terminal obtains K measurement instances corresponding to a first downlink reference signal set, where the K measurement instances are obtained based on the terminal measuring the downlink reference signals in the first downlink reference signal set at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; K, N, F and P are integers greater than or equal to 1.

2. The method according to claim 1, wherein: The method further comprises: The terminal receives first configuration information and / or second configuration information sent by the network device; The first configuration information is used to configure the first downlink reference signal set; the second configuration information is used to configure the second downlink reference signal set.

3. The method according to claim 2, wherein: The first configuration information is further used to configure first time domain information corresponding to the first downlink reference signal set, and the first time domain information is used to determine K measurement times corresponding to the K measurement instances; and / or, The second configuration information is further used to configure second time domain information corresponding to the second downlink reference signal set, and the second time domain information is used to determine F prediction moments corresponding to the F prediction instances.

4. The method according to claim 3, wherein: The first time domain information includes at least one of the following: a measurement period, a first offset, and a number K of measurement moments; and / or, The second time domain information includes at least one of the following: a prediction period, a second offset, and the number F of prediction moments.

5. The method according to any one of claims 2 to 4, wherein: The first configuration information and / or the second configuration information are related to capability information of the terminal.

6. The method according to any one of claims 2 to 5, wherein: The method further comprises: The terminal sends the capability information of the terminal to the network device, where the capability information of the terminal includes at least one of the following: first information, where the first information is used to indicate whether the terminal supports measurement and / or prediction of time domain mobility; second information, the second information is used to indicate at least one of the following: the maximum number of cells that the terminal supports measuring on all downlink carrier components CC or bandwidth part BWP; the maximum number of configured first downlink reference signal subsets that the terminal supports on all downlink CCs or BWPs; the maximum number of first downlink reference signal subsets that the terminal supports measuring on all downlink CCs or BWPs; The third information is used to indicate at least one of the following: the maximum number of predicted cells supported by the terminal on all downlink CCs or BWPs; the maximum number of configured second downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; the maximum number of predicted first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; Fourth information, the fourth information is used to indicate at least one of the following: the maximum number of cells supported for measurement by the terminal on each downlink CC or BWP; the maximum number of configured first downlink reference signal subsets supported by the terminal on each downlink CC or BWP; the maximum number of first downlink reference signal subsets supported for measurement by the terminal on each downlink CC or BWP; The fifth information is used to indicate at least one of the following: the number of predicted cells supported by the terminal on each downlink CC or BWP at most; the number of configured second downlink reference signal subsets supported by the terminal on each downlink CC or BWP at most; the number of predicted second downlink reference signal subsets supported by the terminal on each downlink CC or BWP; Sixth information, where the sixth information is used to indicate a maximum number of downlink reference signals that the terminal supports measuring in each first downlink reference signal subset; seventh information, where the seventh information is used to indicate a maximum number of predicted downlink reference signals supported by the terminal in each second downlink reference signal subset; Eighth information, where the eighth information is used to indicate the number of measurement moments supported by the terminal; Ninth information, the ninth information is used to indicate the number of prediction moments supported by the terminal.

7. The method according to any one of claims 1 to 6, wherein: The K measurement instances include: K×N groups of link qualities; or, K×N indexes and K×N groups of link qualities, the K×N indexes and the K×N link qualities have a corresponding relationship; each of the K×N indexes corresponds to a measurement time index and a cell index.

8. The method according to any one of claims 1 to 7, wherein: Each of the F prediction instances includes at least one of the following: The best L cell indexes among the P cells; The best J downlink reference signal resource indexes in each best cell; The link quality corresponding to each optimal downlink reference signal resource index; L, J are positive integers.

9. The method according to claim 8, wherein: Each prediction instance further includes: a prediction time index corresponding to the prediction instance.

10. The method according to any one of claims 1 to 8, wherein: The first model is located at the terminal side.

11. The method according to claim 10, wherein: The method further comprises: The terminal sends the F prediction instances to a network device.

12. According to the method of claim 11, the reporting mode of the F prediction instances includes at least one of the following: periodic reporting, semi-continuous reporting, non-periodic reporting, and mobility event reporting based on layer 1 and / or layer 2.

