A multi-track cooperative adaptive measurement parameter configuration method and device

Abstract

The application relates to the field of non-ground networks, and provides a multi-orbit cooperative adaptive measurement parameter configuration method and device. A network device dynamically configures measurement parameters for a terminal device according to second channel quality data, wherein the second channel quality data is predicted according to first channel quality data actually measured by the terminal device; the measurement parameters are sent to the terminal device, or the measurement parameters are sent to a network device of a cooperative orbit, and the terminal device receives the measurement parameters sent by the network device. The application can save the operation of repeatedly configuring the measurement parameters by the cooperative orbit, reduce the resource consumption of the network device for configuring the measurement parameters, ensure the continuity of the terminal device service, and further can perceive the channel change in advance and realize the timely adjustment of the measurement parameters.

Description

Technical Field

[0001] This application relates to the field of non-terrestrial networks, and in particular to a method and apparatus for configuring adaptive measurement parameters for multi-track coordination. Background Technology

[0002] In existing technical solutions, for various scenarios, including satellite communication scenarios based on multi-orbit networking, network equipment carried by satellites operating in each orbit typically configures the measurement parameters of reference signals independently based on the channel quality data actually measured by the user equipment (UE). The UE then performs channel quality measurements based on the measurement parameters configured for each orbit. Specifically, the UE measures channel quality using two mobility management reference signals: SSB-RS (Synchronization Signal Block - Reference Signal) and CSI-RS (Channel State Information - Reference Signal), thereby obtaining key channel quality indicators. The UE reports this information to the network equipment through periodic or event-driven measurements for signal scheduling and mobility management. Summary of the Invention

[0003] This application provides a method and apparatus for configuring adaptive measurement parameters in a multi-track collaborative manner, which solves the problems of untimely adjustment of measurement parameters on the network device side and high UE measurement overhead in the existing technology where each track network device independently configures measurement parameters.

[0004] To address the aforementioned technical problems, this application provides, on the one hand, a multi-track collaborative adaptive measurement parameter configuration device, applied to network equipment on each track, specifically:

[0005] The device includes a first processing unit configured to control the network device to perform the following operations:

[0006] Based on the second channel quality data, the measurement parameters are dynamically configured, wherein the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device;

[0007] The measurement parameters are sent to the terminal device, or the measurement parameters are sent to the network device of the collaborative track, and the network device of the collaborative track sends the measurement parameters to the terminal device.

[0008] A second aspect of this application provides a multi-track cooperative adaptive measurement parameter configuration device for use in a terminal device, wherein the device includes a second processing unit configured to control the terminal device to perform the following operations:

[0009] The device receives measurement parameters sent by a network device, wherein the measurement parameters are dynamically configured by the network device based on second channel quality data or obtained from a network device in a cooperative orbit, and the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device.

[0010] A third aspect of this application provides a method for configuring adaptive measurement parameters for multi-track coordination, applied to network devices on each track, wherein the method includes:

[0011] Based on the second channel quality data, the measurement parameters are dynamically configured, wherein the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device;

[0012] The measurement parameters are sent to the terminal device, or the measurement parameters are sent to the network device of the collaborative track, and the network device of the collaborative track sends the measurement parameters to the terminal device.

[0013] A fourth aspect of this application provides a method for configuring adaptive measurement parameters for multi-track coordination, applied to a terminal device, the method comprising:

[0014] The device receives measurement parameters sent by a network device, wherein the measurement parameters are dynamically configured by the network device based on second channel quality data or obtained from a network device in a cooperative orbit; the second channel quality data is predicted based on first channel quality data actually measured by the terminal device.

[0015] A fifth aspect of this application provides a network device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the aforementioned network device-side methods.

[0016] A sixth aspect of this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the aforementioned methods on the terminal device side.

[0017] A seventh aspect of this application provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer device, implements the method described in any of the foregoing embodiments.

[0018] The eighth aspect of this application provides a computer-readable program product, the computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer device, implements the method described in any of the foregoing embodiments.

[0019] In the multi-track collaborative scenario described above, the network device on one track performs the measurement parameter determination operation, sending the terminal device's measurement parameters to the network device on the collaborative track. This automatically triggers the collaborative track's network device to pre-configure the measurement parameters for the terminal device, eliminating the need for the collaborative track to repeatedly perform the measurement parameter determination operation, reducing the resource overhead of the network device in determining measurement parameters, and ensuring seamless transitions during beam switching and continuity of terminal device services. Simultaneously, based on the first channel quality data actually measured by the terminal device, the second channel quality data is predicted. Based on the second channel quality data, the measurement parameters are dynamically configured for the terminal device, enabling early detection of channel changes and timely adjustment of measurement parameters. This reduces spectrum resource consumption and signaling overhead, optimizes air interface resource configuration, and improves communication efficiency.

[0020] To make the above and other objects, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] The elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. Furthermore, in the drawings, similar reference numerals denote corresponding parts in several drawings and can be used to indicate corresponding parts used in more than one embodiment.

[0022] The accompanying drawings, which form part of the specification, are used to provide a further understanding of the embodiments of this application and illustrate the implementation methods of this application, together with the textual description, to explain the principles of this application. Obviously, the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0023] Figure 1 A structural diagram of a satellite communication system according to an embodiment of this application is shown;

[0024] Figure 2 A schematic diagram of user groups according to an embodiment of this application is shown;

[0025] Figure 3 A schematic diagram of the input-output time window of the channel prediction model according to an embodiment of this application is shown;

[0026] Figure 4A schematic diagram of distributed decision-making according to an embodiment of this application is shown;

[0027] Figure 5 A schematic diagram of an existing centralized decision-making system is shown;

[0028] Figure 6 A flowchart illustrating the channel prediction model training process according to an embodiment of this application is shown.

[0029] Figure 7 A flowchart illustrating a channel prediction model performance supervision embodiment of this application is shown;

[0030] Figure 8 A schematic diagram of the channel prediction model management framework for user groups according to an embodiment of this application is shown;

[0031] Figure 9 A flowchart illustrating the interaction process of a satellite communication system according to an embodiment of this application is shown;

[0032] Figure 10 Another flowchart illustrating the interaction process of the satellite communication system according to an embodiment of this application is shown;

[0033] Figure 11 This illustration shows a schematic diagram of the relationship between predicting second channel quality data using a channel prediction model and configuring measurement parameters according to an embodiment of this application.

[0034] Figure 12 A flowchart of an adaptive measurement parameter method for multi-track coordination on the network device side according to an embodiment of this application is shown;

[0035] Figure 13 A flowchart of an adaptive measurement parameter method for multi-track coordination on the terminal device side according to an embodiment of this application is shown;

[0036] Figure 14 Another flowchart of the adaptive measurement parameter method for multi-track coordination on the terminal device side according to an embodiment of this application is shown;

[0037] Figure 15 This invention illustrates a structural diagram of a multi-track cooperative adaptive measurement parameter device on the network device side according to an embodiment of this application;

[0038] Figure 16 This invention illustrates a structural diagram of a multi-track cooperative adaptive measurement parameter device on the terminal device side according to an embodiment of this application;

[0039] Figure 17 A schematic diagram of a terminal device according to an embodiment of this application is shown;

[0040] Figure 18 A schematic diagram of the network device configuration according to an embodiment of this application is shown.

[0041] Explanation of symbols in the attached drawings:

[0042] 101. Network equipment;

[0043] 102. Terminal equipment;

[0044] 103. Network equipment;

[0045] 301. First-time window;

[0046] 302. Second time window;

[0047] 401, 5011, 5021, 5031: Channel quality data actually measured by a single terminal device within the first time window;

[0048] 402, 5012, 5022, 5032: Channel quality data predicted by a single terminal device within the second time window;

[0049] 801. Data Collection Unit;

[0050] 802, Model Training Unit;

[0051] 803. Model Management Unit;

[0052] 804, Model Storage Unit;

[0053] 805. Model Reasoning Unit;

[0054] 1101. Channel prediction model;

[0055] 1501, First Processing Unit;

[0056] 1601, Second Processing Unit;

[0057] 1700. Terminal equipment;

[0058] 1710. Processor;

[0059] 1720. Memory;

[0060] 1730. Communication module;

[0061] 1740. Input Unit;

[0062] 1750. Monitor;

[0063] 1760. Power supply;

[0064] 1800. Network equipment;

[0065] 1810, Processor;

[0066] 1820. Memory;

[0067] 1830, Program;

[0068] 1840. Transceiver;

[0069] 1850, Antenna. Detailed Implementation

[0070] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0072] In this specification, unless otherwise stated, "and / or" describes an association between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. Furthermore, in this disclosure, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0073] In this specification, the expressions "greater than" or "less than" may be used to determine whether a specific condition is met. However, this is only for illustrative purposes and is not intended to exclude statements of "above" or "below". A condition described as "above" may be replaced by "greater than", a condition described as "below" may be replaced by "less than", and a condition described as "above and less than" may be replaced by "greater than and below". Furthermore, hereinafter, "A" to "B" represent at least one of the elements from A (inclusive) to B (inclusive).

[0074] In the embodiments of this application, the singular forms "a," "the," etc., including the plural forms, should be broadly understood as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0075] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0076] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0077] This application uses terminology used in some communication specifications (e.g., 3rd Generation Partnership Project, European Telecommunications Standards Institute, ETSI, Extensible Radio Access Network, ERAN, Open-Radio Access Network, O-RAN) to describe various embodiments, but this is merely illustrative. The various embodiments of this application can also be readily modified and applied in other communication systems.

