A beam management method and apparatus

Abstract

This application discloses a beam management method and apparatus applied to network equipment, comprising: receiving first feedback information reported by a user equipment, the first feedback information including measured values ​​of signal parameters of a first beam set within N historical time windows, the signal parameters including physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio (SINR), where N is an integer greater than or equal to 1; performing beam prediction based on the first feedback information to obtain beam prediction results, the beam prediction results including a second beam set and / or second beam information, the second beam set being a subset of a third beam set, the third beam set being a set of candidate beams used by the network equipment for data transmission; the second beam information including predicted values ​​of signal parameters of each beam in the second beam set. Implementing embodiments of this application can improve the accuracy of beam prediction and reduce the signaling overhead and latency of beam prediction.

Description

Technical Field

[0001] This application relates to the field of wireless communication, and more particularly to a beam management method and apparatus. Background Technology

[0002] In 5G and future communication networks, base stations can transmit dozens or even hundreds of narrow beams to cover users in different directions, enabling multi-user, high-speed, and low-latency communication. The narrow beams transmitted by base stations are beamforms where the electromagnetic wave energy radiated by the antenna is highly concentrated in a specific direction. Their beamwidth (i.e., the angular range covered by the main lobe) is narrow, typically between a few degrees and tens of degrees, exhibiting stronger directivity and higher energy concentration. Therefore, their antenna gain is high, compensating for path loss of high-frequency signals and reducing interference in other directions, making them suitable for multi-user spatial multiplexing scenarios. In communication between the base station and user equipment, the base station can manage the narrow beams, optimizing coverage through beam sweeping and beam tracking. This allows for dynamic adaptation to environmental changes, flexible responses to user movement, and capacity enhancement, enabling parallel transmission for multiple users.

[0003] However, in existing technical solutions, the methods used by base stations in beam scanning and beam monitoring for measuring and predicting beams have high latency, large signaling overhead, and insufficient measurement and prediction accuracy. This leads to problems such as data transmission delay and packet loss in wireless communication between the base station and user equipment. It may also cause deviations in resource allocation, that is, allocating limited resources to non-optimal beams, which reduces the efficiency and performance of the communication system. Summary of the Invention

[0004] This application provides a beam management method and apparatus that can improve the accuracy and flexibility of beam prediction.

[0005] In a first aspect, embodiments of this application provide a beam management method applied to a network device, comprising: receiving first feedback information reported by a user device, the first feedback information including measured values ​​of signal parameters of a first beam set within N historical time windows, the signal parameters including physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio (SINR), where N is an integer greater than or equal to 1; performing beam prediction based on the first feedback information to obtain a beam prediction result, the beam prediction result including a second beam set and / or second beam information, the second beam set being a subset of a third beam set, the third beam set being a set of candidate beams used by the network device for data transmission; the second beam information including predicted values ​​of the signal parameters of each beam in the second beam set.

[0006] This application provides a beam management method for network devices, which can improve the accuracy of beam prediction in network devices and reduce signaling overhead and latency in beam prediction. Specifically, the network device obtains historical data (i.e., the first feedback information) of beams transmitted to the user equipment (i.e., beams in the first beam set) within a historical period by receiving first feedback information sent by the user equipment. Based on the first feedback information, the network device performs beam prediction to obtain a beam (i.e., the second beam set) or beam information (i.e., the second prediction information) that is more suitable for transmission to the user equipment in the future. The first feedback information includes measured values ​​of physical layer reference signal received power (L1-RSRP), reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), which can provide the network device with more comprehensive historical data of beams in the first beam set. This allows the network device to improve the accuracy of beam prediction based on the first feedback information. In addition, more accurate prediction results can reduce the number of times the network device transmits candidate beams to determine the optimal beam (i.e., narrowing the range of candidate beams), as well as the number of times feedback information is received and analyzed and the amount of data contained in the feedback information. Therefore, it can reduce the latency and signaling overhead of the network device in determining the optimal beam for transmitting data, and improve the real-time performance and efficiency of wireless communication.

[0007] In one possible implementation, the first feedback information includes measured values ​​of the signal parameters of each beam in the first beam set within each of the N historical time windows, wherein one signal parameter of each beam corresponds to one or more measured values ​​in one historical time window. In this embodiment, the first feedback information can reflect the signal quality of the beams in the first beam set over multiple historical time windows. In other words, when the historical time window is long, the first feedback information can reflect the signal quality of the beams in the first beam set over a longer period using the measured values ​​of the signal parameters corresponding to fewer historical time windows. When the historical time window is short, the first feedback information can better reflect the changes in the signal quality of the beams in the first beam set over a shorter period using the measured values ​​of the signal parameters corresponding to more historical time windows. Thus, the first feedback information can increase the amount of data used for beam prediction by increasing the number and length of historical time windows, thereby improving the reliability of beam prediction.

[0008] In one possible implementation, the step of performing beam prediction based on the first feedback information to obtain a beam prediction result includes: inputting the first feedback information into a beam prediction model to output a beam prediction result. In this embodiment, beam prediction can be performed using a beam prediction model, enabling network devices to automatically extract features from the first feedback information without manual intervention. Furthermore, beam prediction using a model can handle large-scale data, improving the accuracy and speed of beam prediction, and also enhancing the generalization ability of beam prediction.

[0009] In one possible implementation, inputting the first feedback information into the beam prediction model to output a beam prediction result includes: determining the channel conditions of the user equipment based on the first feedback information; preprocessing the first feedback information based on the channel conditions to obtain preprocessed first feedback information, wherein the number of parameter types in the signal parameters of the preprocessed first feedback information is less than or equal to the number of parameter types in the signal parameters of the first feedback information before preprocessing; and inputting the preprocessed first feedback information into the beam prediction model to output a beam prediction result. In this embodiment, although the first feedback information received by the network device includes measured values ​​corresponding to three signal parameters, not all three signal parameters are necessary when performing beam prediction under different channel conditions corresponding to different user equipment. Therefore, the network device can first determine the channel conditions of the user equipment based on the first feedback information, and then select one or more of the three signal parameters from the first feedback information based on the channel conditions. This ensures that the first feedback information on which beam prediction depends provides sufficient signal quality information, and allows for the removal of some data that is not very useful for beam prediction based on different channel conditions, thereby reducing computational load and improving prediction efficiency.

[0010] In one possible implementation, the preprocessing of the first feedback information includes: selecting one or more parameters from L1-RSRP, RSRQ, and SINR based on the channel conditions; and normalizing the measured values ​​of the selected parameters in the first feedback information according to the parameter type, so as to map the measured values ​​corresponding to different types of parameters to different value ranges. In this embodiment, different types of signal parameters have different value ranges, and the absolute values ​​of the parameters are often large. Normalization can make the measured values ​​of different types of signal parameters at the same dimension level, which can reduce the computational complexity and improve the inference speed when performing beam prediction based on the first feedback information. In addition, normalizing the measured values ​​of signal parameters to different value ranges according to different types can strengthen the influence of certain (or several) signal parameters on beam prediction and improve the accuracy of beam prediction.

[0011] In one possible implementation, the method further includes: receiving reference information, the reference information including one or more of beam selection rules and parameter evaluation thresholds; the step of inputting the preprocessed first feedback information into a beam prediction model to output a beam prediction result includes: inputting the preprocessed first feedback information and the reference information into the beam prediction model to output a beam prediction result. In this embodiment, the network device can also receive reference information for beam prediction to assist beam prediction, making the obtained beam prediction results more suitable for the needs of different tasks and improving the flexibility and robustness of beam prediction.

[0012] In one possible implementation, the second beam set includes K beams, where K is an integer greater than 1, and the K beams are the K beams in the third beam set that satisfy preset conditions. In this embodiment, by pre-setting preset conditions, the beams included in the predicted results are more in line with the needs of different situations, thereby increasing the robustness of beam prediction.

[0013] In one possible implementation, the method further includes: determining a fourth beam set based on the beam prediction result, wherein the fourth beam set is a beam set transmitted by the network device to the user equipment for determining the optimal beam, the fourth beam set including one or more beams in the second beam set, or the fourth beam set including one or more beams in the second beam set and the third beam set, and the optimal beam is a beam in the fourth beam set; transmitting the beam in the fourth beam set to the user equipment; receiving the identifier of the optimal beam reported by the user equipment; and transmitting the optimal beam to the user equipment. In this embodiment, after obtaining the beam prediction result, the network device can further select based on the beam prediction result and the actual situation of the network device or environment to determine the final set of candidate beams (i.e., the fourth beam set) that can be used to transmit data to the user equipment. This avoids the communication quality degradation caused by directly transmitting the beam corresponding to the beam prediction result when the beam prediction is inaccurate, and at the same time makes the beam transmitted by the network device more in line with the needs of the user equipment, reducing the signaling overhead and latency generated in determining the optimal beam.

[0014] In one possible implementation, the beam prediction model is a gated recurrent unit (GRU) model. In this embodiment, the beam prediction model used is a GRU model. Compared to other recurrent neural network models in the prior art, the GRU model can more fully capture the temporal changes of the beam, making its predictions based on historical data more accurate. Furthermore, the GRU model has a simpler structure, can handle a larger amount of data, and has a simpler computation process, resulting in less memory usage and less computation time for beam prediction, thus improving the real-time performance of beam prediction.

[0015] Secondly, embodiments of this application provide a beam management method applied to a user equipment, comprising: receiving each beam in a first beam set transmitted by a network device, and measuring each beam in the first beam set to obtain first feedback information, wherein the first feedback information includes measured values ​​of signal parameters of the first beam set within N historical time windows, the signal parameters including physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1; and sending the first feedback information to the network device.

[0016] In one possible implementation, the method further includes: receiving each beam in a fourth beam set transmitted by a network device, and measuring each beam in the fourth beam set to obtain second feedback information, wherein the fourth beam set is a beam set sent by the network device to the user equipment for determining an optimal beam, and the second feedback information includes measured values ​​of the signal parameters of each beam in the fourth beam set; determining the optimal beam in the fourth beam set based on the second feedback information; and sending an identifier of the optimal beam to the network device.

[0017] Thirdly, embodiments of this application provide a beam management device applied to a network device. The device includes: a receiving module for receiving first feedback information reported by a user equipment, the first feedback information including measured values ​​of signal parameters of a first beam set within N historical time windows, the signal parameters including physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio (SINR), where N is an integer greater than or equal to 1; and a prediction module for performing beam prediction based on the first feedback information to obtain a beam prediction result, the beam prediction result including a second beam set and / or second beam information, the second beam set being... A subset of the third beam set, wherein the third beam set is a set of candidate beams used by the network device for data transmission; the second beam information includes predicted values ​​of the signal parameters of each beam in the second beam set; a scanning module is used to determine a fourth beam set based on the beam prediction results, and to scan the beams in the fourth beam set to the user equipment, wherein the fourth beam set is a set of beams transmitted by the network device to the user equipment for determining the optimal beam, and the fourth beam set includes one or more beams in the second beam set, or the fourth beam set includes one or more beams in the second beam set and the third beam set.