13. The method according to claim 12, wherein: For the periodic reporting or the semi-persistent reporting, before the terminal sends the F prediction instances to the network device, the method further includes: The terminal receives third configuration information sent by the network device, where the third configuration information is used to configure a reporting period and a reporting offset corresponding to the periodic reporting or the semi-persistent reporting.

14. The method according to claim 12, wherein: For the non-periodic reporting, before the terminal sends the F prediction instances to the network device, the method further includes: The terminal receives first downlink control information DCI sent by a network device, where the first DCI is used to trigger the non-periodic reporting.

15. The method according to claim 12, wherein: The layer 1 and / or layer 2 mobility events include at least one of the following: A first event, wherein the first event is that at some or all of the F predicted moments, the predicted link performance of the current serving cell is better than the first threshold or weaker than the second threshold; A second event, wherein the second event is that at some or all of the F prediction moments, the predicted link performance of the current serving cell is better or weaker than the predicted link performance of the candidate cell plus an offset; A third event, wherein the third event is: at some or all of the F prediction moments, the predicted link performance of the candidate cell is better than the third threshold or weaker than the fourth threshold; A fourth event, wherein the fourth event is: at some or all of the F prediction moments, the predicted link performance of the current serving cell is weaker than the fifth threshold, and the predicted link performance of the candidate cell is better than the sixth threshold; The current cell is one of the P cells, and the candidate cell is one of the P cells except the current cell.

16. The method according to claim 12 or 15, wherein: For the mobility event reporting based on layer 1 and / or layer 2, before the terminal sends the F prediction instances to the network device, the method further includes: The terminal determines that the mobility event of layer 1 and / or layer 2 is triggered, and sends a report request to the network device; The terminal receives a second DCI sent by the network device, where the second DCI is used for scheduling a physical uplink shared channel PUSCH for reporting.

17. The method according to any one of claims 11 to 16, wherein: The reporting granularity of the F prediction instances is one prediction instance.

18. The method according to claim 17, wherein: The report has a first format, the first format including: a prediction time index, L cell indexes, X1 downlink reference signal resource indexes, and X1 link qualities; The L cell indexes are the best L cell indexes among the P cells; the X1 downlink reference signal resource indexes include the best J downlink reference signal resource indexes in each of the L cells; and the X1 link qualities have a corresponding relationship with the X1 downlink reference signal resource indexes.

19. The method according to any one of claims 11 to 16, wherein: The reporting granularity of the F prediction instances is F prediction instances.

20. The method according to claim 19, wherein: The reporting has a second format, the second format includes F groups of information, and the F groups of information have a corresponding relationship with the F prediction instances; Each group of information in the F groups of information includes: a prediction time index, L cell indexes, X1 downlink reference signal resource indexes and X1 link qualities; or, Each group of information in the F groups of information includes: L cell indexes, X1 downlink reference signal resource indexes and X1 link qualities; The L cell indexes are the best L cell indexes among the P cells; the X1 downlink reference signal resource indexes include the best J downlink reference signal resource indexes in each of the L cells; and the X1 link qualities have a corresponding relationship with the X1 downlink reference signal resource indexes.

21. The method according to any one of claims 1 to 8, wherein: The first model is located at the network device side.

22. The method according to claim 21, wherein: The method further comprises: The terminal sends the K measurement instances to a network device.

23. The method according to claim 22, wherein: The reporting manner of the K measurement instances includes at least one of the following: periodic reporting, semi-continuous reporting, and non-periodic reporting.

24. The method according to claim 23, wherein: For the periodic reporting or the semi-persistent reporting, before the terminal sends the K measurement instances to the network device, the method further includes: The terminal receives fourth configuration information sent by the network device, where the fourth configuration information is used to configure a reporting period and a reporting offset corresponding to the periodic reporting or the semi-persistent reporting.

25. The method according to claim 23, wherein: For the non-periodic reporting, before the terminal sends the K measurement instances to the network device, the method further includes: The terminal receives a third DCI sent by a network device, where the third DCI is used to trigger the non-periodic reporting.

26. The method according to any one of claims 22 to 25, wherein: The reporting granularity of the K measurement instances is one measurement instance.