[0078] In the embodiments of this application, communication between devices in the communication system can be carried out according to communication protocols at any stage, such as including but not limited to the following communication protocols: 1G (Generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G and 5G, New Radio (NR) and / or other currently known or future communication protocols.

[0079] For ease of understanding, the technical terms involved in the embodiments of this application will be explained below.

[0080] (1) Terminal Device: refers to a device with wireless transceiver capabilities that can cooperate with network equipment to provide communication services to users. Terminal devices can be fixed or mobile, and can also be called terminal, user equipment (UE), user terminal, mobile terminal (MT), user agent, subscriber station (SS), access terminal (AT), station or mobile station (MS), etc.

[0081] For example, terminal devices can be mobile phones, tablets, laptops, wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless communication devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, Internet of Things (IoT) devices, narrowband Internet of Things (NB-IoT) devices, ambient Internet of Things (A-IoT) devices, vehicle-to-everything (V2X) devices, devices in device-to-device communication (D2D), enhanced machine-type communication (eMTC) devices, and reduced-capacity devices. Capability (RedCap), cellular phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), machine-type communication devices, laptops, wireless modems, digital cameras, clients, handheld devices with wireless communication capabilities, in-vehicle or marine devices, etc.

[0082] In scenarios such as the Internet of Things (IoT), terminal devices can also be machines or devices used for monitoring or measurement, including but not limited to: Machine Type Communication (MTC) terminals, vehicle communication terminals, Device to Device (D2D) terminals, Machine to Machine (M2M) terminals, Very Small Aperture Terminals (VSAT), etc.

[0083] (2) Network equipment: refers to network-side equipment capable of communicating with terminal equipment. Network equipment can be located on satellites or the ground. Network equipment can also be called space base station, satellite-borne base station, satellite, satellite communication node, satellite network terminal equipment, satellite communication module, or base station, etc. This network-side equipment can also be called access network equipment or wireless access network equipment. Network-side equipment can be a base station (BTS) in a satellite-borne Global System for Mobile Communication (GSM) or Code Division Multiple Access (CDMA) communication system; a base station (NodeB, NB) in a satellite-borne Wideband Code Division Multiple Access (WCDMA) system; an evolved base station (eNB, eNodeB) in a satellite-borne LTE system; a base station in a terrestrial network or non-terrestrial network (NTN), such as a base station (gNB) in a satellite-borne 5G network; a base station in a future network (e.g., 6G) after 5G, carried by satellite; a base station in a future evolved Public Land Mobile Network (PLMN) network, carried by satellite; a Transmission Reception Point (TRP), carried by satellite; or a Cloud Radio Access Network, carried by satellite. In the context of Networks (CRAN), wireless controllers can also be satellite-borne city base stations, micro base stations, pico base stations, or femtobase stations. Base stations can also be ground-based base stations capable of satellite communication, and can be referred to as Access Points (APs), 5G nodes (5th Generation Nodes), Wireless Points, or Transmission / Reception Points (TRPs), the latter having equivalent technical meanings. Network equipment can also refer to base station equipment carried by High Altitude Platform Stations (HAPS) with loiter capabilities, such as large balloons or airships, base station equipment in Roadside Units (RSUs), or base station equipment in vehicle-to-everything (V2X) networks.Network equipment can also include broadcast transmitters, Mobile Management Entities (MMEs), gateways, servers, Radio Network Controllers (RNCs), Base Station Controllers (BSCs), and so on.

[0084] Base stations can include, but are not limited to: NodeBs (or NBs), evolved NodeBs (eNodeBs or eNBs), and 5G base stations (gNBs), IAB hosts, etc. They can also include Remote Radio Heads (RRHs), Remote Radio Units (RRUs), relays, or low-power nodes (such as femtos, picos, etc.). The term "base station" can include some or all of their functions, and each base station can provide communication coverage to a specific geographic area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0085] Both terminal devices and base station devices can perform beamforming, but the embodiments of this application are not limited to this. In some embodiments, the terminal may or may not perform beamforming. Furthermore, the base station may or may not perform beamforming. That is, only one of the terminal and the base station can perform beamforming, or neither the terminal nor the base station may perform beamforming. In this application, a beam refers to the spatial flow of signals in a wireless channel, formed by one or more antennas or antenna elements; such a formation process can be called beamforming.

[0086] Furthermore, the term "network side" or "network equipment side" refers to one side of the network, which can be a base station or include one or more network devices as described above. The term "terminal side" or "terminal equipment side" refers to the side of the user or terminal, which can be a UE or include one or more terminal devices as described above.

[0087] In the embodiments of this application, configuration / instruction refers to the network device configuring / instructing directly or indirectly through higher-layer signaling. Configuration / instruction can be achieved by introducing higher-layer parameters into the higher-layer signaling, where higher-layer parameters refer to information fields and / or information elements / information units / information cells (IEs) in the higher-layer signaling.

[0088] In the embodiments of this application, the reference signal is, for example, a synchronization signal block (PBCHBlock, SSB), a cell reference signal (CRS), a channel state information reference signal (CSI-RS), and so on.

[0089] The 3rd Generation Partnership Project Technical Report (3GPP TR) 22.870, in its Release 20 6G use cases and service requirements, clearly states that 6G networks will achieve ubiquitous connectivity through both terrestrial and non-terrestrial networks to meet specific Quality of Service (QoS) standards and ensure seamless global coverage. In non-terrestrial networks, due to the highly dynamic nature of satellites, satellite beams experience frequent channel state changes, increasing the demand for channel measurement and reporting. Furthermore, 3GPP TR 22.870 also proposes use cases for low-Earth orbit (LEO) sparse constellation deployments, requiring satellite operators to guarantee uninterrupted service to users even before the constellation is fully deployed. Multi-track coordinated channel measurement, as a potential solution to achieve this goal, has significant application value for 6G non-terrestrial networks.

[0090] In existing technical solutions, one way to measure channel quality in multi-orbit satellite communication scenarios is as follows: network equipment on each orbit independently configures the measurement parameters of the reference signal based on the channel quality data actually measured by the terminal equipment. Specifically, the terminal equipment measures channel quality using two mobility management reference signals, SSB-RS and CSI-RS, to obtain key channel quality indicators. These indicators include measured values ​​of Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference plus Noise Ratio (SINR). The terminal equipment reports the measured channel quality indicator information to the network equipment through periodic or event-driven measurements, enabling the network equipment to perform signal scheduling and mobility management.

[0091] In summary, the existing method of independently configuring measurement parameters for each track network device using reference signals has the following problems:

[0092] (1) High measurement cost

[0093] Spectrum resource overhead: Frequent reference signal measurements require a certain amount of spectrum resources and transmit power. Especially in non-terrestrial networks or high-dynamic environments, frequent reference signal measurements may increase the overall communication cost.

[0094] Signaling overhead: The reporting of measurement results requires transmission via air interface signaling. Frequent reporting of measurement results will generate additional air interface signaling overhead, leading to increased consumption of air interface resources and increased burden on the air interface.

[0095] (2) Delay and real-time issues

[0096] In non-terrestrial network scenarios, the round-trip time between satellite and ground is significant, and the high speed of satellite movement generates substantial Doppler frequency offset. This causes the measurement channel information acquired by network devices to become outdated, failing to reflect the user's actual channel quality in a timely manner. Network devices configuring measurement parameters based on outdated measurement channel data suffer from large delays and poor real-time performance, thus impacting user experience and system response speed. Furthermore, the method of independently configuring measurement parameters for each orbital network device results in untimely adjustments to measurement parameters on the network device side.

[0097] Therefore, how to achieve timely adjustment of measurement parameters of various track network devices and reduce the measurement overhead of terminal devices has become a key challenge that urgently needs to be addressed.

[0098] To address the aforementioned technical problems, this application provides a satellite communication system, such as... Figure 1 As shown, the satellite communication system includes: network device 101, terminal device 102, and network device 103. For simplicity, Figure 2 This example uses only one terminal device and two network devices, but the embodiments of this application are not limited to this. Network device 103 is a network device that works in tandem with network device 101.

[0099] In this embodiment of the application, network devices 101 and 103 and terminal device 102 can transmit existing services or services that can be implemented in the future. For example, these services may include, but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0100] For example, terminal device 102 can send data to network devices 101 and 103, such as using authorized or unauthorized transmission methods. Network devices 101 and 103 can receive data sent by one or more terminal devices 102 and send feedback information to terminal device 102, such as ACK / NACK confirmation information. Based on the feedback information, terminal device 102 can confirm the end of the transmission process, or can initiate new data transmission, or can retransmit the data.

[0101] Network devices 101 and 103 can send data to terminal device 102 using unicast, multicast, or broadcast methods. Terminal device 102 can receive data sent by one or more network devices (e.g., dual-connection or multi-connection) via the downlink or by terminal device 102 via a sidelink.

[0102] Specifically, network device 101 is configured to perform the following operations: dynamically configure measurement parameters based on second channel quality data, wherein the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device; send the measurement parameters to terminal device 102, or send the measurement parameters to network device 103 in a cooperative track, which then sends the measurement parameters to the terminal device. In practice, network device 101 first determines the channel fluctuation parameters based on the second channel quality data; then, it dynamically configures the measurement parameters for the terminal device based on the channel fluctuation parameters. In non-cooperative track scenarios, network device 101 sends the measurement parameters to terminal device 102. In cooperative track scenarios, network device 101 sends the measurement parameters to network device 103 in a cooperative track, which then sends the measurement parameters to terminal device 102. Specifically, network device 103 in a cooperative track can send the measurement parameters to terminal device 102 when the terminal device performs beam switching.