[0018] In one possible implementation, the receiving module is further configured to: receive the identifier of the optimal beam reported by the user equipment, wherein the optimal beam is a beam in the fourth beam set.

[0019] Fourthly, embodiments of this application provide a beam management device applied to a user equipment. The device includes: a measurement module for measuring each beam in a first beam set to obtain first feedback information, the first feedback information including measured values ​​of signal parameters of the first beam set within N historical time windows, the signal parameters including physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1; a feedback module for sending the first feedback information to a network device; and a communication module for receiving the optimal beam transmitted by the network device and establishing a communication link based on the optimal beam.

[0020] In one possible implementation, the measurement module is further configured to: measure each beam in the fourth beam set to obtain second feedback information, the fourth beam set being a beam set sent by the network device to the user equipment for determining the optimal beam, the second feedback information including measured values ​​of the signal parameters of each beam in the fourth beam set.

[0021] In one possible implementation, the feedback module is further configured to: send the second feedback information to the network device.

[0022] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described in the first aspect or any embodiment of the first aspect.

[0023] In a sixth aspect, embodiments of the present invention provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described in the second aspect or any embodiment of the second aspect.

[0024] In a seventh aspect, embodiments of the present invention provide a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform a method as described in the first aspect or any embodiment thereof.

[0025] Eighthly, embodiments of the present invention provide a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform a method as described in the second aspect or any embodiment of the second aspect. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0027] Figure 1 This is a schematic diagram of a wireless communication system 100 provided in an embodiment of this application.

[0028] Figure 2A This is a schematic diagram of a communication link between a network device and a user device provided in an embodiment of this application.

[0029] Figure 2B This is a schematic diagram of beam scanning for a network device provided in an embodiment of this application.

[0030] Figure 2C This is another schematic diagram of beam scanning for a network device provided in the embodiments of this application.

[0031] Figure 3A This is a schematic diagram of a beam management method provided in an embodiment of this application.

[0032] Figure 3B This is a schematic diagram of a data prediction process for first feedback information provided in an embodiment of this application.

[0033] Figure 3C This is a schematic diagram of a base station-side beam management method provided in an embodiment of this application.

[0034] Figure 4A This is a network structure diagram of a GRU beam prediction model provided in an embodiment of this application.

[0035] Figure 4B This is a schematic diagram of GRU network input and output provided in an embodiment of this application.

[0036] Figure 5A This is a schematic diagram of a beam prediction device 50 provided in an embodiment of this application.

[0037] Figure 5B This is a schematic diagram of another beam prediction device 60 provided in the embodiments of this application.

[0038] Figure 6 This is a schematic diagram of the structure of a user equipment provided in an embodiment of this application.

[0039] Figure 7 This is a schematic diagram of the structure of a network device provided in an embodiment of this application. Detailed Implementation

[0040] The embodiments of this application will now be described with reference to the accompanying drawings. The terms "first," "second," "third," and "fourth," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. The reference to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] The terms “component,” “module,” “system,” etc., used in this specification are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0042] First, combine Figure 1 This application describes the wireless communication system to which the beam management method provided in the embodiments is applicable.

[0043] The technical solutions of the embodiments of this application can be applied to various communication systems, such as: long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, 5th generation (5G) mobile communication systems or NR communication systems, and networks operating according to other systems and radio technologies (including future systems and radio technologies not explicitly mentioned herein).

[0044] Figure 1 This is a schematic diagram of a wireless communication system 100 provided in an embodiment of this application. Figure 1 As shown, the wireless communication system 100 may include one or more network devices, for example, Figure 1 The network device 101 shown may also include one or more user devices, for example, Figure 1 The mobile terminal 102, vehicle-mounted terminal 103, and fixed terminal 104 shown are examples of such devices.

[0045] The network device 101 in this application embodiment can be a device for communicating with user equipment. The network device 101 can be a base station, an evolved Node B (eNB), a home base station, an access point (AP), a wireless relay node, a wireless backhaul node, a transmission point (TP), or a transmission and reception point (TRP) in a wireless fidelity (WIFI) system, or a gNB in ​​a New Radio (NR) system. Alternatively, it can be a component or part of a base station, such as a central unit (CU), a distributed unit (DU), or a baseband unit (BBU). The embodiments of this application do not limit the specific technology or device form used in the network device. In this application, network device 101 can refer to the network device 101 itself, or it can be a chip applied in the network device 101 to perform wireless communication processing functions.

[0046] In this embodiment, the mobile terminal 102 in the wireless communication system 100 can also be referred to as a terminal, mobile station (MS), mobile terminal (MT), etc. Specifically, the mobile terminal 102 can be a mobile phone, tablet computer, mobile computing device with wireless transceiver function, a wireless terminal applied in scenarios such as virtual reality (VR) and augmented reality (AR), or other mobile terminals that can support wireless communication. The vehicle-mounted terminal 103 is a terminal device installed on a vehicle to enable information interaction between the vehicle and the outside world (such as other vehicles, base stations, traffic infrastructure, etc.). It can be a vehicle navigation terminal, a self-driving terminal, or a wireless terminal applied in scenarios such as transportation safety. The fixed terminal 104 refers to a device or terminal that is placed in a fixed position, does not have the characteristic of being able to move freely, and is mainly connected to the wireless communication system through a wired or fixed wireless access point to realize data transmission, communication, and other functions. For example, it is applied in industrial control, remote medical care, smart grid, and smart city. Wireless terminals in scenarios such as city and smart home. In this application embodiment, the aforementioned user equipment and the chips applicable to the aforementioned user equipment are collectively referred to as user equipment. However, this application embodiment does not limit the specific technology or device form used in the user equipment.

[0047] In some possible implementations, network device 101 and user equipment (including mobile terminal 102, vehicle terminal 103, and fixed terminal 104) can be distributed across a geographical area to form a wireless communication system 100, and may include devices in different forms or with different capabilities. Network device 101 and user equipment (including mobile terminal 102, vehicle terminal 103, and fixed terminal 104) can communicate wirelessly via one or more network device-user equipment communication links 105. For example, network device 101 can support coverage of a certain area. Within this coverage area, network device 101 and user equipment (including mobile terminal 102, vehicle terminal 103, and fixed terminal 104) can establish one or more network device-user equipment communication links 105. The coverage area can be a geographical area. Within this geographical area, the network device, in conjunction with the user equipment, can support signal communication of one or more radio access technologies (e.g., radio frequency access links). In some implementations of the wireless communication system 100, direct communication links can also be established between user equipment (D2D). For example, the vehicle-to-mobile terminal communication link 106 can be a communication link between a vehicle (e.g., vehicle-to-vehicle terminal 103) and terminal equipment (e.g., mobile terminal 102 and fixed terminal 104). The vehicle-to-mobile terminal communication link 106 can support one or more communication methods (e.g., short-range wireless communication links such as Bluetooth, near-field communication (NFC), cellular network communication links, dedicated short-range communication (DSRC), and vehicle-to-everything (V2X)). Furthermore, communication links can also be established between terminal equipment (e.g., mobile terminal 102 and fixed terminal 104), such as the mobile terminal-to-fixed terminal communication link 107. In addition, communication links can also be established between mobile terminals 102, between vehicle-to-vehicle terminals 103, and between fixed terminals 104. Figure 1 (not shown in the image), for example, a side-link communication channel. In some respects, vehicles may communicate using vehicle-to-vehicle (V2X) communication, vehicle-to-vehicle (V2V) communication, or some combination of the above methods.

[0048] It should be noted that, Figure 1 The exemplary illustrations provided are merely for illustrating the wireless communication system to which the embodiments of this application are applicable. This wireless communication system may also include other network devices, such as core network devices, wireless relay devices, and wireless backhaul devices. Figure 1 (Not shown in the image). Furthermore, this application embodiment does not limit the number of network devices and user devices included in the wireless communication system.

[0049] Based on the above wireless communication system, the following will be combined with Figure 2A , Figure 2B as well as Figure 2C This further explains the communication link between network equipment and user equipment, as well as beam management. Figure 2A This is a schematic diagram of a communication link between a network device and a user equipment provided in an embodiment of this application, such as... Figure 2A As shown, network device 101 and user equipment 202 can communicate via downlink communication link 203 and uplink communication link 204, wherein downlink communication link 203 and uplink communication link 204 can be Figure 1 An example of a network device-user equipment communication link 105, wherein user equipment 202 is an example of a user equipment, which may include... Figure 1 The mobile terminal 102, vehicle terminal 103, and fixed terminal 104 may also include other user equipment or chips capable of wireless communication. This application embodiment does not limit their form. Furthermore, user equipment 202 may include one or more specific devices or chips; that is, user equipment 202 can be a general term for user equipment in any wireless communication system, or it can refer to a specific user equipment. This application embodiment does not limit their quantity. Downlink communication link 203 is a downlink (DL) from network device 101 to user equipment 202, used to support network device 101 sending data or control signals to user equipment 202. Uplink communication link 204 is an uplink (UL) from user equipment 202 to network device 101, used to support user equipment 202 sending data or request signals to network device 101.

[0050] Specifically, when network device 101 communicates with user equipment 202 via downlink communication link 203, it can achieve this through multi-antenna beamforming. Here, beamforming refers to the network device 101 (e.g., a base station) controlling the signal phase and amplitude of multiple antenna elements in its antenna array to concentrate electromagnetic wave energy in one or more specific directions, forming a directional radiation pattern similar to a "beam." Beamforming can significantly enhance the signal strength in the target direction while reducing interference in other directions, thereby improving the performance of the wireless communication system. The process of network device 101 establishing a downlink mainly includes: firstly, broadcasting a Synchronization Signal Block (SSB) through the antenna array; after user equipment 202 powers on, it searches for and receives the SSB signal, completes time and frequency synchronization, and obtains the cell ID; furthermore, beam scanning is used to discover user equipment 202. Specifically, network device 101 can continue to transmit Channel State Information Reference Signal (CSI-RS) or other reference signals through beam scanning. Network device 101 can optimize scanning efficiency and accuracy using different beam sets; please refer to [link to relevant documentation]. Figure 2B , Figure 2B This is a schematic diagram of beam scanning for a network device provided in an embodiment of this application, such as... Figure 2BAs shown, the network device first performs a wide-beam scan using a wide-beam set (Set B) 211, which typically contains multiple wide beams (e.g., Figure 2B The two wider fan-shaped areas shown in Set B 211 can represent two specific wide beams. It should be noted that the number of wide beams included in Set B 211 is not fixed; its specific number depends on the size of the area covered by network device 101 and the width of the beam emitted by network device 101. Figure 2B Set B 211 is merely an exemplary illustration to illustrate wireless communication between network device 101 and user equipment 202 and does not constitute a specific limitation.