27. The method according to claim 26, wherein: The report has a third format, and the third format includes: a measurement time index and X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

28. The method according to claim 26, wherein: The report has a fourth format, and the fourth format includes: a measurement time index, N cell indexes, and X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

29. The method according to any one of claims 22 to 25, wherein: The reporting granularity of the K measurement instances is K measurement instances.

30. The method of claim 29, wherein: The report has a fifth format, the fifth format including: K groups of information, the K groups of information corresponding to the K measurement instances; each group of information in the K groups of information includes: X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

31. The method of claim 29, wherein: The report has a sixth format, the sixth format includes: N cell indexes and K group information, or the sixth format includes: N cell indexes, K measurement instance indexes and K group information; the K group information has a corresponding relationship with the K measurement instance indexes; each group of information in the K group information includes: X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

32. The method according to any one of claims 27 to 31, wherein: The format of the X2 link qualities in the report adopts a differential reporting format or a non-differential reporting format.

33. The method of claim 32, wherein: For the case where the reporting granularity is one measurement instance, in the differential reporting format, The X2 link qualities are represented by a reference link quality and X2-1 differential values; or, The X2 link qualities are divided into N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

34. The method of claim 32, wherein: For the case where the reporting granularity is K measurement instances, in the differential reporting format, K×X2 link qualities are represented by a reference link quality and K×X2-1 differential values; or, The K×X2 link qualities are divided into K×N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

35. The method of claim 32, wherein: In the non-differential reporting format, the X2 link qualities are represented by X2 link qualities.

36. The method according to any one of claims 22 to 25, wherein: The reporting granularity of the K measurement instances is the measurement result of one cell in one measurement instance.

37. The method of claim 36, wherein: The report has a sixth format, and the sixth format includes: a measurement time index, a cell index, and M link qualities; The M link qualities include link qualities of M downlink reference signals of the cell corresponding to the cell index, and M is a positive integer.

38. The method of claim 37, wherein: The format of the M link qualities in the report adopts a differential reporting format or a non-differential reporting format.

39. The method of claim 38, wherein: In the differential reporting format, the M link qualities are represented by a reference link quality and M-1 differential values.

40. The method of claim 38, wherein: In the non-differential reporting format, the M link qualities are represented by M link qualities.

41. The method according to any one of claims 1 to 40, wherein: The method further comprises: The terminal receives a cell switching command sent by the network device, where the cell switching command is used to instruct the terminal to switch to a target cell, where the target cell is determined based on part or all of the F prediction instances.

42. The method according to any one of claims 1 to 20, wherein: The method further comprises: The terminal sends a cell switching request to the network device, where the cell switching request carries a target prediction instance, and the target prediction instance is determined based on the F prediction instances.

43. The method according to any one of claims 1 to 42, wherein: The measurement times corresponding to the K measurement instances and the prediction times corresponding to the F prediction instances satisfy the following time series relationship: The measurement moments corresponding to the K measurement instances are within a first time period, and the prediction moments corresponding to the F prediction instances are within a second time period, and the second time period does not overlap with the first time period; or The measurement moments corresponding to the K measurement instances are located within the K first time periods, and the prediction moments corresponding to the F prediction instances are located within the last first time period of the K first time periods.

44. A mobility prediction method, the method comprising: A network device receives K measurement instances corresponding to a first downlink reference signal set sent by a terminal, wherein the K measurement instances are obtained based on the terminal measuring the downlink reference signal in the first downlink reference signal set at K measurement times, and the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on a first model, and different prediction instances correspond to different prediction times, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; K, N, F and P are integers greater than or equal to 1.

45. The method of claim 44, wherein: The method further comprises: The network device sends first configuration information and / or second configuration information to the terminal; The first configuration information is used to configure the first downlink reference signal set; the second configuration information is used to configure the second downlink reference signal set.

46. ​​The method of claim 45, wherein: The first configuration information is further used to configure first time domain information corresponding to the first downlink reference signal set, and the first time domain information is used to determine K measurement times corresponding to the K measurement instances; and / or, The second configuration information is further used to configure second time domain information corresponding to the second downlink reference signal set, and the second time domain information is used to determine F prediction moments corresponding to the F prediction instances.

47. The method of claim 46, wherein: The first time domain information includes at least one of the following: a measurement period, a first offset, and a number K of measurement moments; and / or, The second time domain information includes at least one of the following: a prediction period, a second offset, and the number F of prediction moments.