[0103] Terminal device 102 is configured to perform the following operations: receive measurement parameters sent by network device 101 or network device 103 in a cooperative orbit. Furthermore, terminal device 102 is also configured to perform channel quality measurements using a reference signal based on the measurement parameters.

[0104] Multi-orbit coordination includes at least one of the following: inter-satellite switching within the same orbital plane, and cross-orbit switching.

[0105] The first channel quality data includes the channel quality data actually measured within a first time window, and the second channel quality data includes the predicted channel quality data within a second time window. The second time window is adjacent to the first time window. For example, if the current time is t, the range of the first time window is from t-N1+1 to t, and the range of the second time window is from t to t+N2, where N1 and N2 are time units. In implementation, after each actual measurement of channel quality data for a first time window, the second channel quality data is predicted using a channel prediction model. Measurement parameters are then configured based on the predicted second channel quality data, thereby enabling dynamic configuration of measurement parameters. The channel prediction model reflects the mapping relationship between the channel quality data within the first time window and the channel quality data within the second time window.

[0106] The channel quality data includes at least one of the following: RSRP value and SINR value.

[0107] The measurement parameters include at least time-domain and / or frequency-domain measurement configurations, typically associated with the measurement period and the time-frequency density of the reference signal. In some implementations, network device 101 transmits the measurement parameters to terminal device 102 via the Radio Resource Control (RRC) layer.

[0108] Among them, the channel fluctuation parameters include at least one of the following: channel state change rate and bit error rate.

[0109] The channel state change rate calculation process includes: obtaining the channel state change rate by calculating the correlation of channel quality within the second time window, or obtaining the channel state change rate by calculating the difference of channel quality within the second time window, or obtaining the channel state change rate by calculating the variance of channel quality within the second time window.

[0110] The bit error rate calculation process includes: the channel bit error rate can be calculated using the channel quality within the second time window.

[0111] This embodiment is applicable to multi-orbit collaborative scenarios. A network device in one orbit (e.g., LEO) performs the measurement parameter determination operation, automatically triggering network devices in the collaborating orbits (e.g., MEO or GEO) to pre-configure the measurement parameters. This eliminates the need for the collaborating orbits to repeatedly perform the measurement parameter determination operation, reducing the resource overhead of network devices in determining measurement parameters, ensuring seamless switching between different orbits, and guaranteeing the continuity of terminal device services. Specifically, in sparse LEO constellation deployment use cases, for coverage blind spots existing in LEO satellites, MEO or GEO satellites obtain measurement parameter configurations in advance based on the measurement parameters transmitted by the LEO satellites, ensuring the continuity of terminal device services.

[0112] Simultaneously, based on the channel quality data actually measured by the terminal device, second channel quality data is predicted. Channel fluctuation parameters are then determined based on this second channel quality data. Measurement parameters are dynamically configured for the terminal device according to these channel fluctuation parameters. This avoids the intermediate steps of relying on real-time measurement with reference signals, as is done in traditional solutions. It reduces the system's dependence on frequent reference signal measurements, lowers the latency in obtaining measurement results, allows for early detection of channel changes, and enables timely adjustment of measurement parameters. This, in turn, reduces spectrum resource consumption and signaling overhead, optimizes air interface resource allocation, and improves communication efficiency. This embodiment is particularly effective in reducing measurement frequency and optimizing satellite link resource utilization in environments with slow channel changes.

[0113] In summary, this application can flexibly adjust measurement parameters under different orbital altitudes and different channel dynamic conditions, thereby effectively reducing spectrum resource overhead and air interface signaling overhead in resource-limited environments.

[0114] In some embodiments, network devices 101 and 103 are further configured to send measurement parameters to terminal devices associated with user groups to which the terminal devices belong.

[0115] User groups can be categorized according to at least one of the following methods: service model, terminal device type, or beamwidth (beam coverage). For example... Figure 2 The diagram shows how terminal devices are grouped into user groups based on wave positions.

[0116] In detail, terminal devices within a user group that have similar channel characteristics or resource requirements can share measurement parameters. For example, grouping by service mode means users in the same user group have similar network resource requirements and real-time requirements, enabling them to share measurement parameters. Another example is grouping by terminal type, where terminal devices of the same type have similar requirements for measurement parameter configuration; for instance, IoT devices need to conserve energy, while drones require increased measurement frequency due to their high-speed movement. Yet another example is grouping by beam position, where terminal devices under the same beam coverage are affected by similar physical environments, resulting in highly correlated channel states, allowing them to share channel prediction models.

[0117] This embodiment enables terminal devices within a user group to share measurement parameters by setting up user groups and sending measurement parameters to the terminal devices in that user group, thereby improving the configuration efficiency of measurement parameters for each terminal device.

[0118] In some embodiments, the second channel quality data is predicted using a pre-trained channel prediction model. In practice, the first channel quality data actually measured by the terminal device is input into the channel prediction model, which then predicts the second channel quality data.

[0119] The channel prediction model is trained using historical channel quality data measured by the terminal device. The input to the channel prediction model is the channel quality data actually measured by the terminal device within a first time window (first channel quality data), and the output is the channel quality data predicted by the terminal device within a second time window (second channel quality data). Figure 3 As shown, the first time window 301 and the second time window 302 maintain continuity in time. For example, the first time window 301 is N1 time units before the current time, and the second time window 302 is N2 time units after the current time. N1 and N2 are positive integers and can be set according to actual needs.

[0120] In non-terrestrial networks, the periodic motion of satellite orbits causes channel quality to be time-dependent. Therefore, by establishing a channel prediction model, it is possible to effectively learn the mapping relationship between channel data in the later time window (i.e., the second time window) and the earlier time window (i.e., the first time window), and then to quickly predict the future channel quality (i.e., the second channel quality data) based on the channel prediction model.

[0121] This embodiment uses a pre-established channel prediction model to predict channel quality data, reducing the time delay caused by traditional reference signal measurement, and can quickly predict future channel fluctuations.

[0122] Currently, 3GPP's standardization projects on Artificial Intelligence (AI) use cases only focus on CSI prediction on the terminal device side. This clarifies that the CSI prediction use case model is deployed solely on the terminal device side, and the model is trained using local data by the terminal device. However, when there are a large number of terminal devices, independently trained models lead to data redundancy and resource waste. Furthermore, independently trained models suffer from low accuracy due to limited data volume. In addition, the limited computing power, storage capacity, and battery life of terminal devices make it difficult to support complex AI model training and inference.

[0123] Furthermore, unlike terrestrial network scenarios, in non-terrestrial networks, multiple terminal devices may be illuminated within the narrow beam coverage area. To more effectively utilize the channel prediction model for channel quality prediction and address the aforementioned technical issues, in some embodiments, the network device 101 may train the channel prediction model as follows:

[0124] First, the terminal devices are grouped to obtain user groups. For example, the terminal devices can be grouped according to the service mode, terminal device type, or beam position (beam coverage range).

[0125] Then, network device 101 trains a channel prediction model for each user group. Specifically, the channel prediction model for each user group is trained using the historical actual measured channel quality data of the terminal devices related to each user group.

[0126] In this model, the channel prediction model for each user group employs distributed decision-making, such as... Figure 4 As shown, the input to the channel prediction model for each user group is the channel quality data 401 (i.e., the first channel quality data) actually measured by a single terminal device within the first time window, and the output of the channel prediction model for each user group is the channel quality data 402 (i.e., the second channel quality data) predicted by a single terminal device within the second time window. During the decision-making phase, the terminal devices associated with each user group can share the channel prediction model for that user group.

[0127] In the channel prediction model determined in this embodiment, during the model training phase, terminal devices within the same user group share channel quality data to avoid redundant training and thus improve data utilization efficiency. During the model decision phase, the shared channel prediction model trained on the network side is used to predict channel quality, which can improve the applicability and efficiency of the model.

[0128] This application employs a distributed decision-making channel prediction model, which, compared to a centralized decision-making channel prediction model (such as...),... Figure 5 As shown in the figure, the following technical effects can be achieved:

[0129] (1) Reduced computational overhead: Centralized decision-making involves input and output data covering information of terminal devices (UE1, ..., UE K) within the entire user group. The number of model parameters increases with the number of UEs in the user group, resulting in high training complexity. Distributed decision-making only considers the strategy of each UE, with relatively fewer model parameters, which can reduce the computational overhead of the model.

[0130] (2) Scalability: For centralized decision-making, when the number of UEs in a user group changes, the input and output dimensions of the model will also change, requiring relearning. For distributed decision-making, when the number of UEs in a user group changes, the input and output dimensions of the model remain unchanged, and there is no need to relearn. This allows for better adaptation to the dynamic and resource-constrained environment of non-terrestrial networks, and improves the scalability of the channel prediction model.

[0131] (3) Reduced response time: Centralized decision-making requires transmitting UE information to the network side first, and then distributing the decision to each UE after the network side completes the decision, resulting in a long decision response time. Distributed decision-making is carried out directly on the local machine, resulting in a short decision response time.

[0132] In some embodiments, such as Figure 6 As shown, the channel prediction model training process for each user group includes:

[0133] 601, Collect channel quality data actually measured by terminal devices related to each user group;

[0134] 602. Based on the channel quality data actually measured by the terminal devices related to each user group, construct a training sample set for each user group;

[0135] 603. Using the training sample sets of each user group, a prediction model is trained to obtain the channel prediction model for each user group.

[0136] Among them, the channel prediction model for each user group is used to predict the second channel quality data of the terminal equipment related to each user group.