[0051] Specifically, the wide beam in Set B 211 has a wide coverage area, enabling signal transmission over a large region, but its gain is relatively low, making it suitable for quickly detecting user equipment 202. The wide beam in Set B 211 can sequentially transmit reference signals (e.g., CSI-RS) in space at certain angular intervals (e.g., every 30 degrees) to cover the entire service area and attempt to detect potential user equipment 202. After receiving the wide beam in Set B 211, user equipment 202 can measure the reference signal strength of the wide beam and report it to network device 101. Based on the measurement results reported by user equipment 202, network device 101 can determine the approximate location of user equipment 202. Further details can be found in [link to relevant documentation]. Figure 2C , Figure 2C This is another schematic diagram of beam scanning for a network device provided in this application embodiment. The network device 101 can switch to a narrow beam set (Set A) 221 for refined scanning. Set A 221 may include multiple narrow beams (e.g. Figure 2C The five narrower fan-shaped regions shown in Set A 221 can represent five specific narrow beams. These narrow beams are typically narrow in width but have higher gain, enabling them to be more accurately pointed at the user equipment 202 and improving the quality of wireless communication. It should be noted that, similar to Set B 211, the number of narrow beams in Set A 221 is not fixed; its specific number is also related to the size of the area covered by network device 101 and the width of the beam emitted by network device 101. Figure 2CThe narrow beam set (Set A) 221 is merely an exemplary illustration of wireless communication between network device 101 and user equipment 202 and does not constitute a specific limitation. Generally, the number of beams in Set A 221 is greater than the number of beams in Set B 211. Furthermore, user equipment 202 can measure the reference frame signal strength of the beams in Set A 221 and report it to network device 101. Based on the measurement results reported by user equipment 202, network device 101 can further adjust the beams until the optimal beam is determined, and then conduct wireless communication with user equipment 202 through the optimal beam. The optimal beam refers to the candidate beam used by network device 101 to transmit data to user equipment 202 that maximizes signal quality or minimizes interference.

[0052] Based on the aforementioned process of establishing a downlink between network device 101 and user equipment 202 to achieve wireless communication, it is understandable that when network device 101 uses a narrow beam set for scanning, the beam width is smaller compared to a wide beam, thus requiring denser beam direction testing. This results in higher computational complexity and latency, as well as higher scanning overhead. Furthermore, frequent beam scanning and feedback increase signaling overhead, further increasing communication latency. Additionally, when user equipment 202 is in a high-speed moving scenario, its extremely high speed (or obstruction by obstacles) causes channel conditions to change rapidly over time. Based on the above issues... The current technical solutions typically perform time-domain downlink beam prediction on the network device side. This involves predicting the optimal beam for the user equipment in the next time period (e.g., a narrow beam set or a specific beam within a narrow beam set) based on historical beam information (e.g., measurement information based on wide beams). This aims to minimize beam scanning overhead and corresponding signaling overhead, and reduce latency during wireless communication. Furthermore, in scenarios involving high-speed movement or obstruction, time-domain downlink beam prediction can quickly adapt to channel changes, enabling the wireless communication system to complete beam switching in a short time and reducing communication interruptions or signal quality degradation.

[0053] In existing technical solutions, artificial intelligence (AI) or machine learning (ML) models can be deployed at the base station side for time-domain downlink prediction. However, when making predictions, existing technical solutions rely on overly simplistic historical measurement information, which fails to fully reflect the true condition of the wireless channel. This often leads to predictions that deviate from reality, specifically the incorrect selection of suboptimal beams, resulting in decreased communication quality and issues such as data transmission delays and packet loss. Furthermore, inaccurate predictions can cause base stations to allocate resources incorrectly, distributing limited resources to suboptimal beams and reducing the efficiency and performance of the entire communication system. In addition, the model structures used in existing technical solutions are often too complex (e.g., Long Short-Term Memory (LSTM) models), resulting in excessively high time costs for processing large amounts of input data and significant delays in generating prediction results, making it difficult to meet the response requirements of ultra-reliable low-latency communication (URLLC).

[0054] To address the problems existing in the prior art, this application provides a beam management method that can accurately reflect beam information and channel status over historical time periods. Furthermore, it uses a simpler model for beam prediction to obtain more accurate downlink beam prediction results in the time domain, thereby reducing latency and signaling overhead.

[0055] The main flow of the beam management method provided in this application embodiment is described below. This method is applied to network device 101 and mainly includes:

[0056] The network device 101 receives first feedback information reported by the user equipment 202. This first feedback information includes measured values ​​of three parameters within N historical time windows: Physical Layer Reference Signal Received Power (L1-RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR), where N is an integer greater than or equal to 1. The historical time window refers to a period of time prior to the current moment.

[0057] In one possible implementation, the specific time length of the N historical time windows can be set by the network device 101, and the set time length of the N historical time windows is sent to the user device 202 before the user device 202 sends the first feedback information to the network device. The user device 202 can measure the beams in the first beam set based on the specific length of the N historical time windows to obtain the first feedback information. In this system, network device 101, as the core control node of a wireless communication system (e.g., wireless communication system 100), can determine the specific length of the historical time window based on the overall situation of the wireless communication system (e.g., network load, service type distribution, etc.) and the characteristics of the wireless environment (e.g., signal interference level, propagation loss, etc.). For example, in areas with high interference, network device 101 may set a shorter historical time window to acquire signal change information (e.g., first feedback information) more frequently. This improves the timeliness and accuracy of beam prediction based on the first feedback information. Conversely, in areas with relatively stable signals, network device 101 may set a longer historical time window (and simultaneously reduce the number of historical time windows) to reduce the frequency of acquiring signal change information, thereby reducing unnecessary signaling overhead and latency. Optionally, the lengths of the N historical time windows can be different.

[0058] Specifically, the first feedback information may include three parameters: L1-RSRP, RSRQ, and SINR. The definitions of these three parameters are as follows:

[0059] 1. L1-RSRP: refers to the linear average power of the reference signal received on a specific resource element, measured in decibels per milliwatt (dBm). The specific resource element refers to the resource element used to carry the reference signal (RS). In this application, the resource element used to carry the reference signal can be a beam (including beams in the first beam set, the second beam set, and the third beam set). The calculation formula for L1-RSRP is as follows:

[0060]

[0061] in, The quantity of resource elements to be measured. For the first In one aspect of the embodiments of this application, the L1-RSRP can be used to measure the strength of the reference signal received by the user equipment 202 from the network device 101. The higher the value of L1-RSRP, the stronger the signal received by the user equipment 202, indicating that the user equipment 202 is closer to the network device 101 or the loss on the signal propagation path is smaller, and the network coverage is better.

[0062] 2. RSRQ: This is the ratio of RSRP to the Received Signal Strength Indicator (RSSI), which comprehensively reflects signal strength and interference level. The unit is decibels (dB), and its calculation formula is as follows:

[0063] in, Similarly, this refers to the quantity of resource elements being measured. RSRQ is the total received power of all signals (including serving cell, neighboring cells, interference, and noise) within the same resource block (i.e., multiple user equipments sharing or competing for the same physical resource unit at the same time and frequency range). It can measure the received signal strength in wireless communication. Compared with L1-RSRP, RSRQ not only reflects signal strength but also comprehensively reflects signal strength and interference. That is, if the signal strength received by user equipment 202 is high, that is, the RSRP value is high, but the number of surrounding interference signals is large and the intensity is high, that is, the RSSI value is high, then the ratio of RSRP to RSSI, RSRQ, will be low. This indicates that the actual channel communication quality is not ideal due to the presence of interference signals.

[0064] 3. SINR: The ratio of the power of the received useful signal to the sum of the power of the interference and noise signals, also known as RSRP (Sum of Power Received) to the power of the interference plus noise. The SINR (Signal-to-Noise Ratio) is the ratio of signal strength to noise level, usually measured in decibels (dB). SINR directly reflects the degree of interference and noise affecting a signal during transmission, thus directly characterizing channel quality. Its calculation formula is:

[0065]

[0066] in, This refers to the co-channel interference power, which is the interference power generated by other cells (neighboring cells) in a wireless network that use the same frequency as the current serving cell for signal transmission, affecting the signal received by user equipment in the current serving cell. SINR represents the power of the noise signal. A higher SINR value indicates that the useful signal is stronger relative to the interference and noise, resulting in better communication quality, lower data transmission error rate, and the ability to support higher data transmission rates. Conversely, a lower SINR value indicates poorer communication quality and a greater likelihood of data transmission errors.

[0067] In one possible implementation, the first feedback information can be measured by the user equipment 202 and fed back to the network device 101. The first feedback information includes the three parameters mentioned above. In this way, when the network device 101 makes a prediction based on the first feedback information, it does not only use L1-RSRP as the parameter data, but also uses L1-RSRP, RSRQ and SINR as reference data for beam prediction.

[0068] Further, beam prediction is performed based on the first feedback information to obtain beam prediction results. Specifically, network device 101 can perform beam prediction based on the first feedback information to obtain beam prediction results. The beam prediction results include one or more of a second beam set or second beam information. The second beam set is a subset of a third beam set, which is a set of candidate beams used by the network device for data transmission. The second beam information is the prediction information of the beams in the second beam set, and the second beam information includes the predicted values ​​of the L1-RSRP, RSRQ, and SINR parameters of the beams in the second beam set.

[0069] In some possible implementations, network device 101 may maintain multiple dynamic beam sets, which may include beam sets for determining potential user equipment 202 or for determining the wireless channel environment of user equipment 202 (e.g., Figure 2B The wide beam set (Set B 211) shown has a large beam width, but poor signal directivity and gain, making it difficult to meet the response requirements of reliable low-latency communication. However, the beams can quickly acquire spatial coverage information, determine the approximate location of user equipment 202, and the channel environment. In addition, network device 101 can also maintain a dynamic set of candidate beams (one or more) for data transmission, namely a third beam set. The beams in the third beam set have a smaller beam width, but strong directivity and signal gain, which can better meet the communication requirements of the wireless communication system (e.g., wireless communication system 100). They are candidate beams that can establish a communication link with user equipment 202 and transmit data (that is, one or more of these beams may eventually establish a communication link with user equipment 202 and transmit data). In one possible implementation, the third beam set can be... Figure 2C The narrow beam set Set A 221 is shown.

[0070] However, since the beam width in the third beam set is usually small, multiple beams are needed to cover a certain spatial area. Since the user equipment 202 ultimately only needs one beam to establish a communication link, not all beams in the third beam set are usable by the user equipment 202 or have good signal quality. Some beams in the third beam set are clearly unsuitable for the user equipment 202 due to their orientation and the channel environment of the user equipment 202. Therefore, before using narrow beam scanning, beam prediction is needed to determine one or more beams in the third beam set that are more suitable for the user equipment 202 and have better communication quality; this is the second beam set. Optionally, the beam prediction result may include the second beam set, which is a subset of the third beam set. That is, the second beam set includes one or more beams from the third beam set. In one possible implementation, the beams included in the second beam set refer to the identifiers (IDs) of one or more beams in the third beam set, rather than the actual beams.