48. A method according to any one of claims 45 to 47, wherein: The first configuration information and / or the second configuration information are related to capability information of the terminal.

49. The method according to any one of claims 45 to 48, wherein: The method further comprises: The network device receives capability information of the terminal sent by the terminal, where the capability information of the terminal includes at least one of the following: first information, where the first information is used to indicate whether the terminal supports measurement and / or prediction of time domain mobility; second information, the second information is used to indicate at least one of the following: the maximum number of cells supported for measurement by the terminal on all downlink CCs or BWPs; the maximum number of configured first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; the maximum number of first downlink reference signal subsets supported for measurement by the terminal on all downlink CCs or BWPs; The third information is used to indicate at least one of the following: the maximum number of predicted cells supported by the terminal on all downlink CCs or BWPs; the maximum number of configured second downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; the maximum number of predicted first downlink reference signal subsets supported by the terminal on all downlink CCs or BWPs; Fourth information, the fourth information is used to indicate at least one of the following: the maximum number of cells supported for measurement by the terminal on each downlink CC or BWP; the maximum number of configured first downlink reference signal subsets supported by the terminal on each downlink CC or BWP; the maximum number of first downlink reference signal subsets supported for measurement by the terminal on each downlink CC or BWP; The fifth information is used to indicate at least one of the following: the number of predicted cells supported by the terminal on each downlink CC or BWP at most; the number of configured second downlink reference signal subsets supported by the terminal on each downlink CC or BWP at most; the number of predicted second downlink reference signal subsets supported by the terminal on each downlink CC or BWP; Sixth information, where the sixth information is used to indicate a maximum number of downlink reference signals that the terminal supports measuring in each first downlink reference signal subset; seventh information, where the seventh information is used to indicate a maximum number of predicted downlink reference signals supported by the terminal in each second downlink reference signal subset; Eighth information, where the eighth information is used to indicate the number of measurement moments supported by the terminal; Ninth information, the ninth information is used to indicate the number of prediction moments supported by the terminal.

50. The method according to any one of claims 44 to 49, wherein: The K measurement instances include: N×K groups of link qualities; or, N×K indexes and N×K groups of link qualities, the N×K indexes and the N×K link qualities have a corresponding relationship; each of the N×K indexes corresponds to a cell index and a measurement time index.

51. The method according to any one of claims 44 to 50, wherein: Each of the F prediction instances includes at least one of the following: The best L cell indexes among the P cells; The best J downlink reference signal resource indexes in each best cell; The link quality corresponding to each optimal downlink reference signal resource index; L, J are positive integers.

52. The method of claim 51, wherein: Each prediction instance further includes: a prediction time index corresponding to the prediction instance.

53. A method according to any one of claims 44 to 52, wherein: The reporting manner of the K measurement instances includes at least one of the following: periodic reporting, semi-continuous reporting, and non-periodic reporting.

54. The method of claim 53, wherein: The method further comprises: The network device sends fourth configuration information to the terminal, where the fourth configuration information is used to configure a reporting period and a reporting offset corresponding to the periodic reporting or the semi-persistent reporting.

55. The method of claim 53, wherein: The method further comprises: The network device sends a third DCI to the terminal, where the third DCI is used to trigger the non-periodic reporting.

56. The method according to any one of claims 44 to 55, wherein: The reporting granularity of the K measurement instances is one measurement instance.

57. The method of claim 56, wherein: The report has a third format, and the third format includes: a measurement time index and X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

58. The method of claim 56, wherein: The report has a fourth format, and the fourth format includes: a measurement time index, N cell indexes, and X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

59. The method according to any one of claims 44 to 55, wherein: The reporting granularity of the K measurement instances is K measurement instances.

60. The method of claim 59, wherein: The report has a fifth format, the fifth format including: K groups of information, the K groups of information corresponding to the K measurement instances; each group of information in the K groups of information includes: X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

61. The method of claim 59, wherein: The report has a sixth format, the sixth format includes: N cell indexes and K group information, or the sixth format includes: N cell indexes, K measurement instance indexes and K group information; the K group information has a corresponding relationship with the K measurement instance indexes; each group of information in the K group information includes: X2 link qualities; The X2 link qualities include link qualities of M downlink reference signals of each cell in the N cells, where M is a positive integer.