[0137] This embodiment trains a channel prediction model for each user group using historical channel quality data from terminal devices within each user group. This enables channel quality data sharing among terminal devices within a user group, improving data utilization efficiency. Furthermore, compared to training the channel prediction model independently for each terminal device, this approach reduces the capability requirements of the terminal devices, avoids redundant training, reduces training resource overhead, and improves model training efficiency.

[0138] The channel prediction model for each user group is shared by the terminal devices entering each user group, which can better adapt to the dynamic and resource-constrained environment of non-terrestrial networks and improve the scalability of the model.

[0139] In some embodiments, such as Figure 7 As shown, after training the channel prediction model for each user group, the following steps are also included:

[0140] 701. Based on the channel quality data actually measured by all terminal devices related to each user group, construct a monitoring sample set for each user group;

[0141] 702. By using the monitoring sample set of each user group, the performance of the channel prediction model of each user group is monitored.

[0142] 703. Retrain the channel prediction model for user groups that do not meet the preset conditions. The preset conditions include, for example, that the error between the predicted channel quality data and the actual channel quality data is less than a preset threshold, which can be determined based on the actual prediction accuracy.

[0143] This embodiment monitors the performance of the channel prediction model for each user group, enabling timely updates to the channel prediction model and improving the prediction accuracy of the channel prediction model for each user group.

[0144] In some embodiments, such as Figure 8As shown, the framework of the channel prediction model for managing each user group in network device 101 includes: a data collection unit 801, a model training unit 802, a model management unit 803, a model storage unit 804, and a model inference unit 805. Among these, Figure 8 This information is derived from 3GPP TR 38.843 technical report.

[0145] The data collection unit 801 is used to collect channel quality data actually measured by all terminal devices related to each user group, including training data, monitoring data, and inference data. The training data and monitoring data are historical channel quality data actually measured by the terminal devices, such as data collected over a past period. Specifically, the training data includes a training sample set for each user group, and the monitoring data includes a monitoring sample set for each user group. The inference data is the channel quality data measured by the terminal device for the current first time window.

[0146] The model training unit 802 is used to train the prediction model using the training sample set of each user group to obtain the channel prediction model of each user group, and store the channel prediction model of each user group in the model storage unit 804.

[0147] The model management unit 803 monitors the performance of the channel prediction model for each user group using the monitoring sample set for each user group. When the performance does not meet the requirements, it sends a performance feedback / retraining request to the model training unit 802, which then retrains the channel prediction model (Updated Model) for the user group that does not meet the preset conditions. The retrained channel prediction model is then stored in the model storage unit 804. When the performance meets the requirements, it sends a model transfer / delivery request to the model storage unit 804. The model management unit 803 is also used to adjust the model, such as selecting, activating / deactivating, switching, and rolling back.

[0148] The model management unit 803 is also used to send management instructions to the model inference unit 805.

[0149] After receiving the model transfer / delivery request, the model storage unit 804 transmits the channel prediction model transfer / delivery request it stores to the model inference unit 805.

[0150] The model inference unit 805 inputs the inference data into the corresponding channel prediction model, predicts the second channel quality data, and outputs the inference result to the model management unit 803.

[0151] Based on the established channel prediction model for each user group, the second channel quality data of the terminal device can be quickly predicted.

[0152] In some embodiments, such as Figure 9 As shown, the process for determining adaptive measurement parameters for multi-track coordination includes:

[0153] (1) Network device 101 pre-trains channel prediction models for each user group;

[0154] (2) Terminal device 102 sends the actual measured channel quality data to network device 101;

[0155] (3) Network device 101 receives the channel quality data actually measured by terminal device 102; determines the first channel quality data based on the channel quality data actually measured within a predetermined range; inputs the first channel quality data into the channel prediction model of the group to which the terminal device belongs, and the second channel quality data of terminal device 102 is predicted by the channel prediction model of the group to which the terminal device belongs.

[0156] (4) Network device 101 determines the channel fluctuation parameters based on the second channel quality data;

[0157] (5) Network device 101 dynamically configures measurement parameters for terminal device 102 based on channel fluctuation parameters and triggers the network device to coordinate track to dynamically configure measurement parameters for terminal device 102;

[0158] (6a) Network device 101 sends the terminal device measurement parameters to terminal device 102;

[0159] (6b) Network device 101 sends the terminal device measurement parameters to network device 103 in the cooperative track;

[0160] (6c) The network device 103 of the collaborative track receives the measurement parameters of the terminal device;

[0161] (6d) The network device 103 of the collaborative track sends the measurement parameters of the terminal device to the terminal device 102;

[0162] (7) Terminal device 102 receives measurement parameters;

[0163] (8) The terminal device 102 performs channel quality measurement using a reference signal based on the measurement parameters.

[0164] Steps (6b) to (6d) are optional; either step (6a) or steps (6b) to (6d) can be executed. Steps (6b) to (6d) are applicable to collaborative orbit scenarios and can be executed when the terminal device performs beam switching.

[0165] This embodiment uses a network device storage channel prediction model, which can reduce the storage resource requirements of terminal devices.

[0166] The predetermined range is defined as either the number of measurements M or the measurement time T, where M and T are positive integers. Each network device is configured with its own predetermined range, and the predetermined range configured for each orbit is positively correlated with the altitude of that orbit. That is, M or T has different configurations depending on the orbit. For low-Earth orbit (LEO) satellites, the satellite's movement speed is high, and the channel may change significantly in a short period; therefore, M or T can be configured with smaller values. For medium-Earth orbit (MEO) satellites, the channel change rate is relatively low, so a moderate M or T can be set and further optimized based on the measurement results. For high-Earth orbit (HEO) satellites, the satellite's movement speed is slower, and the channel change is relatively gradual; therefore, a larger M or T can be selected.

[0167] In practice, channel quality data is predicted once for each predetermined range, channel fluctuation parameters are evaluated based on the predicted channel quality data, and measurement parameters are dynamically configured for the terminal equipment based on the channel fluctuation parameters.

[0168] By using a dynamic measurement parameter configuration scheme, different orbits can respond to channel changes more flexibly and efficiently, improve the resource utilization of satellite links, reduce the air interface transmission burden, and achieve a better communication experience.

[0169] In some implementations, network device 101 may also dynamically configure measurement parameters for the user group to which terminal device 102 belongs, and send the measurement parameters to the terminal devices related to the user group to which terminal device 102 belongs.

[0170] This implementation improves the efficiency of measurement parameter configuration by sharing the measurement parameters of one terminal device with all terminal devices in the group to which that terminal device belongs.

[0171] In some implementations, network device 101 is further configured to: when a new terminal device accesses network device 101, determine the user group to which the new terminal device belongs; and assign the new terminal device to the user group to which the new terminal device belongs.

[0172] For example, when user groups are divided by frequency band, when a new terminal device enters a certain frequency band, the new terminal device can directly use the channel prediction model of that frequency band without having to train a separate channel prediction model for the terminal device.

[0173] In some embodiments, such as Figure 10 As shown, the process for determining adaptive measurement parameters for multi-track coordination includes:

[0174] (1) Network device 101 pre-trains the channel prediction model for each user group and sends the channel prediction model for each user group to the terminal device 102 related to the corresponding user group;

[0175] When user groups are divided into bands, the channel prediction model for that band is sent to the terminal devices entering the band.

[0176] In practice, whenever the channel prediction model of a user packet is updated, the updated channel prediction model of the user packet will also be sent to the terminal equipment related to the user packet.

[0177] (2) Channel prediction model for terminal device 102 receiving user packets sent by network device;

[0178] (3) The terminal device 102 determines the first channel quality data based on the channel quality data actually measured within the predetermined range; and inputs the first channel quality data into the local channel prediction model to predict the second channel quality data.

[0179] (4) Terminal device 102 sends second channel quality data to network device 101;

[0180] (5) Network device 101 determines the channel fluctuation parameters based on the second channel quality data;

[0181] (6) Network device 101 dynamically configures measurement parameters for terminal device 102 based on channel fluctuation parameters;

[0182] (7a) Network device 101 sends the measurement parameters of the terminal device to terminal device 102;

[0183] (7b) Network device 101 sends the measurement parameters of the terminal device to network device 103 of the cooperative track;

[0184] (7c) The network device 103 of the collaborative track receives the measurement parameters of the terminal device;

[0185] (7d) The network device 103 of the collaborative track sends the measurement parameters of the terminal device to the terminal device 102;

[0186] (8) Terminal device 102 receives measurement parameters;

[0187] (9) Terminal equipment 102 performs channel quality measurement using reference signals based on measurement parameters.

[0188] Steps (7b) to (7d) are optional steps; either step (7a) or steps (7b) to (7d) can be executed. Steps (7b) to (7d) are applicable to collaborative orbit scenarios and can be executed when the terminal device performs beam switching. Figure 10 The fact that the network device 103 of the coordinated track sends measurement parameters to the terminal device 102 is only an example. In actual implementation, the network device 103 of the coordinated track can send measurement parameters to other terminal devices.

[0189] This embodiment reduces the processing load on network devices by sending the channel prediction model to the terminal device.

[0190] In the above embodiments, the channel prediction model of each user group is shared by the terminal devices entering each user group, which can better adapt to the dynamic and resource-constrained environment of non-terrestrial networks and improve the scalability of the model.

[0191] In some embodiments, network device 101 is further configured to perform the following operations: when a new terminal device accesses network device 101, determine the user group of the new terminal device; and assign the new terminal device to the user group of the new terminal device.

[0192] In some embodiments, the measurement parameters are dynamically configured based on the channel fluctuation parameters, including:

[0193] When the first condition is met, perform at least one of the following operations:

[0194] Shorten the measurement cycle;

[0195] Increase the time-frequency domain density of the reference signal;

[0196] The first condition includes at least one of the following:

[0197] The channel state change rate is greater than a first preset threshold;

[0198] The bit error rate is greater than the second preset threshold.