[0071] In one possible implementation, the beam prediction result may further include second beam information, which is the prediction information of beams in the second beam set. That is, the predicted values ​​of the L1-RSRP, RSRQ, and SINR parameters of the beams in the second beam set at future times. This information represents the beams in the second beam set predicted from the third beam set and reflects the channel quality of the beams in the second beam set. Optionally, the beam prediction result may include the second beam set, second beam information, or both.

[0072] Specifically, after receiving the first feedback information, if the network device 101 directly scans the beams in the third beam set without performing beam prediction, it needs to receive parameter measurements for the beams in the third beam set again and further evaluate the parameter measurements. It may even need to scan and evaluate multiple times until the optimal beam is determined, and then use the optimal beam to transmit data. In this existing technical solution, since the number of beams in the third beam set is large, the signaling overhead and latency of scanning and evaluation are large, which cannot meet the response requirements of ultra-reliable low-latency communication (URLLC). Therefore, beam prediction is required. The method provided in this application can predict the beams in the third beam set that are more likely to establish communication links and transmit data, or their corresponding prediction information, based on measurement information for the first beam set. This reduces the number of times the network device transmits candidate beams to determine the optimal beam (i.e., narrows the range of candidate beams), and also reduces the number of times feedback information is received and analyzed, as well as the amount of data contained in the feedback information. Therefore, it can reduce the latency and signaling overhead of the network device in determining the optimal beam for transmitting data, and improve the real-time performance and efficiency of wireless communication. In addition, the first feedback information includes the measured values ​​of physical layer reference signal received power (L1-RSRP), reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), which can provide the network device with more comprehensive historical data of the beams in the first beam set, enabling the network device to improve the accuracy of beam prediction based on the first feedback information.

[0073] In some possible implementations, the beam management method provided in this application can perform beam prediction using a beam prediction model, as described below. Figure 3A To illustrate the beam management method provided in the embodiments of this application in more detail, Figure 3A This is a schematic flowchart of a beam management method provided in an embodiment of this application. Figure 3A As shown, beam management methods may include:

[0074] Step S301: Transmit the beams from the first beam set.

[0075] Specifically, network device 101 transmits beams from a first beam set to user equipment 202 for measurement. The first beam set is a set of beams maintained by network device 101 for scanning user equipment 202. The beamwidth in the first beam set is relatively large, which can be used to quickly determine the approximate location of user equipment 202 and the channel environment. In one possible implementation, the first beam set can be... Figure 2B The wide beam set Set B 211 is shown.

[0076] Step S302: Measure the first beam set to obtain the first feedback information.

[0077] Specifically, user equipment 202 receives beams from the first beam set transmitted by network device 101 and measures them. In one possible implementation, network device 101 may set specific time lengths for N historical time windows based on the overall situation of the wireless communication system before transmitting the first beam set to user equipment 202. For details, please refer to the relevant description in the main process of the beam management method described above, which will not be repeated here. Further, network device 101 may send the lengths of the N historical time windows to user equipment 202 to guide user equipment 202 in measuring the beams in the first beam set. Optionally, network device 101 may send the lengths of the N historical time windows to user equipment 202 via beams in the first beam set, or via other beams.

[0078] In one possible implementation, the user equipment 202 measures the beams in the first beam set according to the length of the N historical time windows set by the network equipment 101, so as to obtain the measured values ​​of the signal parameters L1-RSRP, RSRQ, SINR corresponding to each beam in the first beam set within the N historical time windows.

[0079] Step S303: Report the first feedback information.

[0080] Specifically, user equipment 202 can send the measured first feedback information to network device 101. In one possible implementation, before sending the first feedback information to network device 101, user equipment 202 can also perform preliminary preprocessing on the first feedback information, as detailed in the relevant description of step S404 below.

[0081] Step S304: Preprocess the first feedback information.

[0082] Specifically, while using L1-RSRP, RSRQ, and SINR for beam prediction can address the issue of limited historical reference data and improve prediction accuracy, the sensitivity of historical parameters varies significantly across different communication scenarios. For instance, in high-interference environments, RSRP, as a core indicator of signal coverage strength, directly reflects the connection stability of the beam between user equipment 202 and network equipment 101. However, in dynamic scenarios, RSRQ and SINR better reflect changes in interference and instantaneous channel quality, making them more suitable for measuring beam status in dynamic environments. Therefore, network equipment... Preprocessing the first feedback information by network device 101 may include dynamically selecting one or more parameters from the first feedback information before performing beam prediction. This can make the historical reference data used for beam prediction more suitable for the communication environment of user equipment 202, thereby making beam prediction more accurate. In addition, the first feedback data received by network device 101 includes multiple parameter measurement values ​​of multiple beams in multiple historical time windows. The data volume is large and complex. Preprocessing the first feedback information by network device 101 may also include splicing it into one or more sequences to facilitate input into the beam prediction model for beam prediction.

[0083] In one possible implementation, this application provides a possible scheme for data preprocessing of the first feedback information, which can be found in [reference needed]. Figure 3B , Figure 3B This is a schematic diagram of a data prediction processing procedure for first feedback information provided in an embodiment of this application, such as... Figure 3B As shown, the data preprocessing of the first feedback information by the network device 101 may include:

[0084] Step S401: Determine the channel conditions based on the first feedback information.

[0085] Specifically, after receiving the first feedback information, network device 101 can determine the channel conditions of user equipment 202 that sent the first feedback information based on the specific parameter measurement values ​​in the first feedback information. The first feedback information includes the measurement values ​​corresponding to L1-RSRP, RSRQ, and SINR of the beams in the first beam set. Network device 101 can determine the scenario in which user equipment 202 is located, i.e., the channel conditions, based on the specific L1-RSRP, RSRQ, and SINR measurement values. The channel conditions refer to the state description of the impact on signal transmission quality after the combined effect of the physical characteristics of the signal transmission path (e.g., beam) and environmental factors in wireless communication.

[0086] In one possible implementation, please refer to Table 1, which is a table of L1-RSRP, RSRQ, SINR, and RSSI value ranges provided in the embodiments of this application. The L1-RSRP value range is typically between -140dBm and -44dBm, with a typical range between -140dBm and -80dBm. In the first feedback information, when the measured value corresponding to L1-RSRP is -85dBm or above, it indicates that the signal strength of the beam in the first beam set received by user equipment 202 is good, suitable for high-speed data transmission. At this time, the communication quality between user equipment 202 and network device 101 is high (e.g., virtually unobstructed), and a large amount of data can be transmitted stably. When the measured value corresponding to L1-RSRP is between -85dBm and -95dBm, it indicates that the signal strength of the beam in the first beam set received by user equipment 202 is good, which can meet the data transmission needs of most user equipment 202, and the communication experience is relatively smooth. When the measured value corresponding to L1-RSRP is between -95dBm and -105dBm, it indicates that the signal strength of the beam in the first beam set received by user equipment 202 is good, which can meet the data transmission needs of most user equipment 202, and the communication experience is relatively smooth. The signal strength of the beam in the first beam set received by user equipment 202 is generally low. There is some obstruction between user equipment 202 and network device 101 (e.g., user equipment 202 is located deep indoors or in a densely built-up area). It is suitable for low-speed data transmission, and the communication quality may be degraded due to the reduced data transmission speed during wireless communication. When the measured value corresponding to L1-RSRP is below -105dBm, it indicates that the signal strength of the beam in the first beam set received by user equipment 202 is very poor. There is severe obstruction between user equipment 202 and network device 101 (e.g., user equipment 202 is in a basement, elevator, or other enclosed space, or is far from the base station), which may lead to a significant decrease in data transmission speed or even connection interruption.

[0087] The RSRQ value typically ranges from -19.5dB to -3dB, with a typical range of -19dB to -3dB. In the first feedback information, when the measured value corresponding to RSRQ is -10dB or higher, it indicates that the beam in the first beam set received by user equipment 202 has excellent signal quality, the reference signal power is very high relative to the interference and noise power, user equipment 202 may be in an area with good signal coverage and low interference, stable signal transmission, extremely low bit error rate, and can support high-speed, high-quality data transmission with low latency; when the measured value corresponding to RSRQ is between -10dB and -15dB, it indicates that the beam in the first beam set received by user equipment 202 has good signal quality, the ratio of reference signal power to interference and noise power is at a high level, and user equipment 202 is in an area with good signal coverage and low interference, stable signal transmission, extremely low bit error rate, and can support high-speed, high-quality data transmission with low latency. There may be some interference, but the interference intensity is small and does not seriously affect the signal quality. The data transmission rate is relatively high, and the network latency is slightly increased compared to when the RSRQ measurement value is -10dB or higher. When the RSRQ measurement value is between -15dB and -18dB, it indicates that the signal quality of the beam in the first beam set received by user equipment 202 is average, and the ratio of reference signal power to interference and noise power is at a moderate level. User equipment 202 may be in an area with significant interference (such as the center of a commercial area or a large office building). The data transmission rate decreases, and data transmission delay and quality degradation occur. When the RSRQ measurement value is below -18dB, it indicates that the signal quality of the beam in the first beam set received by user equipment 202 is poor, and the ratio of reference signal power to interference and noise power is very low. User equipment 202 may be in an area with severe interference (such as a large event site, a factory workshop, etc.). The communication link is unstable, and data transmission is frequently interrupted.

[0088] The SINR value typically ranges from -20dB to 30dB, with a typical range of -3dB to 20dB. In the first feedback information, when the SINR measurement is 15dB or higher, it indicates that the useful signal strength of the beam in the first beam set received by user equipment 202 is much greater than the interference and noise strength, the signal quality is very good, user equipment 202 may be in an area with good signal coverage and minimal interference, and the data transmission rate is extremely high; when the SINR measurement is between 10dB and 15dB, it indicates that the useful signal strength of the beam in the first beam set received by user equipment 202 is significantly greater than the interference and noise strength, the signal quality is good, there may be a small amount of interference around user equipment 202, but the interference strength is small and insufficient to significantly affect the signal quality, the data transmission rate is high, and the network latency is low; when the SINR measurement is between 5dB and 10dB, it indicates that the useful signal strength of the beam in the first beam set received by user equipment 202 is slightly greater than the interference and noise strength, the signal quality is average, and user equipment 202 may be in an area with good signal coverage and minimal interference, resulting in a very high data transmission rate and low network latency. 02 may be located in an area with relatively high interference (such as a residential area or a small commercial area), resulting in decreased data transmission quality and rate. When the SINR measurement value is between 0dB and 5dB, it indicates that the useful signal strength of the beam in the first beam set received by user equipment 202 is similar to the interference and noise strength, resulting in poor signal quality. User equipment 202 may be located in an area with severe interference (such as a large-scale performance venue), where data transmission is unstable and the rate is low. When the SINR measurement value is below 0dB, it indicates that the interference and noise strength of the beam in the first beam set received by user equipment 202 is greater than the useful signal strength, making it almost impossible to receive the signal normally. User equipment 202 may be located in an area with severe signal interference or extremely poor coverage (such as enclosed spaces like basements or elevator shafts, or near strong interference sources), where user equipment 202 is basically unable to establish a communication link for data transmission.