62. The method according to any one of claims 57 to 61, wherein: The format of the X2 link qualities in the report adopts a differential reporting format or a non-differential reporting format.

63. The method of claim 62, wherein: For the case where the reporting granularity is one measurement instance, in the differential reporting format, The X2 link qualities are represented by a reference link quality and X2-1 differential values; or, The X2 link qualities are divided into N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

64. The method of claim 62, wherein: For the case where the reporting granularity is K measurement instances, in the differential reporting format, K×X2 link qualities are represented by a reference link quality and K×X2-1 differential values; or, The K×X2 link qualities are divided into K×N groups of link qualities, and each group of link qualities is represented by a reference link quality and M-1 differential values.

65. The method of claim 62, wherein: In the non-differential reporting format, the X2 link qualities are represented by X2 link qualities.

66. The method of any one of claims 44 to 55, wherein: The reporting granularity of the K measurement instances is the measurement result of one cell in one measurement instance.

67. The method of claim 66, wherein: The report has a sixth format, and the sixth format includes: a measurement time index, a cell index, and M link qualities; The M link qualities include link qualities of M downlink reference signals of the cell corresponding to the cell index, and M is a positive integer.

68. The method of claim 67, wherein: The format of the M link qualities in the report adopts a differential reporting format or a non-differential reporting format.

69. The method of claim 68, wherein: In the differential reporting format, the M link qualities are represented by a reference link quality and M-1 differential values.

70. The method of claim 68, wherein: In the non-differential reporting format, the M link qualities are represented by M link qualities.

71. The method of any one of claims 44 to 70, wherein: The method further comprises: The network device sends a cell switching command to the terminal, where the cell switching command is used to instruct the terminal to switch to a target cell, where the target cell is determined based on some or all of the F prediction instances.

72. The method of any one of claims 44 to 71, wherein: The measurement times corresponding to the K measurement instances and the prediction times corresponding to the F prediction instances satisfy the following time series relationship: The measurement moments corresponding to the K measurement instances are within a first time period, and the prediction moments corresponding to the F prediction instances are within a second time period, and the second time period does not overlap with the first time period; or The measurement moments corresponding to the K measurement instances are located within the K first time periods, and the prediction moments corresponding to the F prediction instances are located within the last first time period of the K first time periods.

73. A mobility prediction device, applied to a terminal, the device comprising: An acquisition unit is used to acquire K measurement instances corresponding to a first downlink reference signal set, where the K measurement instances are obtained based on the terminal measuring the downlink reference signals in the first downlink reference signal set at K measurement moments, where the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to acquire F prediction instances corresponding to a second downlink reference signal set based on a first model, where different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; K, N, F and P are integers greater than or equal to 1.

74. A mobility prediction device, applied to a network device, comprising: A receiving unit, configured to receive K measurement instances corresponding to a first downlink reference signal set sent by a terminal, wherein the K measurement instances are obtained based on the terminal measuring a downlink reference signal in the first downlink reference signal set at K measurement moments, and the first downlink reference signal set includes a first downlink reference signal subset of N cells; wherein the K measurement instances are used to obtain F prediction instances corresponding to a second downlink reference signal set based on a first model, and different prediction instances correspond to different prediction moments, and the second downlink reference signal set includes a second downlink reference signal subset of P cells; and K, N, F and P are integers greater than or equal to 1.

75. A communication device comprising: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory so that the network device executes the method as described in any one of claims 1 to 43, or the method as described in any one of claims 44 to 72.

76. A chip, comprising: A processor, configured to call and run a computer program from a memory so that a device equipped with the chip executes a method as described in any one of claims 1 to 43, or a method as described in any one of claims 44 to 72.

77. A computer-readable storage medium for storing a computer program, wherein the computer program causes a computer to execute the method of any one of claims 1 to 43, or any one of claims 44 to 72.

78. A computer program product comprising computer program instructions, the computer program instructions causing a computer to perform the method of any one of claims 1 to 43, or any one of claims 44 to 72.

79. A computer program, the computer program causing a computer to perform the method of any one of claims 1 to 43, or any one of claims 44 to 72.

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