[0199] In some embodiments, the measurement parameters are dynamically configured based on the channel fluctuation parameters, including:

[0200] When the second condition is met, perform at least one of the following operations:

[0201] Extend the measurement cycle;

[0202] Reduce the time-frequency domain density of the reference signal;

[0203] The second condition includes at least one of the following:

[0204] The channel state change rate is less than or equal to a first preset threshold;

[0205] The bit error rate is less than or equal to the second preset threshold.

[0206] In some embodiments, the measurement parameters are dynamically configured based on the channel fluctuation parameters, including:

[0207] When the third condition is met, perform at least one of the following operations:

[0208] Extend the measurement cycle;

[0209] Reduce the time-frequency domain density of the reference signal;

[0210] The third condition is that the configuration execution operation is performed when the channel state change rate is greater than the first preset threshold and the bit error rate is less than or equal to the second preset threshold.

[0211] In some embodiments, the measurement parameters are dynamically configured based on the channel fluctuation parameters, including:

[0212] First, determine whether the channel change rate is greater than a first preset threshold;

[0213] If not (i.e., the channel change rate is less than or equal to the first preset threshold), then extend the measurement period or reduce the time-frequency domain density of the reference signal;

[0214] If so (i.e., the channel change rate is greater than the first preset threshold), then determine whether the bit error rate is greater than the second preset threshold;

[0215] If so (i.e., the bit error rate is greater than the second preset threshold), then shorten the measurement period or increase the time-frequency domain density of the reference signal;

[0216] If not (i.e., the bit error rate is less than or equal to the second preset threshold), then extend the measurement period or reduce the time-frequency domain density of the reference signal.

[0217] In some embodiments, when the channel fluctuation parameter includes the channel state change rate, the network device 101 dynamically configures measurement parameters for the terminal device based on the channel fluctuation parameter, including:

[0218] When the channel state change rate is greater than a first preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal;

[0219] When the channel state change rate is less than or equal to a first preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal.

[0220] When the channel state change rate is greater than the first preset threshold, it indicates that the channel state is changing rapidly. By shortening the measurement period or increasing the time-frequency domain density of the reference signal, the changes in signal quality can be tracked in a timely manner.

[0221] When the channel state change rate is less than or equal to the first preset threshold, it indicates that the channel state changes slowly. By extending the measurement period or reducing the time-frequency domain density of the reference signal, it is possible to reduce overhead and reduce the air interface transmission burden in satellite links with limited resources.

[0222] The first preset threshold is closely related to the satellite's orbital altitude and the channel's variation characteristics. Specifically, network devices at each altitude orbit are associated with a first preset threshold, and the first preset threshold associated with each orbit's network devices is positively correlated with the orbital altitude. For example, for low-Earth orbit (LEO) satellites, the channel state changes at a relatively high rate, and the system needs to be able to track these changes in a timely manner; therefore, the first preset threshold should be set to a lower rate of change. For medium-Earth orbit (MEO) satellites, the first preset threshold can be set within a moderate range. For high-Earth orbit (HEO) satellites, a higher first preset threshold can be tolerated, and the first preset threshold can be set to a higher rate of change.

[0223] In some embodiments, when the channel fluctuation parameter includes the bit error rate, the network device 101 dynamically configures measurement parameters for the terminal device based on the channel fluctuation parameter, including:

[0224] When the bit error rate is less than or equal to the second preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal.

[0225] When the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

[0226] If the bit error rate is less than or equal to the second preset threshold, that is, the bit error rate is kept within an acceptable range, it indicates that the communication quality is still good. By extending the measurement period or reducing the time-frequency domain density of the reference signal, the system burden can be reduced.

[0227] The setting of the second preset threshold needs to consider the different characteristics of satellite orbits. Specifically, network devices at each altitude orbit are associated with a second preset threshold, and the second preset threshold associated with each orbit's network devices is positively correlated with the altitude of that orbit. For example, for low-Earth orbit (LEO) satellites, the satellite speed is relatively fast, and the bit error rate is usually affected by rapid channel changes. To ensure communication quality, a lower second preset threshold needs to be set. For medium-Earth orbit (MEO) satellites, the satellite speed is moderate, and channel changes are slower than for LEO satellites. The second preset threshold can be appropriately set at a medium level to cope with the relatively stable channel quality changes of MEO satellites. For high-Earth orbit (HEO) satellites, the signal propagation path is long, channel fluctuations are slow, and bit error rate fluctuations are small. Therefore, a higher second preset threshold can be tolerated.

[0228] In some embodiments, when the channel fluctuation parameters include the channel state change rate and the bit error rate, the network device 101 dynamically configures measurement parameters for the terminal device based on the channel fluctuation parameters, including:

[0229] When the channel state change rate is greater than the first preset threshold and the bit error rate is less than or equal to the second preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal;

[0230] When the channel state change rate is greater than the first preset threshold and the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

[0231] The first preset threshold is closely related to the altitude of the satellite orbit and the variation characteristics of the channel. Specifically, network devices at each altitude orbit are associated with a first preset threshold, and the first preset threshold associated with each network device on each orbit is positively correlated with the altitude of that orbit.

[0232] The setting of the second preset threshold needs to take into account the different characteristics of satellite orbits. Specifically, network devices at each altitude orbit are associated with a second preset threshold, and the second preset threshold associated with each network device at each orbit is positively correlated with the altitude of each orbit.

[0233] In some embodiments, the relationship between dynamically configured measurement parameters and model predictions is as follows: Figure 11As shown, the channel quality data (i.e., the first channel quality data) measured within the first time window of the predetermined range (the number of measurements M or the measurement time T) of the terminal device is first input into the channel prediction model 1101 of the packet to which the terminal device belongs, and the second channel quality data is predicted. Then, the channel fluctuation parameters are calculated based on the second channel quality data, and the channel fluctuation situation is judged based on the channel fluctuation parameters. Next, the measurement parameters are dynamically configured according to the channel fluctuation situation, and the terminal device performs channel quality measurement according to the measurement parameters. After multiple measurements, the above process of using the channel prediction model to perform prediction and subsequent processes is repeated.

[0234] In this embodiment, the channel prediction model takes as input real channel quality measurements over a historical period and outputs predicted channel quality values ​​for a future period. This allows for advance prediction of channel fluctuations, enabling pre-configuration of measurement parameters and reducing the latency of real-time reference signal measurement and reporting. By dynamically configuring measurement parameters using predicted channel quality values, the measurement period can be extended or the reference signal density reduced, thereby decreasing spectrum resource consumption and signaling overhead, optimizing air interface resource allocation, and improving communication efficiency. The end-to-end channel prediction achieved through the channel prediction model reduces the latency associated with traditional reference signal measurements, enhancing the user experience.

[0235] In some embodiments, an adaptive measurement parameter configuration method for multi-track coordination applied to the network device side is also provided, such as... Figure 12 As shown, it includes:

[0236] 1201. Based on the second channel quality data, the measurement parameters are dynamically configured, wherein the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device.

[0237] 1202, send measurement parameters to the terminal device, or send measurement parameters to the network device of the collaborative track, and the network device of the collaborative track sends the measurement parameters to the terminal device.

[0238] The first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window.

[0239] Multi-orbit coordination includes at least one of the following: inter-satellite switching within the same orbital plane, and cross-orbit switching.

[0240] Before step 1201 is executed, the second channel quality data of the terminal device is first acquired. Step 1201 includes: determining the channel fluctuation parameters based on the second channel quality data; and dynamically configuring measurement parameters for the terminal device based on the channel fluctuation parameters.

[0241] During step 1202, the measurement parameters can be sent to the terminal device through the Radio Resource Control (RRC) layer.

[0242] In this embodiment, a network device on one track performs the measurement parameter determination operation, automatically triggering the network device on the coordinating track to pre-configure the measurement parameters. This eliminates the need for the coordinating track to repeatedly perform the measurement parameter determination operation, reducing the resource overhead of the network device in determining the measurement parameters and ensuring the continuity of terminal device services. Simultaneously, based on the first channel quality data actually measured by the terminal device, second channel quality data is predicted. Measurement parameters are then dynamically configured for the terminal device based on the second channel quality data, enabling early detection of channel changes and timely adjustment of measurement parameters. This reduces spectrum resource consumption and signaling overhead, optimizes air interface resource allocation, and improves communication efficiency.

[0243] In some embodiments, the adaptive measurement parameter configuration method for multi-track coordination applied to the network device side further includes: sending the measurement parameters to terminal devices related to the user group to which the terminal device belongs. In some embodiments, user groups are divided according to at least one of the following methods: service mode; terminal device type; waveform.

[0244] In some embodiments, the adaptive measurement parameter configuration method for multi-track coordination applied to the network device side further includes:

[0245] Receive the first channel quality data actually measured and sent by the terminal device;

[0246] The first channel quality data is input into the channel prediction model of the packet to which the terminal device belongs, and the second channel quality data of the terminal device is predicted.

[0247] Among them, the channel prediction model of each user group reflects the mapping relationship between the channel quality data in the first time window and the channel quality data in the second time window.

[0248] In some embodiments, the channel prediction model training process for each user group includes:

[0249] Collect channel quality data actually measured by terminal devices related to each user group;

[0250] Based on the channel quality data actually measured by the terminal devices related to each user group, a training sample set for each user group is constructed.

[0251] By using the training sample sets of each user group, prediction models are trained to obtain the channel prediction models for each user group.