[0089] The RSSI value typically ranges from -110dBm to -30dBm, with a typical range of -100dBm to -60dBm. In the first feedback information, when the RSSI measurement value is between -50dBm and -30dBm, it indicates that the signal of the beam in the first beam set received by user equipment 202 is very strong, the data transmission is very stable, the data transmission rate is high, and the latency is low. When the RSSI measurement value is between -70dBm and -50dBm, it indicates that the signal of the beam in the first beam set received by user equipment 202 is good, the wireless link is relatively stable, and user equipment 202 may be in an area with good coverage by network device 101. When the RSSI measurement value is between -101dBm and -70dBm, it indicates that the signal of the beam in the first beam set received by user equipment 202 is poor, the data transmission rate decreases, and transmission interruption may occur.

[0090] In one possible implementation, the first feedback information is measured and fed back by the user equipment 202 within N historical time windows. Therefore, the network device 101 can determine the channel conditions of the user equipment 202 within the N historical time windows based on the first feedback information.

[0091] Table 1. Range of L1-RSRP, RSRQ, SINR, and RSSI values

[0092]

[0093] Step S402: Based on the channel conditions, select the parameter type in the first feedback information.

[0094] In one possible implementation, based on the determined channel conditions of the user equipment 202, the network device 101 can select one or more signal parameters from the first feedback information (that is, select one or more of the three signal parameters L1-RSRP, RSRQ, and SINR). It should be noted that the network device 101 selects one or more parameter types from the first feedback information by retaining (or removing) the specific parameter measurement values ​​(within N historical time windows) corresponding to one or more parameter types. In this way, the first feedback information that is more suitable for the communication environment of the user equipment 202 can be used as the reference data for beam prediction.

[0095] Specifically, when network device 101 determines that the channel conditions of user equipment 202 are in a high-interference scenario, such as in a dense urban area or a scenario with multiple users competing for resources, RSRQ can reflect the degree of interference affecting the signal quality of the beams in the first beam set received by user equipment 202, and SINR can reflect the quantization signal-to-noise ratio of the beams in the first beam set received by user equipment 202. The combination of the two can better dynamically capture the impact of interference fluctuations on the channel. In addition, in high-interference scenarios, the strength of the interference signal may be higher than that of the useful signal. Since it is difficult to completely separate the useful signal and the interference signal in actual measurement, the measured L1-RSRP is likely to contain the power component of the interference signal, which will cause the measured L1-RSRP to fail to accurately reflect the true strength of the useful signal, and instead increase the overhead of measurement and calculation. Therefore, optionally, network device 101 can select parameters RSRQ and SINR from the three signal parameters (L1-RSRP, RSRQ, SINR) of the first feedback information. That is, it can retain the specific parameter measurement values ​​corresponding to RSRQ and SINR (within N historical time windows) in the first feedback information, or remove the specific parameter measurement values ​​corresponding to L1-RSRP (within N historical time windows) from the first feedback information.

[0096] When network device 101 determines that the channel conditions of user equipment 202 are in a weak signal scenario, such as when user equipment 202 is at the edge of the coverage area of ​​network device 101 or indoors, L1-RSRP can provide information on beam signal strength, which is a key parameter for beam coverage assessment. SINR can provide information on beam signal quality, which can help assess signal stability and avoid misjudgment due to transient noise. The combination of the two can provide a more comprehensive assessment of the wireless communication environment. In addition, when user equipment 202 is in a weak signal environment, RSRQ (Reference Signal Received Quality) is not very meaningful and may increase the computational load and complexity when performing beam prediction based on the first feedback information. Therefore, optionally, network device 101 can select parameters L1-RSRP and SINR from the three parameters (L1-RSRP, RSRQ, SINR) of the first feedback information. That is, it can retain the specific parameter measurement values ​​corresponding to L1-RSRP and SINR (within N historical time windows) in the first feedback information, or remove the specific parameter measurement values ​​corresponding to RSRQ (within N historical time windows) from the first feedback information.

[0097] When network device 101 determines that the channel conditions of user equipment 202 are in a simplified deployment scenario, that is, when user equipment 202 is in a miniaturized, flexible, or lightweight network coverage environment, it may mean that network device 101, which transmits the first beam set to user equipment 202, has simplified its deployment method, network architecture, or operation and maintenance due to resource constraints or low complexity requirements. In this scenario, the wireless communication system in which user equipment 202 is located is relatively small in scale, has a relatively small number of devices, relatively simple interference sources, and a small coverage area. The signal propagation path is relatively simple, and L1-RSRP can reflect the signal strength and coverage well, without the need to measure RSRQ and SINR to evaluate channel quality. In addition, in simplified deployment scenarios, user equipment 202 usually does not have high requirements for channel quality, and its requirements for real-time performance and reliability are relatively low. Therefore, only L1-RSRP is needed to measure the signal quality well, so as to ensure that the beam predicted based on the reference information can better meet the needs of user equipment 202. Therefore, optionally, network device 101 can select parameter L1-RSRP from the three parameters (L1-RSRP, RSRQ, SINR) in the first feedback information. That is, it can retain the specific parameter measurement value corresponding to L1-RSRP (within N historical time windows) in the first feedback information, or remove the specific parameter measurement values ​​corresponding to RSRQ and SINR (within N historical time windows) in the first feedback information.

[0098] In some possible implementations, network device 101 can directly use the first feedback information, that is, without filtering or selection. In this case, the first feedback information can more comprehensively and accurately reflect the quality of the beams in the first beam set received by user equipment 202, which can improve the accuracy of beam prediction.

[0099] Step S403: Normalize the first feedback information.

[0100] In one possible implementation, the first feedback information after parameter selection may include parameter measurement values ​​corresponding to one or more of the three parameters: L1-RSRP, RSRQ, and SINR. As shown in Table 1, the three parameters have different value ranges. Before using the first feedback information as reference information for beam prediction, it is necessary to normalize the parameter measurement values ​​corresponding to different types of parameters in the first feedback information to solve the scale difference problem of different types of data, thereby improving the performance and stability of prediction.

[0101] Optionally, the normalization of the parameter measurement values ​​of different types of parameters in the first feedback information can be performed by an interval scaling method. The L1-RSRP is used to evaluate the strength of the signal received by the user equipment 202 and is a basic indicator for measuring the quality of wireless signals. It plays a very important role in measuring signal strength. Therefore, when scaling the first feedback information, a differentiated scaling method can be used to ensure the core role of L1-RSRP in beam prediction based on the first feedback information.

[0102] Specifically, when no parameter selection is performed on the first feedback information, i.e., when the specific parameter measurements corresponding to the three parameters in the first feedback information are retained, the specific parameter measurements corresponding to L1-RSRP can be range-scaled. For example, the parameter measurements corresponding to L1-RSRP within N historical time windows can be mapped to the interval [0, 1, 2]. Similarly, the specific parameter measurements corresponding to RSRQ and SINR can be normalized (Min-Max Normalization) to the interval [0, 1]. In other words, the parameter measurements corresponding to RSRQ and SINR within N historical time windows can be mapped to [0, 1]. Thus, L1-RSRP in the normalized first feedback information has a larger data range, which is equivalent to assigning a higher weight to L1-RSRP in the normalized first feedback information, highlighting its core role in signal measurement.

[0103] Optionally, when the first feedback information after parameter selection processing (that is, the first feedback information after step S402) only includes the parameter measurement values ​​corresponding to RSRQ and SINR, the specific parameter measurement values ​​corresponding to the two can be normalized to the interval [0, 1], that is, the parameter measurement values ​​corresponding to RSRQ and SINR in N historical time windows are mapped to [0, 1].

[0104] Optionally, when the first feedback information after parameter selection processing (that is, the first feedback information after step S402) only includes the parameter measurement values ​​corresponding to L1-RSRP and SINR, the specific parameter measurement values ​​corresponding to L1-RSRP can be range-scaled. For example, the parameter measurement values ​​corresponding to L1-RSRP in N historical time windows can be mapped to the interval [0, 1, 2], and the specific parameter measurement values ​​corresponding to SINR can be deviation-standardized, that is, the parameter measurement values ​​corresponding to SINR in N historical time windows can be mapped to the interval [0, 1].

[0105] Optionally, when the first feedback information after parameter selection processing (i.e., the first feedback information after step S402) only includes the parameter measurement value corresponding to L1-RSRP, the specific parameter measurement value corresponding to L1-RSRP can still be range-scaled. For example, the parameter measurement value corresponding to L1-RSRP within N historical time windows can be mapped to the interval [0, 1.2]. This can ensure consistency with the normalization processing of multi-parameter scenarios and dimensional compatibility of reference data in beam prediction. Moreover, compared with the prior art that only relies on the original value of L1-RSRP, mapping the parameter measurement value corresponding to L1-RSRP to the interval [0, 1.2] can amplify the dynamic range of L1-RSRP, allowing it to occupy a higher weight in beam prediction.

[0106] Step S404: Perform splicing processing on the first feedback information.

[0107] Specifically, the first feedback information includes the measured values ​​of one or more signal parameters of each beam in the first beam set within N historical time windows, where N is an integer greater than or equal to 1. Each signal parameter of each beam corresponds to one or more measured values ​​in a historical time window. Therefore, the first feedback information has a large amount of data and there is a certain time relationship between the data. It needs to be spliced ​​to form a time series as the input of the beam prediction model, so that the beam prediction model can predict the beam with the best signal quality for user equipment 202 at future times based on the input with time sequence characteristics.

[0108] In one possible implementation, the measured values ​​of one or more signal parameters within N historical time windows in the first feedback information can first be sequenced according to the type of signal parameter. The following example, using first feedback information that still contains three types of signal parameters after parameter selection, illustrates how to concatenate the first feedback information. The sequence of signal parameters for the N historical time windows after concatenation can be:

[0109] ;

[0110] ;

[0111] .

[0112] in, The total number of beams received by the user equipment 202, which measures and sends the first feedback information, or, in one possible implementation, 1 to... This can represent the identifier of each beam in the first beam set. Specifically, user equipment 202 may include k specific user equipments, where k is an integer greater than or equal to 1; therefore, the sequence... A sequence of L1-RSRP measurements for each beam in the first beam set can be measured for the k-th specific user equipment in user equipment 202 within the i-th historical time window, where the sequence... Any element in represents the L1-RSRP measurement value of the k-th specific user equipment in user equipment 202 for a certain beam in the first beam set within the i-th historical time window, for example, a sequence. elements in Represents: The L1-RSRP measurement value of the k-th specific user equipment in user equipment 202 for the second beam (or the beam identified as "2") in the first beam set within the i-th historical time window;

[0113] Optionally, sequence This can be a sequence of RSRQ measurements taken by the k-th specific user equipment in user equipment 202 within the i-th historical time window for each beam in the first beam set, where the sequence... Any element in represents the RSRQ measurement value of the k-th specific user equipment in user equipment 202 for a certain beam in the first beam set within the i-th historical time window, for example, a sequence. elements in Represents: The RSRQ measurement value of the k-th specific user equipment in user equipment 202 for the second beam (or the beam identified as "2") in the first beam set within the i-th historical time window;

[0114] Optionally, sequence This can be a sequence of SINR measurements taken by the k-th specific user equipment in user equipment 202 within the i-th historical time window for each beam in the first beam set, where the sequence... Any element in the sequence represents the SINR measurement value of the k-th specific user equipment in user equipment 202 for a certain beam in the first beam set within the i-th historical time window, for example, the sequence. elements in Represents: The SINR measurement value of the k-th specific user equipment in user equipment 202 for the second beam (or the beam identified as "2") in the first beam set within the i-th historical time window.