[0252] In some embodiments, after training the channel quality prediction model for each user group, the performance of the channel quality prediction model for each user group is monitored in real time, and the model is updated based on the monitored performance, specifically including:

[0253] Based on the channel quality data actually measured by the terminal devices related to each user group, a monitoring sample set for each user group is constructed.

[0254] The performance of the channel prediction model for each user group is monitored using the monitoring sample set of each user group.

[0255] Retrain the channel prediction model for user groups that do not meet the preset conditions.

[0256] In some embodiments, when a new terminal device accesses a network device, the user group to which the new terminal device belongs is first determined; then the new terminal device is assigned to the user group to which it belongs.

[0257] In some embodiments, the adaptive measurement parameter configuration method for multi-track coordination applied to the network device side further includes:

[0258] Receive the second channel quality data sent by the terminal device;

[0259] The second channel quality data is obtained by the terminal device by inputting the first channel quality data actually measured by the terminal device into the channel prediction model stored locally.

[0260] The channel prediction model on the terminal device side is trained and sent by the network device. Specifically, after the network device trains or updates the channel prediction model for each user group, it sends the channel prediction model for each user group to the terminal device related to each user group. The channel prediction model reflects the mapping relationship between channel quality data within the first time window and channel quality data within the second time window.

[0261] In some embodiments, the channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate. The measurement parameters include at least one of the following: measurement period, reference signal time-frequency domain density.

[0262] In some implementations, measurement parameters are dynamically configured for the terminal device based on channel fluctuation parameters, including:

[0263] When the channel state change rate is greater than a first preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal;

[0264] When the channel state change rate is less than or equal to a first preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal.

[0265] In some implementations, measurement parameters are dynamically configured for the terminal device based on channel fluctuation parameters, including:

[0266] When the bit error rate is less than or equal to the second preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal.

[0267] When the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

[0268] In some implementations, measurement parameters are dynamically configured for the terminal device based on channel fluctuation parameters, including:

[0269] When the channel state change rate is greater than the first preset threshold and the bit error rate is less than or equal to the second preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal;

[0270] When the channel state change rate is greater than the first preset threshold and the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

[0271] In the above embodiments, the network devices of each height track are associated with a first preset threshold, and the first preset threshold associated with the network devices of each track is positively correlated with the height of each track.

[0272] Each network device on each altitude track is associated with a second preset threshold, and the second preset threshold associated with each network device on each track is positively correlated with the altitude of each track.

[0273] The above description only covers the steps or processes relevant to this application, but this application is not limited thereto. The methods in the embodiments of this application may also include other steps or processes, and for details of these steps or processes, please refer to related technologies.

[0274] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0275] In some embodiments, an adaptive measurement parameter configuration method for multi-track coordination applied to the terminal device side is also provided, such as... Figure 13 As shown, it includes:

[0276] 1301, Receive measurement parameters sent by the network device, wherein the measurement parameters are dynamically configured by the network device based on the second channel quality data or obtained from the network device in the cooperative track, and the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device.

[0277] 1302. Based on the measurement parameters, channel quality is measured using a reference signal.

[0278] The first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window.

[0279] The measurement parameters are dynamically configured based on the channel fluctuation parameters, which are determined based on the second channel quality data.

[0280] Among them, the channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate;

[0281] The measurement parameters include at least one of the following: measurement period, time-frequency domain density of the reference signal.

[0282] In some embodiments, the adaptive measurement parameter configuration method for multi-track coordination applied to the terminal device side further includes:

[0283] Send first channel quality data to the network device, so that the network device can input the first channel quality data into the channel prediction model of the packet to which the terminal device belongs, and predict the second channel quality data of the terminal device.

[0284] Among them, the channel prediction model of each user group reflects the mapping relationship between the channel quality data in the first time window and the channel quality data in the second time window.

[0285] In some embodiments, another adaptive measurement parameter configuration method for multi-track coordination applied to the terminal device side is also provided, such as... Figure 14 As shown, it includes:

[0286] 1401, Channel prediction model for receiving user packets sent by network devices;

[0287] 1402, The first channel quality data measured in actual measurements is input into the channel prediction model to predict the second channel quality data;

[0288] 1403, Send the second channel quality data to the network device;

[0289] 1404, Receive measurement parameters sent by the network device or the network device of the cooperative track, wherein the measurement parameters are configured by the network device based on the second channel quality data;

[0290] 1405. Based on the measurement parameters, channel quality is measured using a reference signal.

[0291] Users are grouped according to at least one of the following methods:

[0292] Business model;

[0293] Terminal device type;

[0294] Wave position.

[0295] The above description only covers the steps or processes relevant to this application, but this application is not limited thereto. The methods in the embodiments of this application may also include other steps or processes, and for details of these steps or processes, please refer to related technologies.

[0296] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0297] In some embodiments, an adaptive measurement parameter configuration device for multi-track coordination is also provided for network device side of each track. This device may be, for example, a network device, or one or more components or parts configured within the network device.

[0298] Figure 15 This is a schematic diagram of an adaptive measurement parameter configuration device for multi-track coordination applied to the network device side of each track according to an embodiment of this application. Since the principle of the adaptive measurement parameter configuration device for multi-track coordination applied to the network device side of each track is the same as that of the adaptive measurement parameter configuration method for multi-track coordination applied to the network device side of each track, its specific implementation can refer to the foregoing embodiment, and the contents that are the same will not be repeated.

[0299] like Figure 15 As shown, the adaptive measurement parameter configuration device for multi-track coordination applied to the network device side of each track includes: a first processing unit 1501, which is configured to control the network device to perform the following operations:

[0300] Based on the second channel quality data, the measurement parameters are configured, wherein the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device;

[0301] The measurement parameters are sent to the terminal device, or the measurement parameters are sent to the network device of the collaborative track, and the network device of the collaborative track sends the measurement parameters to the terminal device.

[0302] The configuration of measurement parameters based on the second channel quality data includes: determining the channel fluctuation parameters based on the second channel quality data; and dynamically configuring the measurement parameters based on the channel fluctuation parameters.

[0303] The channel quality data includes at least one of the following: RSRP value and SINR value.

[0304] The measurement parameters may include the measurement period, or the time-frequency domain density of a reference signal, or both the measurement period and the time-frequency domain density of a reference signal. In some implementations, the network device dynamically configures the measurement parameters through the RRC layer.

[0305] Among them, the channel fluctuation parameters include at least one of the following: channel state change rate and bit error rate.

[0306] The first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window.

[0307] In some embodiments, the operation further includes sending measurement parameters to a terminal device associated with a user group to which the terminal device belongs.

[0308] Users are grouped according to at least one of the following methods: business model; terminal device type; wave position.

[0309] In some embodiments, the operation further includes:

[0310] Receive the first channel quality data actually measured and sent by the terminal device;

[0311] The first channel quality data is input into the channel prediction model of the packet to which the terminal device belongs, and the second channel quality data of the terminal device is predicted.

[0312] Among them, the channel prediction model of each user group reflects the mapping relationship between the channel quality data in the first time window and the channel quality data in the second time window.

[0313] In some embodiments, the operation further includes:

[0314] Collect channel quality data actually measured by terminal devices related to each user group;

[0315] Based on the channel quality data actually measured by the terminal devices related to each user group, a training sample set for each user group is constructed.

[0316] By using the training sample sets of each user group, prediction models are trained to obtain the channel prediction models for each user group.

[0317] In some embodiments, the operation further includes:

[0318] Based on the channel quality data actually measured by the terminal devices related to each user group, a monitoring sample set for each user group is constructed.

[0319] The performance of the channel prediction model for each user group is monitored using the monitoring sample set of each user group.

[0320] Retrain the channel prediction model for user groups that do not meet the preset conditions.

[0321] In some embodiments, the operation further includes:

[0322] When a new terminal device connects to a network device, determine the user group to which the new terminal device belongs;

[0323] The new terminal device is assigned to the user group to which it belongs.

[0324] In some embodiments, the operation further includes:

[0325] Receive the second channel quality data sent by the terminal device;

[0326] The second channel quality data is obtained by the terminal device by inputting the first channel quality data actually measured by the terminal device into the channel prediction model stored locally;

[0327] The channel prediction model reflects the mapping relationship between channel quality data within the first time window and channel quality data within the second time window.

[0328] In some embodiments, the operation further includes:

[0329] Channel prediction models for each user group are trained using historical channel quality data measured by terminal devices related to each user group.

[0330] Send the channel prediction model for each user group to the terminal device associated with each user group.

[0331] In some embodiments, the operation further includes:

[0332] Based on the channel quality data actually measured by the terminal devices related to each user group, a monitoring sample set for each user group is constructed.

[0333] The performance of the channel prediction model for each user group is monitored using the monitoring sample set of each user group.

[0334] Retrain the channel prediction model for user groups that do not meet the preset conditions;

[0335] The retrained channel prediction model for user packets is sent to the user packet-related terminal equipment.

[0336] In some embodiments, the first processing unit 1501 dynamically configures measurement parameters for the terminal device based on the second channel quality data, including:

[0337] Based on the second channel quality data, determine the channel fluctuation parameters;

[0338] Measurement parameters are dynamically configured based on channel fluctuation parameters.

[0339] Among them, the channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate; the measurement parameters include at least one of the following: measurement period, reference signal time-frequency domain density.

[0340] In some embodiments, when the channel fluctuation parameter includes the channel state change rate, the measurement parameters are configured based on the channel fluctuation parameter, including:

[0341] When the channel state change rate is greater than a first preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal;

[0342] When the channel state change rate is less than or equal to a first preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal.

[0343] In some embodiments, when the channel fluctuation parameter includes the bit error rate, the measurement parameters are dynamically configured based on the channel fluctuation parameter, including:

[0344] When the bit error rate is less than or equal to the second preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal.

[0345] When the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

[0346] In some embodiments, when the channel fluctuation parameters include the channel state change rate and the bit error rate, the measurement parameters are dynamically configured for the terminal device based on the channel fluctuation parameters, including:

[0347] When the channel state change rate is greater than the first preset threshold and the bit error rate is less than or equal to the second preset threshold, perform at least one of the following operations: extend the measurement period; reduce the time-frequency domain density of the reference signal;

[0348] When the channel state change rate is greater than the first preset threshold and the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

[0349] In the above embodiments, the network devices of each altitude track are associated with a first preset threshold, and the first preset threshold associated with each track's network device is positively correlated with the altitude of each track. The network devices of each altitude track are also associated with a second preset threshold, and the second preset threshold associated with each track's network device is positively correlated with the altitude of each track.

[0350] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The adaptive measurement parameter configuration device for multi-track coordination applied to the network device side of each track in the embodiments of this application may also include other components or modules. For details regarding these components or modules, please refer to related technologies.

[0351] In addition, for the sake of simplicity, Figure 15 This is merely an illustrative example; however, those skilled in the art should understand that various related technologies, such as bus connections, can be employed. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit the scope of the invention.

[0352] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0353] In some embodiments, an adaptive measurement parameter configuration device for multi-track coordination applied to a terminal device is also provided. This device may be, for example, a terminal device, or one or more components or parts configured on the terminal device.

[0354] Figure 16 This paper illustrates a structural diagram of an adaptive measurement parameter device for multi-track coordination on the terminal device side according to an embodiment of this application. Since the principle of this adaptive measurement parameter configuration device for multi-track coordination on the terminal device side is the same as the method of the adaptive measurement parameter configuration method for multi-track coordination on the terminal device side, its specific implementation can refer to the embodiments of the aforementioned method, and the contents that are the same will not be repeated.

[0355] Specifically, such as Figure 16 As shown, the adaptive measurement parameter configuration device for multi-track coordination applied to the terminal device side includes: a second processing unit 1601, which is configured to control the terminal device to perform the following operations:

[0356] The system receives measurement parameters sent by network devices, wherein the measurement parameters are configured by the network devices based on second channel quality data or obtained from network devices in a cooperative orbit, and the second channel quality data is predicted based on the first channel quality data actually measured by the terminal devices.

[0357] The first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window.

[0358] In some embodiments, the measurement parameters are dynamically configured based on channel fluctuation parameters, which are determined based on the second channel quality data.

[0359] The channel fluctuation parameters include at least one of the following: channel state change rate and bit error rate. The measurement parameters include at least one of the following: measurement period and time-frequency domain density of the reference signal.

[0360] In some embodiments, the operation further includes: performing channel quality measurements using a reference signal based on measurement parameters.

[0361] In some embodiments, the operation further includes:

[0362] Send the first channel quality data to the network device, so that the network device can input the first channel quality data into the channel prediction model of the packet to which the terminal device belongs, and predict the second channel quality data of the terminal device.

[0363] Among them, the channel prediction model of each user group reflects the mapping relationship between the channel quality data in the first time window and the channel quality data in the second time window.

[0364] In some embodiments, the following operations are also performed:

[0365] The first channel quality data is input into the locally stored channel prediction model to predict the second channel quality data; wherein, the channel prediction model reflects the mapping relationship between the channel quality data in the first time window and the channel quality data in the second time window;

[0366] Send second channel quality data to the network device;

[0367] The network device sends configured measurement parameters, which are obtained by the network device based on the second channel quality data of the terminal device.

[0368] Channel quality is measured using a reference signal based on the measured parameters.

[0369] In some embodiments, the operation further includes:

[0370] A channel quality prediction model for user packets belonging to terminal devices transmitted by network equipment. The user packets are categorized according to at least one of the following methods: service mode; terminal device type; bandwidth.

[0371] In addition, for the sake of simplicity, Figure 16 The diagram only exemplifies the connection relationships or signal flow between various components or modules; however, those skilled in the art should understand that various related technologies, such as bus connections, can be employed. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not impose any limitations on this.

[0372] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0373] Through the above technical solution, this application can achieve the following technical effects:

[0374] (1) Reduced system overhead: Measurement parameters are flexibly configured according to the channel state change rate and bit error rate, which effectively avoids frequent measurements when the channel is stable. In addition, by using an AI model for channel prediction, the system can reduce its dependence on real-time reference signal measurements, further reducing spectrum resource overhead and air interface signaling transmission overhead.

[0375] (2) Reduce the delay in reporting measurement results: Under the configured measurement parameters, each track predicts channel quality data through AI model, avoiding real-time measurement through reference signal, reducing the delay caused by intermediate links, and reducing the delay in reporting measurement results.

[0376] (3) Reduce model overhead: By grouping users according to different partitioning rules, a shared channel prediction model can be trained on the network side for users in the same group, which can reduce the resource consumption of repeated training and effectively reduce the computational overhead of channel prediction model training and inference.

[0377] (4) Achieving distributed decision-making: Distributed decision-making in non-terrestrial networks avoids the excessive dependence of existing centralized decision-making on network-side computing resources, has a short decision response time, can better adapt to the dynamic environment of non-terrestrial networks, significantly optimizes resource utilization and model scalability, and is more flexible.

[0378] This application also provides a terminal device, but the application is not limited thereto and may also include other devices.

[0379] Figure 17 A schematic diagram of a terminal device according to an embodiment of this application is shown. Figure 17 As shown, terminal device 1700 may include processor 1710 and memory 1720; memory 1720 stores data and programs and is coupled to processor 1710. It is worth noting that this figure is exemplary; other types of structures may be used to supplement or replace this structure to implement telecommunications functions or other functions.

[0380] For example, the processor 1710 can be configured to execute a program to implement an adaptive measurement parameter configuration method for multi-track coordination applied to the terminal device side.

[0381] like Figure 17 As shown, the terminal device 1700 may further include: a communication module 1730, an input unit 1740, a display 1750, and a power supply 1760. The functions of these components are similar to those in the prior art and will not be described in detail here. It is worth noting that the terminal device 1700 is not necessarily required to include these components. Figure 17 All of the components shown are not essential; furthermore, the terminal device 1700 may also include... Figure 17 For components not shown, please refer to existing technologies.

[0382] This application also provides a network device, such as a base station, but this application is not limited to this and may also include other network devices.

[0383] Figure 18 A schematic diagram illustrating the configuration of a network device according to an embodiment of this application is shown. Figure 18 As shown, the network device 1800 may include a processor 1810 (e.g., a central processing unit CPU) and a memory 1820; the memory 1820 is coupled to the processor 1810. The memory 1820 can store various data; it also stores an information processing program 1830, and executes the program 1830 under the control of the processor 1810.

[0384] For example, the processor 1810 can be configured to execute a program to implement the above-described adaptive measurement parameter configuration method for multi-track coordination applied to the network device side.

[0385] In addition, such as Figure 18 As shown, network device 1800 may also include: transceiver 1840 and antenna 1850, etc.; the functions of the above components are similar to those in the prior art, and will not be described in detail here. It is worth noting that network device 1800 is not necessarily required to include... Figure 18 All components shown; in addition, network device 1800 may also include Figure 18 For components not shown, please refer to existing technologies.

[0386] This application also provides a computer-readable program product, which includes a computer program that, when executed by a processor of a computer device, implements the aforementioned adaptive measurement parameter configuration method for multi-track coordination on the network device side.

[0387] This application also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer device, performs the aforementioned network device-side multi-track cooperative adaptive measurement parameter configuration method.

[0388] This application also provides a computer-readable program product, which includes a computer program that, when executed by a processor of a computer device, implements the aforementioned adaptive measurement parameter configuration method for multi-track coordination on the terminal device side.

[0389] This application also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer device, performs the aforementioned multi-track cooperative adaptive measurement parameter configuration method on the terminal device side.

[0390] The apparatus and methods described above in this application can be implemented in hardware or in combination with software. This application relates to a computer-readable program that, when executed by a logic component, enables the logic component to implement the apparatus or components described above, or to implement the various methods or steps described above. Logic components include, for example, field-programmable logic devices (FPGAs), microprocessors, and processors used in computers. This application also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, and flash memory.

[0391] The methods / apparatus described in conjunction with the embodiments of this application can be directly embodied in hardware, software modules executed by a processor, or a combination of both. For example, one or more and / or combinations of one or more functional block diagrams shown in the figures can correspond to various software modules in a computer program flow, or to various hardware modules. These software modules can correspond to the various steps shown in the figures, respectively. These hardware modules can be implemented, for example, using a field-programmable gate array (FPGA) to embed these software modules.

[0392] The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to the processor, enabling the processor to read information from and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and storage medium can reside in an ASIC. The software module can be stored in the memory of a mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a high-capacity MEGA-SIM card or a high-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the high-capacity flash memory device.

[0393] One or more and / or one or more combinations of functional blocks described in the accompanying drawings can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described herein. One or more and / or one or more combinations of functional blocks described in the accompanying drawings can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0394] The present application has been described above with reference to specific embodiments. However, those skilled in the art should understand that these descriptions are exemplary and not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on its spirit and principles, and these modifications and variations are also within the scope of the present application.