[0115] Further, the sequences of the three signal parameters of user equipment 202 within N historical time windows are concatenated in a preset order to obtain the comprehensive signal measurement vector of user equipment 202 corresponding to each historical time window. Optionally, the preset order can be "L1-RSRP sequence-RSRQ sequence-SINR sequence". In one possible implementation, if the user equipment 202 includes k specific user equipments, then for any specific user equipment, the sequences of the three signal parameters corresponding to any historical time window can be concatenated together to obtain the comprehensive signal measurement vector of that user equipment in any historical time window, for example:

[0116]

[0117] in, This is the comprehensive measurement vector of the k-th specific user equipment in user equipment 202 within the first historical time window, where "1" in "sig1" can indicate that the current comprehensive measurement vector is the comprehensive measurement vector corresponding to the first historical time window among N historical time windows.

[0118] In one possible implementation, if after step S402: selecting the parameter type in the first feedback information based on channel conditions, the first feedback information includes parameter measurement values ​​that only retain RSRQ and SINR, or L1-RSRP and SINR, the splicing can still be performed according to the preset order. For example, for the first feedback information that only retains the parameter measurement values ​​corresponding to the signal parameters RSRQ and SINR, if the user equipment 202 includes k specific user equipments, then for any specific user equipment, the sequences of RSRQ and SINR within any historical time window can be spliced ​​together to obtain the comprehensive signal measurement vector of that user equipment in any historical time window.

[0119]

[0120] For example, regarding the first feedback information that retains only the parameter measurement values ​​corresponding to the signal parameters L1-RSRP and SINR, if the user equipment 202 includes k specific user equipments, then for any specific user equipment, the sequences of L1-RSRP and SINR within any historical time window can be concatenated to obtain the comprehensive signal measurement vector of that user equipment within any historical time window:

[0121]

[0122] The first feedback information is concatenated in the order of L1-RSRP sequence-RSRQ sequence-SINR sequence, that is, the signal parameter L1-RSRP is placed at the beginning of the concatenation sequence. This allows the L1-RSRP to directly reflect the linear average power of the reference signal received by the user equipment 202 on a specific resource element. The other two signal parameters then provide supplementary information on signal quality. It should be noted that the above concatenation order is one possible implementation method provided by this application embodiment and does not constitute a specific limitation on the embodiments of this application. In other possible implementations, the preset concatenation order can be other schemes.

[0123] Furthermore, the integrated signal measurement vectors corresponding to user equipment 202 in N historical time windows are arranged into an integrated signal measurement sequence according to the temporal order of the historical time windows to obtain the integrated signal measurement sequence. For example, for the k-th specific user equipment in user equipment 202, the integrated signal measurement sequence can be obtained as follows:

[0124]

[0125] in, These represent the integrated signal measurement sequences of the k-th specific user equipment in the first, second, and third historical time windows, up to the Nth historical time window.

[0126] If the first feedback information only retains the signal parameter L1-RSRP, then it is only necessary to assemble the parameter measurement values ​​of L1-RSRP within N historical time windows in the first feedback information into a sequence to obtain the sequence:

[0127]

[0128] The L1-RSRP sequences corresponding to the user equipment 202 in N historical time windows are then combined into a comprehensive signal measurement sequence according to the chronological order of the historical time windows.

[0129] Based on the above preprocessing of the first feedback information, the final preprocessed first feedback information can be in sequence form, that is, the preprocessed first feedback information can be a comprehensive measurement sequence of user equipment 202. It should be noted that user equipment 202 may include one or more specific devices or chips, that is, user equipment 202 can be a general term for user equipment in any wireless communication system, or it can refer to a specific user equipment.

[0130] Optionally, the splicing process for the first feedback can also be performed by the user equipment 202. That is, after the user equipment measures the beams in the first beam set to obtain the first feedback information, it can first splice the first feedback information to obtain the first feedback information in the form of a comprehensive measurement sequence, and then send the first feedback information to the network device 101. The specific process can refer to the above process of splicing the first feedback information containing three signal parameters. Furthermore, the network device 101 can perform channel condition analysis and select the type of signal parameter based on the spliced ​​first feedback information to obtain the preprocessed first feedback information.

[0131] Step S305: Perform beam prediction based on the preprocessed first feedback information.

[0132] Specifically, network device 101 can predict beams using a beam prediction model, wherein the preprocessed first feedback information can be used as input to the beam prediction model.

[0133] In one possible implementation, the network device 101 may also receive beam prediction reference information to be input into the beam prediction model as auxiliary information for beam prediction. The reference information consists of parameters related to beam prediction, such as beam selection rules and quality assessment thresholds. This reference information may be preset relevant information that can guide the beam prediction process of the model.

[0134] In one possible implementation, network device 101 also needs to input a third beam set into the beam prediction model so that the beam prediction model can predict a second beam set and / or second prediction information from the third beam set, i.e., obtain the beam prediction result. Specifically, network device 101 can maintain a third beam set, which can be a dynamic beam set maintained by network device 101. The beams in the third beam set have a narrower width than the beams in the first beam set. The beams in the third beam set are a set of candidate beams for future data transmission to user equipment 202, i.e., the beams that actually establish a communication link with the user equipment in the wireless communication system. They have stronger directivity and higher gain in the target direction, and the number of beams in the third beam set is usually greater than the number of beams in the first beam set. In one possible implementation, the first beam set can be a wide beam set (Set B), and the third beam set can be a narrow beam set (Set A). The second beam set is a set of beams that meet preset conditions and are predicted from the third beam set by the beam prediction model in combination with reference information and the first feedback information. The second prediction information is the signal parameter measurement values ​​corresponding to the beams that meet preset conditions and are predicted from the third beam set by the beam prediction model in combination with reference information and the first feedback information. Optionally, the types of signal parameters included in the second prediction information can depend on the types of signal parameters included in the first feedback information input to the beam prediction model. That is, if the first feedback information input to the beam prediction model only includes one or more signal parameters among L1-RSRP, RSRQ, and SINR, then the second prediction information will also correspondingly include the same type of signal parameters. For example, after step S304: preprocessing the first feedback information, if the first feedback information only includes the parameter measurement values ​​corresponding to the signal parameters RSRQ and SINR and is input to the beam prediction model, then the second prediction value predicted by the beam prediction model will also only include the RSRQ and SINR measurement values ​​of the beams that meet preset conditions and are predicted from the third beam set. The preset conditions can vary depending on the beam prediction model used, and specific details can be found below. Figure 4B The corresponding description of the preset conditions is included in the instructions. Additionally, optionally, the first beam set, the second beam set, and the third beam set can be sets of corresponding beam identifiers, rather than actual sets of beams.

[0135] In one possible implementation, the beam prediction model can be a gated recurrent unit (GRU) model, a special type of recurrent neural network (RNN) that can be used to process sequential data (e.g., time series, natural language, etc.). See also [link to relevant documentation]. Figure 4A , Figure 4A This is a network structure diagram of a GRU beam prediction model provided in an embodiment of this application, such as... Figure 4A As shown, the GRU beam prediction model can include an input layer, which is the interface for interaction between the GRU beam prediction model and external data. It is responsible for receiving the input of the sequence data at each time step, such as receiving the first feedback information and reference information as input data. The GRU beam prediction model can also include GRU layers, which are the core of the GRU beam prediction model and are composed of multiple GRU units. They are used to process the sequence data and capture the long-term dependencies within it. For example, they can combine historical data to perform nonlinear transformations on the input data and pass it to the next layer. The GRU layer is a type of hidden layer. Furthermore, the GRU beam prediction model can also include fully connected layers, which are also a core component of the GRU beam prediction model and are also a type of hidden layer. They are used to perform linear transformations and nonlinear activations on the input data and historical data. Furthermore, the GRU beam prediction model can also include an output layer, which is used to generate the beam prediction results of the model. The beam prediction results can include a second beam set, a second prediction information, or a combination of both. The design of the output layer depends on the specific task type. For example, if the final beam prediction result output by the GRU beam prediction model only includes the second beam set, the output layer can use the normalized exponential (softmax) function. This activation function can convert the original values ​​of the output layer into probability values. That is, the output layer can output the selection probability of each beam in the third beam set. The selection probability mentioned here can be the suitability of the corresponding beam to transmit data to the user equipment 202 in the future time window. The beam prediction model can determine K beams that meet the preset conditions as the second beam set based on the selection probability of each beam in the third beam set. The preset conditions here can be: the top K beams with the highest selection probability in the third beam set, or: the K beams in the third beam set with a selection probability greater than a threshold (if there are more than K beams with a selection probability greater than the threshold, K beams can be randomly sampled from the beams with a selection probability greater than the threshold). If the final beam prediction result output by the GRU beam prediction model only includes the second prediction information, the output layer can use a linear activation function to directly output the second prediction information.

[0136] Specifically, compared to traditional recurrent neural network (RNN) models, the GRU model can capture the temporal changes of wireless signals through a gated recurrent mechanism. (See also...) Figure 4B , Figure 4B This is a schematic diagram of the input and output of a GRU network provided in an embodiment of this application. The GRU beam prediction model can be derived from the current input. and the hidden state of the previous time step The inputs constitute the current time step. Here, a time step refers to a discrete point in time within the sequence data, corresponding to an element in the sequence data. For example, the preprocessed first feedback information includes a comprehensive measurement sequence, and an element in this comprehensive measurement sequence can correspond to a time step. The hidden state refers to the internal state of the GRU beam prediction model at each time step, used to store historical information of the sequence. It is the core for capturing long-term relationships in the sequence (e.g., it can capture the temporal relationships within the first feedback information). The GRU beam prediction model combines... and The output of the current time step can be obtained. and the hidden state corresponding to the next time step .

[0137] Furthermore, compared to traditional recurrent neural network (RNN) models, such as long short-term memory networks (LSTM), the GRU model simplifies the model structure through reset gates and update gates (LSTM includes input gates, forget gates, and output gates), which reduces the number of parameters and thus the amount of computation, resulting in faster and more efficient inference.

[0138] Step S306: Determine the fourth beam set based on the beam prediction results.