Claims

1. A multi-track collaborative adaptive measurement parameter configuration device, applied to network equipment on each track, wherein, The device includes a first processing unit configured to control the network device to perform the following operations: The system receives first channel quality data actually measured by a terminal device; inputs the first channel quality data into a channel prediction model for user packets to predict second channel quality data for the terminal device; or, it receives second channel quality data sent by the terminal device, which is predicted by the terminal device by inputting the first channel quality data actually measured into a locally stored channel prediction model. Wherein, the first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window; Among them, the channel prediction model reflects the mapping relationship between channel quality data in the first time window and channel quality data in the second time window; Dynamically configure measurement parameters based on the second channel quality data; The measurement parameters are sent to the terminal device, or the measurement parameters are sent to the network device of the collaborative track, and the network device of the collaborative track sends the measurement parameters to the terminal device.

2. The apparatus of claim 1, wherein, The operation also includes: The measurement parameters are sent to the terminal devices associated with the user group to which the terminal device belongs.

3. The apparatus of claim 2, wherein, The user groups are divided according to at least one of the following methods: Business model; Terminal device type; Wave position.

4. The apparatus according to any one of claims 1 to 3, wherein, The operation also includes: Send the channel prediction model for each user group to the terminal device associated with each user group.

5. The apparatus of claim 1, wherein, The step of dynamically configuring measurement parameters based on the second channel quality data includes: Based on the second channel quality data, determine the channel fluctuation parameters; The measurement parameters are dynamically configured based on the channel fluctuation parameters.

6. The apparatus of claim 5, wherein, The channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate; The measurement parameters include at least one of the following: measurement period, time-frequency domain density of reference signal.

7. The apparatus of claim 6, wherein, The step of dynamically configuring measurement parameters based on the channel fluctuation parameters includes: When the channel state change rate is greater than a first preset threshold, at least one of the following operations is performed: shortening the measurement period; increasing the time-frequency domain density of the reference signal; When the channel state change rate is less than or equal to a first preset threshold, at least one of the following operations is performed: extending the measurement period; reducing the time-frequency domain density of the reference signal.

8. The apparatus of claim 6, wherein, The step of dynamically configuring measurement parameters based on the channel fluctuation parameters includes: When the bit error rate is less than or equal to the second preset threshold, at least one of the following operations is performed: extending the measurement period; reducing the time-frequency domain density of the reference signal; When the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

9. The apparatus of claim 6, wherein, The step of dynamically configuring measurement parameters based on the channel fluctuation parameters includes: When the channel state change rate is greater than a first preset threshold and the bit error rate is less than or equal to a second preset threshold, at least one of the following operations is performed: extending the measurement period; reducing the time-frequency domain density of the reference signal; When the channel state change rate is greater than a first preset threshold and the bit error rate is greater than a second preset threshold, at least one of the following operations is performed: shorten the measurement period; increase the time-frequency domain density of the reference signal.

10. The apparatus of claim 7 or 9, wherein, The first preset threshold for network device association in each track is positively correlated with the height of each track.

11. The apparatus of claim 8 or 9, wherein, The second preset threshold associated with the network devices of each track is positively correlated with the height of each track.

12. The apparatus of claim 1, wherein, The multi-orbit coordination includes: inter-satellite switching within the same orbital plane and cross-orbit switching.

13. The apparatus of claim 1, wherein, Sending the measurement parameters to the terminal device includes: The measurement parameters are sent to the terminal device via the Radio Resource Control (RRC) layer.

14. A multi-track coordinated adaptive measurement parameter configuration device, applied to a terminal device, wherein, The device includes a second processing unit configured to control the terminal device to perform the following operations: Send first channel quality data to the network device, so that the network device can input the first channel quality data into the channel prediction model of user packets to predict the second channel quality data of the terminal device; or, input the first channel quality data into the locally stored channel prediction model to predict the second channel quality data, and send the second channel quality data to the network device. Wherein, the first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window; Among them, the channel prediction model reflects the mapping relationship between channel quality data in the first time window and channel quality data in the second time window; The network device receives measurement parameters sent by the network device, wherein the measurement parameters are dynamically configured by the network device based on the second channel quality data or obtained from the network device in the cooperative orbit.

15. The apparatus of claim 14, wherein, The operation also includes: Channel quality is measured using a reference signal based on the measured parameters.

16. The apparatus of claim 14, wherein, The operation also includes: The network device receives the channel quality prediction model of the user group to which the terminal device belongs, sent by the network device.

17. The apparatus of claim 14 or 16, wherein, The user groups are divided according to at least one of the following methods: Business model; Terminal device type; Wave position.

18. The apparatus of claim 14, wherein, The measurement parameters are dynamically configured based on the channel fluctuation parameters, which are determined based on the second channel quality data.

19. The apparatus of claim 18, wherein, The channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate; The measurement parameters include at least one of the following: measurement period, time-frequency domain density of reference signal.

20. A multi-track collaborative adaptive measurement parameter configuration method, applied to network devices on each track, wherein, The method includes: The system receives first channel quality data actually measured by a terminal device; inputs the first channel quality data into a channel prediction model for user packets to predict second channel quality data for the terminal device; or, it receives second channel quality data sent by the terminal device, which is predicted by the terminal device by inputting the first channel quality data actually measured into a locally stored channel prediction model. Wherein, the first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window; Among them, the channel prediction model reflects the mapping relationship between channel quality data in the first time window and channel quality data in the second time window; Based on the second channel quality data, the measurement parameters are dynamically configured, wherein the second channel quality data is predicted based on the first channel quality data actually measured by the terminal device; The measurement parameters are sent to the terminal device, or the measurement parameters are sent to the network device of the collaborative track, and the network device of the collaborative track sends the measurement parameters to the terminal device.

21. The method of claim 20, wherein, The method further includes: The measurement parameters are sent to the terminal devices associated with the user group to which the terminal device belongs.

22. The method of claim 21, wherein, The user groups are divided according to at least one of the following methods: Business model; Terminal device type; Wave position.

23. The method of claim 20, wherein, The method further includes: The channel prediction model for each user group is sent to the terminal device associated with each user group.

24. The method of claim 20, wherein, The step of dynamically configuring measurement parameters based on the second channel quality data includes: Based on the second channel quality data, determine the channel fluctuation parameters; The measurement parameters are dynamically configured based on the channel fluctuation parameters.

25. The method of claim 24, wherein, The channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate; The measurement parameters include at least one of the following: measurement period, time-frequency domain density of reference signal.

26. The method of claim 25, wherein, The step of dynamically configuring measurement parameters based on the channel fluctuation parameters includes: When the channel state change rate is greater than a first preset threshold, at least one of the following operations is performed: shortening the measurement period; increasing the time-frequency domain density of the reference signal; When the channel state change rate is less than or equal to a first preset threshold, at least one of the following operations is performed: extending the measurement period; reducing the time-frequency domain density of the reference signal.

27. The method of claim 25, wherein, The step of dynamically configuring measurement parameters based on the channel fluctuation parameters includes: When the bit error rate is less than or equal to the second preset threshold, at least one of the following operations is performed: extending the measurement period; reducing the time-frequency domain density of the reference signal; When the bit error rate is greater than the second preset threshold, perform at least one of the following operations: shorten the measurement period; increase the time-frequency domain density of the reference signal.

28. The method of claim 25, wherein, The step of dynamically configuring measurement parameters based on the channel fluctuation parameters includes: When the channel state change rate is greater than a first preset threshold and the bit error rate is less than or equal to a second preset threshold, at least one of the following operations is performed: extending the measurement period; reducing the time-frequency domain density of the reference signal; When the channel state change rate is greater than a first preset threshold and the bit error rate is greater than a second preset threshold, at least one of the following operations is performed: shorten the measurement period; increase the time-frequency domain density of the reference signal.

29. The method of claim 20, wherein, Sending the measurement parameters to the terminal device includes: The measurement parameters are sent to the terminal device via the Radio Resource Control (RRC) layer.

30. A multi-track coordinated adaptive measurement parameter configuration method, applied to a terminal device, the method comprising: Send first channel quality data to the network device, so that the network device can input the first channel quality data into the channel prediction model of user packets to predict the second channel quality data of the terminal device; or, input the first channel quality data into the locally stored channel prediction model to predict the second channel quality data, and send the second channel quality data to the network device. Wherein, the first channel quality data includes the channel quality data actually measured within the first time window, and the second channel quality data includes the channel quality data predicted within the second time window; Among them, the channel prediction model reflects the mapping relationship between channel quality data in the first time window and channel quality data in the second time window; The network device receives measurement parameters sent by the network device, wherein the measurement parameters are dynamically configured by the network device based on the second channel quality data or obtained from the network device in the cooperative orbit.

31. The method of claim 30, wherein, The method further includes: Channel quality is measured using a reference signal based on the measured parameters.

32. The method of claim 30, wherein, The method further includes: The network device receives the channel quality prediction model of the user group to which the terminal device belongs, sent by the network device.

33. The method of claim 30 or 32, wherein, The user groups are divided according to at least one of the following methods: Business model; Terminal device type; Wave position.

34. The method of claim 30, wherein, The measurement parameters are dynamically configured based on the channel fluctuation parameters, which are determined based on the second channel quality data.

35. The method of claim 34, wherein, The channel fluctuation parameters include at least one of the following: channel state change rate, bit error rate; The measurement parameters include at least one of the following: measurement period, time-frequency domain density of reference signal.

36. A network device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the method according to any one of claims 20 to 29.

37. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the method according to any one of claims 30 to 35.

38. A computer-readable storage medium storing a computer program, wherein, When the computer program is executed by the processor of a computer device, it implements the method of any one of claims 20 to 35.

39. A computer-readable program product comprising a computer program, wherein, When the computer program is executed by the processor of a computer device, it implements the method of any one of claims 20 to 35.

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