[0139] Specifically, after obtaining the beam prediction result through the beam prediction model, network device 101 can determine the fourth beam set based on the beam prediction result. The fourth beam set is the set of beams that network device 101 ultimately transmits to user equipment 202. That is to say, network device 101 can obtain the beam prediction result through the beam prediction model, but the beam prediction result is predicted by the beam model and may not be entirely suitable for transmitting to user equipment 202 and establishing a communication link. Therefore, network device can determine the set of beams that are ultimately transmitted to user equipment 202 based on the beam prediction result and in combination with the actual communication status of the wireless communication system, which is the fourth beam set. Optionally, the fourth beam set can be a subset of the second beam set, or it can include one or more beams from the second beam set and one or more beams from the third beam set.

[0140] Step S307: Transmit the beam from the fourth beam set.

[0141] Furthermore, network device 101 can transmit beams from the fourth beam set to user equipment 202.

[0142] Step S308: Measure the fourth beam set to obtain the second feedback information.

[0143] Specifically, after receiving the beams from the fourth beam set transmitted by the network device 101, the user equipment 202 can measure the beams in the fourth beam set to obtain second feedback information. The second feedback information consists of the measured values ​​of the signal parameters L1-RSRP, RSRQ, and SINR of each beam in the fourth beam set, as measured by the user equipment 202. Optionally, the second feedback information can be the measured values ​​within the current time window, or the measured values ​​within one or more time windows, used to reflect the signal quality of the beams in the fourth beam set.

[0144] Step S309: Determine the optimal beam based on the second feedback information.

[0145] Specifically, user equipment 202 can determine the optimal beam in the fourth beam set based on the second feedback information. That is, by measuring the signal parameters of each beam in the fourth beam set, the beam with the best signal quality and most suitable for supporting the establishment of a communication link between network device 101 and user equipment 202 can be determined.

[0146] Alternatively, the determination of the optimal beam in the fourth beam set based on the second feedback information can be performed by the user equipment 202 or the network device 101. For example, after obtaining the second feedback information through measurement, the user equipment 202 can send the second feedback information to the network device 101, and the network device 101 can determine the optimal beam in the fourth beam set by analyzing the second feedback information.

[0147] Step S310: Report the identifier of the optimal beam.

[0148] Specifically, after determining the optimal beam, the user equipment 202 can send the identifier of the optimal beam to the network device to notify the network device 101 to establish a communication link with itself (i.e., user equipment 202) through the beam corresponding to the identifier.

[0149] Step S311: Transmit the optimal beam.

[0150] Specifically, after receiving the identifier of the optimal beam sent by the user equipment 202, the network device 101 can transmit the beam corresponding to the identifier, that is, the optimal beam, to the user equipment 202, and establish a communication link with the user equipment 202 through the optimal beam in order to transmit data to the user equipment 202.

[0151] It should be noted that the accompanying drawings mentioned above are merely exemplary drawings for illustrating the embodiments and do not constitute a specific limitation on this application.

[0152] In one possible implementation, the network device 101 can be a base station, the method can be applied to the base station, and the base station transmits data with the user. The user equipment here can be... Figure 2A The user equipment 202 shown can be found in [reference]. Figure 3C , Figure 3C This is a schematic flowchart of a base station-side beam management method provided in an embodiment of this application. The method may include:

[0153] 1. The base station scans the transmit beam in Set B.

[0154] Specifically, the base station can first scan the transmit beams in Set B to the user equipment. These transmit beams are the beams transmitted by the base station to the user equipment. Set B can be a wide beam set, for example, it could be... Figure 2B The wide beam set (Set B) 211 is shown. Further, the user equipment can measure the L1-RSRP, RSRQ, and SINR of each beam in Set B to obtain the corresponding measurement values. These corresponding measurement values ​​can serve as the first feedback information.

[0155] 2. Report the L1-RSRP, RSRQ, and SINR measurements for Set B.

[0156] Specifically, the user equipment reports the L1-RSRP, RSRQ, and SINR measurements of Set B to the base station. Optionally, these measurements can be the first feedback information corresponding to Set B. After receiving the L1-RSRP, RSRQ, and SINR measurements of Set B, the base station can perform Top-K beam prediction. The Top-K beam velocity is the K beams most suitable for transmitting data to the user equipment, predicted by the beam prediction model deployed in the base station.

[0157] Optionally, the beam prediction model deployed by the base station can be a GRU model, which can be trained using a GRU model. During beam prediction, the GRU beam prediction model can use a Top-K sampling algorithm. Top-K sampling is a sampling method that generates samples from a probability distribution. That is, optionally, the GRU beam prediction model can output the suitability (in probabilistic form) of multiple beams for transmitting data to the user equipment, and then the base station performs Top-K sampling on them to obtain the K most suitable beams for transmitting data to the user equipment. Using Top-K sampling can more readily select beams with higher probabilities, improving the quality of beam selection. Optionally, the K beams can constitute Set A.

[0158] 3. First K beam scans.

[0159] Specifically, the base station scans the predicted K beams to the user equipment, which is the beams in Set A. In one possible implementation, the user equipment can measure the L1-RSRP, RSRQ, and SINR of the K beams to obtain the corresponding measurement values, and determine the optimal beam among the K beams (Set A) based on the measurement values. The optimal beam is the beam with the best signal and the least interference for transmitting data to the user equipment.

[0160] 4. Report the identifier of the optimal beam in Set A.

[0161] Specifically, the user equipment reports the identifier of the optimal beam to the base station.

[0162] 5. Use the optimal beam in Set A for transmission.

[0163] Specifically, after receiving the identifier of the optimal beam reported by the user equipment, the base station can transmit the beam corresponding to the identifier (i.e., the optimal beam) to the user equipment and transmit data through the optimal beam. Further, the user equipment receives a scan of the optimal beam and, optionally, receives data transmitted by the base station through the optimal beam.

[0164] This application also provides a beam management method, which is applied to user equipment, including:

[0165] The receiver receives each beam from the first beam set transmitted by the network device and measures each beam from the first beam set to obtain first feedback information. The first feedback information includes the measured values ​​of the signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1.

[0166] Send the first feedback message to the network device.

[0167] In one possible implementation, the method further includes:

[0168] The network device receives each beam in the fourth beam set transmitted by the network device and measures each beam in the fourth beam set to obtain second feedback information. The fourth beam set is the beam set sent by the network device to the user equipment for determining the optimal beam. The second feedback information includes the measured values ​​of the signal parameters of each beam in the fourth beam set.

[0169] The optimal beam in the fourth beam set is determined based on the second feedback information;

[0170] The identifier of the optimal beam is sent to the network device.

[0171] It should be noted that the specific process of the beam management method described in the embodiments of this application can be found in the above. Figures 1-4B The relevant descriptions in the application embodiments described herein will not be repeated here.

[0172] This application also provides a beam prediction device 50, applied to network equipment, which can be found in [reference]. Figure 5A , Figure 5A This is a schematic diagram of a beam prediction device 50 provided in an embodiment of this application, as shown below. Figure 5A As shown, the beam prediction device 50 includes:

[0173] The receiving module 501 is used to receive first feedback information reported by the user equipment. The first feedback information includes the measured values ​​of signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1.

[0174] The prediction module 502 is used to perform beam prediction based on the first feedback information to obtain a beam prediction result. The beam prediction result includes a second beam set and / or second beam information. The second beam set is a subset of a third beam set. The third beam set is a set of candidate beams used by the network device for data transmission. The second beam information includes the predicted values ​​of the signal parameters of each beam in the second beam set.

[0175] The scanning module 503 is used to determine a fourth beam set based on the beam prediction result, and to scan the user equipment for beams in the fourth beam set. The fourth beam set is a beam set transmitted by the network device to the user equipment for determining the optimal beam. The fourth beam set includes one or more beams in the second beam set, or the fourth beam set includes one or more beams in the second beam set and the third beam set.

[0176] In one possible implementation, the receiving module 501 is further configured to: receive the identifier of the optimal beam reported by the user equipment, wherein the optimal beam is a beam in the fourth beam set.

[0177] This application also provides a beam prediction device 60, applied to user equipment, which can be found in [reference]. Figure 5B , Figure 5B This is a schematic diagram of another beam prediction device 60 provided in the embodiments of this application, as shown below. Figure 5B As shown, the beam prediction device 60 includes:

[0178] Measurement module 601 is used to measure each beam in the first beam set to obtain first feedback information. The first feedback information includes the measured values ​​of signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1.

[0179] Feedback module 602 is used to send first feedback information to the network device;

[0180] The communication module 603 is used to receive the optimal beam transmitted by the network device and establish a communication link based on the optimal beam.

[0181] In one possible implementation, the measurement module 601 is further configured to: measure each beam in the fourth beam set to obtain second feedback information, the fourth beam set being a beam set sent by the network device to the user equipment for determining the optimal beam, the second feedback information including the measured values ​​of the signal parameters of each beam in the fourth beam set.

[0182] In one possible implementation, the feedback module 602 is further configured to: send the second feedback information to the network device.

[0183] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a user equipment provided in an embodiment of this application. The user equipment may include a processor 710, an internal memory 720, an antenna 1, an antenna 2, a wireless communication module 730, and a wireless communication module 740, etc.

[0184] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the UE. In other embodiments, the UE may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0185] The processor 710 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0186] It is understood that the interface connection relationships between the modules illustrated in this embodiment are merely illustrative and do not constitute a limitation on the structure of the UE. In other embodiments of this application, the UE may also adopt different interface connection methods or a combination of multiple interface connection methods as described in the above embodiments.

[0187] The internal memory 720 can be used to store computer executable program code, including instructions. The processor 710 executes various functional applications and data processing of the UE by running the instructions stored in the internal memory 720. The internal memory 720 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc. The data storage area may store data created during UE use (such as channel state information), etc. Furthermore, the internal memory 720 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. The processor 710 executes various functions and data processing of the UE by running instructions stored in the internal memory 720 and / or instructions stored in memory located within the processor.

[0188] The UE's wireless communication function can be implemented through antenna 1, antenna 2, wireless communication module 730, wireless communication module 740, modem processor, and baseband processor.

[0189] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the UE can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with tuning switches.

[0190] The wireless communication module 730 can provide solutions for wireless communication applications including 2G / 3G / 4G / 9G on the UE. The wireless communication module 730 may include at least one filter, switch, power amplifier, low-noise amplifier (LNA), etc. The wireless communication module 730 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The wireless communication module 730 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the wireless communication module 730 may be housed in the processor 710. In some embodiments, at least some functional modules of the wireless communication module 730 and at least some modules of the processor 710 may be housed in the same device.

[0191] The wireless communication module 740 can provide solutions for wireless communication applications on user equipment, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 740 can be one or more devices integrating at least one communication processing module. The wireless communication module 740 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 710. The wireless communication module 740 can also receive signals to be transmitted from processor 710, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0192] Furthermore, an operating system runs on top of the aforementioned components. Examples include iOS, Android, and Windows operating systems. Applications can be installed and run on this operating system. Those skilled in the art will understand that, for the sake of convenience and brevity, explanations and beneficial effects of any of the UE components described above can be found in the corresponding method embodiments provided above, and will not be repeated here.

[0193] The following describes the structure of a network device 1000 provided in an embodiment of this application. Figure 7 This is a schematic diagram of the structure of a network device provided in an embodiment of this application.

[0194] like Figure 7 As shown, the network device may include: one or more network device processors 1001, memory 1002, communication interface 1003, receiver 1005, transmitter 1006, coupler 1007, antenna module 1008, and network device interface 1009. These components can be connected via bus 1004 or other means. Figure 7 Taking a bus connection as an example:

[0195] The communication interface 1003 can be used by the network device to communicate with other communication devices, such as user equipment. Specifically, the user equipment can be a cellular phone. Specifically, the communication interface 1003 can be a 5G communication interface or a future new radio (NR) communication interface. Not limited to wireless communication interfaces, the network device can also be configured with a wired communication interface 1003, such as a local access network (LAN) interface. The transmitter 1006 can be used to process the signals output by the network device processor 1001. The receiver 1005 can be used to process the mobile communication signals received by the antenna module 1008.

[0196] In some embodiments of this application, the transmitter 1006 and receiver 1005 can be considered as a wireless modem. In a network device, the number of transmitters 1006 and receivers 1005 can be one or more. The antenna module 1008 can be used to convert electromagnetic energy in a transmission line into electromagnetic waves in space, or to convert electromagnetic waves in space into electromagnetic energy in a transmission line. The coupler 1007 is used to split the mobile communication signal received by the antenna module 1008 into multiple paths and distribute them to multiple receivers 1005.

[0197] The memory 1002 is coupled to the network device processor 1001 and is used to store various software programs and / or multiple sets of instructions. Specifically, it may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0198] The network device processor 1001 can be used to read and execute computer-readable instructions. It may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a standalone semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may form a System-on-a-Chip (SoC) with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits), or it may be integrated as a built-in processor within an ASIC. This ASIC with integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), or logic circuits that implement dedicated logic operations. Specifically, the network device processor 1001 can be used to call a program stored in the memory 1002, such as the implementation program of the uplink resource configuration method on the network device side provided in one or more embodiments of this application, and execute the instructions contained in the program.

[0199] It should be noted that, Figure 7 The network device shown is merely one implementation of the embodiments of this application. In actual applications, the network device may include more or fewer components, which is not limited here.

[0200] It should be understood that each step in the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The method steps disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0201] This application also provides a terminal device, which may include a memory and a processor. The memory may be used to store computer programs; the processor may be used to invoke the computer programs in the memory, so that the terminal device executes the method executed on the terminal device side in any of the above embodiments.

[0202] This application also provides a terminal device, which may include a memory and a processor. The memory may be used to store computer programs; the processor may be used to invoke the computer programs in the memory, so that the terminal device executes the method executed on the terminal device side in any of the above embodiments.

[0203] This application also provides a chip system, which includes at least one processor for implementing the functions involved on the terminal device side in any of the above embodiments.

[0204] In one possible design, the chip system also includes a memory for storing program instructions and data, which may be located within or outside the processor.

[0205] The chip system can consist of chips or include chips and other discrete components.

[0206] Optionally, the chip system may include one or more processors. These processors can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0207] Optionally, the chip system may contain one or more memories. The memory may be integrated with the processor or disposed separately from it; this application does not limit this. For example, the memory may be a non-transient processor, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed separately on different chips. This application does not specifically limit the type of memory or the arrangement of the memory and processor.

[0208] For example, the chip system may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0209] This application also provides a computer program product, the computer program product comprising: a computer program (also referred to as code or instructions), which, when the computer program is run, causes the computer to perform the method executed on the terminal device side in any of the above embodiments.

[0210] This application also provides a computer-readable storage medium storing a computer program (also referred to as code or instructions). When the computer program is run, it causes the computer to perform the method executed on the terminal device side in any of the above embodiments.

[0211] The various embodiments of this application can be combined arbitrarily to achieve different technical effects.

[0212] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0213] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0214] In summary, the above description is merely an embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made based on the disclosure of this application should be included within the scope of protection of this application.

Claims

1. A beam management method, characterized in that, Applied to network devices, including: Receive first feedback information reported by user equipment. The first feedback information includes the measured values ​​of signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1. The channel conditions of the user equipment are determined based on the first feedback information; Based on the channel conditions, the first feedback information is preprocessed to obtain preprocessed first feedback information; the preprocessing of the first feedback information includes: selecting one or more parameters among L1-RSRP, RSRQ and SINR based on the channel conditions; and normalizing the measurement values ​​corresponding to the selected one or more parameters in the first feedback information according to the parameter type, so as to map the measurement values ​​corresponding to different types of parameters to different value ranges. The preprocessed first feedback information is input into the beam prediction model to output the beam prediction result; The beam prediction result includes a second beam set and / or second beam information, wherein the second beam set is a subset of a third beam set, and the third beam set is a set of candidate beams used by the network device for data transmission; the second beam information includes predicted values ​​of the signal parameters of each beam in the second beam set.

2. The method according to claim 1, characterized in that, The first feedback information includes the measured values ​​of the signal parameters of each beam in the first beam set in each of the N historical time windows, wherein one signal parameter of each beam corresponds to one or more measured values ​​in one historical time window.

3. The method according to claim 1, characterized in that, The method further includes: Receive reference information, which includes one or more of beam selection rules and parameter evaluation thresholds; The step of inputting the preprocessed first feedback information into the beam prediction model to output the beam prediction result includes: The preprocessed first feedback information and the reference information are input into the beam prediction model to output the beam prediction result.

4. The method according to claim 1, characterized in that, The second beam set includes K beams, where K is an integer greater than 1, and the K beams are the K beams in the third beam set that meet the preset conditions.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Based on the beam prediction results, a fourth beam set is determined. The fourth beam set is a beam set transmitted by the network device to the user equipment for determining the optimal beam. The fourth beam set includes one or more beams from the second beam set, or the fourth beam set includes one or more beams from the second beam set and the third beam set. The optimal beam is a beam from the fourth beam set. Transmit the beams from the fourth beam set to the user equipment; Receive the identifier of the optimal beam reported by the user equipment; The optimal beam is transmitted to the user equipment.

6. The method according to any one of claims 1-5, characterized in that, The beam prediction model is a gated cyclic unit (GRU) model.

7. A beam management method, characterized in that, Applied to user equipment, including: The receiver receives each beam from the first beam set transmitted by the network device and measures each beam from the first beam set to obtain first feedback information. The first feedback information includes the measured values ​​of the signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1. A first feedback message is sent to a network device; the first feedback message is used by the network device to determine the channel conditions of the user equipment based on the first feedback message; based on the channel conditions, the first feedback message is preprocessed to obtain preprocessed first feedback message; the preprocessed first feedback message is input into a beam prediction model to output a beam prediction result; wherein, the preprocessing of the first feedback message includes: selecting one or more parameters from L1-RSRP, RSRQ, and SINR based on the channel conditions; normalizing the measurement values ​​corresponding to the selected one or more parameters in the first feedback message according to the parameter type to map the measurement values ​​corresponding to different types of parameters to different value ranges; the beam prediction result includes a second beam set and / or second beam information, the second beam set is a subset of a third beam set, the third beam set is a set of candidate beams used by the network device for data transmission; the second beam information includes the predicted values ​​of the signal parameters of each beam in the second beam set.

8. The method according to claim 7, characterized in that, The method further includes: The network device receives each beam in the fourth beam set transmitted by the network device and measures each beam in the fourth beam set to obtain second feedback information. The fourth beam set is the beam set sent by the network device to the user equipment for determining the optimal beam. The second feedback information includes the measured values ​​of the signal parameters of each beam in the fourth beam set. The optimal beam in the fourth beam set is determined based on the second feedback information; The identifier of the optimal beam is sent to the network device.

9. A beam management device, characterized in that, Applied to network devices, the device includes: The receiving module is used to receive first feedback information reported by the user equipment. The first feedback information includes the measured values ​​of signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio (SINR), where N is an integer greater than or equal to 1. A prediction module is configured to determine the channel conditions of the user equipment based on the first feedback information; preprocess the first feedback information based on the channel conditions to obtain preprocessed first feedback information; input the preprocessed first feedback information into a beam prediction model to output a beam prediction result; the preprocessing of the first feedback information includes: selecting one or more parameters from L1-RSRP, RSRQ, and SINR based on the channel conditions; normalizing the measurement values ​​corresponding to the selected one or more parameters in the first feedback information according to the parameter type to map the measurement values ​​corresponding to different types of parameters to different value ranges; The beam prediction result includes a second beam set and / or second beam information, wherein the second beam set is a subset of a third beam set, and the third beam set is a set of candidate beams used by the network device for data transmission; the second beam information includes predicted values ​​of the signal parameters of each beam in the second beam set.

10. The apparatus according to claim 9, characterized in that, The device further includes: The scanning module is used to determine a fourth beam set based on the beam prediction result and scan the beams in the fourth beam set to the user equipment. The fourth beam set is a beam set transmitted by the network device to the user equipment for determining the optimal beam. The fourth beam set includes one or more beams in the second beam set, or the fourth beam set includes one or more beams in the second beam set and the third beam set.

11. The apparatus according to claim 10, characterized in that, The receiving module is further configured to: receive the identifier of the optimal beam reported by the user equipment, wherein the optimal beam is a beam in the fourth beam set.

12. A beam management device, characterized in that, Applied to user equipment, the device includes: The measurement module is used to measure each beam in the first beam set to obtain first feedback information. The first feedback information includes the measured values ​​of the signal parameters of the first beam set within N historical time windows. The signal parameters include physical layer reference signal received power L1-RSRP, reference signal received quality RSRQ, and signal-to-interference-plus-noise ratio SINR, where N is an integer greater than or equal to 1. A feedback module is used to send first feedback information to a network device; the first feedback information is used by the network device to determine the channel conditions of the user equipment; based on the channel conditions, the first feedback information is preprocessed to obtain preprocessed first feedback information; the preprocessed first feedback information is input into a beam prediction model to output a beam prediction result; wherein, the preprocessing of the first feedback information includes: selecting one or more parameters from L1-RSRP, RSRQ, and SINR based on the channel conditions; normalizing the measurement values ​​corresponding to the selected one or more parameters in the first feedback information according to the parameter type to map the measurement values ​​corresponding to different types of parameters to different value ranges; the beam prediction result includes a second beam set and / or second beam information, the second beam set being a subset of a third beam set, the third beam set being a set of candidate beams used by the network device for data transmission; the second beam information includes the predicted values ​​of the signal parameters of each beam in the second beam set; A communication module is used to receive the optimal beam emitted by the network device and establish a communication link based on the optimal beam.

13. The apparatus according to claim 12, characterized in that, The measurement module is also used for: The second feedback information is obtained by measuring each beam in the fourth beam set, which is a beam set sent by the network device to the user equipment for determining the optimal beam. The second feedback information includes the measured values ​​of the signal parameters of each beam in the fourth beam set.

14. The apparatus according to claim 13, characterized in that, The feedback module is further configured to: send the second feedback information to the network device.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 7 and 8.

17. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 6.

18. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the method as described in any one of claims 7 and 8.

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