Methods, apparatus, electronic devices and storage media for determining downlink transmission beams

Abstract

This application proposes a method, apparatus, electronic device, and storage medium for determining downlink transmission beams, relating to the fields of 5G communication and data processing technology. The method includes: determining the type of user equipment (UE) based on channel state information; adding a non-convex penalty term to a first optimization objective function for beamforming based on the UE type to obtain a second optimization objective function; decomposing the second optimization objective function to obtain a first solution task corresponding to a convex function and a second solution task for the non-convex penalty term; iteratively solving the first and second solution tasks to obtain a first beamforming vector; and determining the downlink transmission beam pointing to the UE based on the first beamforming vector. This application can reduce computational complexity, improve convergence speed, adapt to rapid channel changes when facing a large number of UEs and rapidly changing channel conditions, improve system spectral efficiency, and enhance beamforming performance in dynamic environments.

Description

Technical Field

[0001] This application relates to the fields of 5G communication and data processing technology, and in particular to a method, apparatus, electronic device and storage medium for determining downlink transmission beams. Background Technology

[0002] In related technologies, downlink transmission beamforming design in 5G multi-user multiple-input multiple-output (MIMO) systems typically relies on convex optimization algorithms. The core idea is to construct an optimization model to improve system performance by maximizing the signal-to-interference-plus-noise ratio (SINR). Specifically, this method aims to maximize the SINR of each user device to find an optimal beamforming vector (BFV) configuration among multiple user devices, achieving interference coordination and rational resource allocation among them. The Lagrange multiplier method is a commonly used solution approach. It integrates constraints into the objective function to form a Lagrange function, then calculates partial derivatives with respect to each variable and solves the system of equations to find the optimal solution. However, when faced with a large number of user equipment and rapidly changing channel conditions, a large number of matrix operations and iterative calculations are required, which may result in a heavy computational burden on the base station (BS) and limit the scalability of the system. Multiple iterations are required to achieve convergence, making it difficult to adapt to rapid changes in the channel and affecting the beamforming effect in dynamic environments.

[0003] Therefore, reducing computational complexity, improving convergence speed, effectively suppressing interference between user equipment, adapting to rapid channel changes when facing a large number of user equipment and rapidly changing channel conditions, improving system spectral efficiency, and enhancing beamforming performance in dynamic environments have become important research directions. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art. The technical solution of this application is as follows: The first aspect of this application proposes a method for determining a downlink transmission beam, comprising: For one user equipment or each of multiple user equipments, obtain the channel state information transmitted by the user equipment, and determine the type of user equipment based on the channel state information; Based on the type of user equipment, a non-convex penalty term is added to the first optimization objective function of beamforming to obtain the second optimization objective function. The second optimization objective function is decomposed to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term; The first and second solution tasks are solved iteratively to obtain the first beamforming vector; Based on the first beamforming vector, the downlink transmission beam pointing to the user equipment is determined.

[0005] A second aspect of this application provides a downlink transmission beam determination apparatus, comprising: The information acquisition module is used to acquire channel status information sent by a user equipment or each of multiple user equipments, and to determine the type of user equipment based on the channel status information. The non-convex optimization processing module is used to add a non-convex penalty term to the first optimization objective function of beamforming according to the type of user equipment, so as to obtain the second optimization objective function; The decomposition module is used to decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term; The iterative solution module is used to iteratively solve the first and second solution tasks to obtain the first beamforming vector; The beamforming processing module is used to determine the downlink transmission beam pointing to the user equipment based on the first beamforming vector.

[0006] A third aspect of this application provides an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement a downlink transmission beam determination method as provided in a first aspect of this application.

[0007] A fourth aspect of this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the downlink transmission beam determination method provided in the first aspect of this application.

[0008] This application decomposes the complex beamforming optimization problem into multiple easily manageable sub-problem solving tasks and dynamically adjusts the algorithm parameters to adapt to channel changes, reducing computational complexity and significantly improving convergence speed. At the same time, it effectively suppresses interference between user equipment, thereby significantly improving the system's spectral efficiency. It also distinguishes between primary user equipment and interfering user equipment by user equipment type, improving the overall system performance and enhancing the system's scalability.

[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0010] Figure 1 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application; Figure 2 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application; Figure 3 This is a schematic diagram of the iterative solution process of one embodiment of this application; Figure 4 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application; Figure 5 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application; Figure 6 This is a structural block diagram of a downlink transmission beam determination device according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0011] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0012] The acquisition, storage, and application of information and data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0013] The following is an explanation of the terms used in this application: QoS (Quality of Service) refers to a network's ability to provide better service for specified network communications by utilizing various underlying technologies. It is a network security mechanism and a technology used to solve problems such as network latency and congestion.

[0014] The following description, in conjunction with the accompanying drawings, outlines the method, apparatus, electronic device, and storage medium for determining the downlink transmission beam according to embodiments of this application.

[0015] Figure 1 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps: S101: For one user equipment or each of multiple user equipments, obtain the channel state information transmitted by the user equipment, and determine the type of user equipment based on the channel state information.

[0016] Optionally, Channel State Information (CSI) includes parameters such as channel gain, channel delay, and channel phase between the user equipment and the network equipment (the network equipment in this application may refer to a 5G base station).

[0017] In this embodiment, within the coverage area of ​​the 5G base station, each user equipment (UE) periodically feeds back the Channel Gain Index (CSI) to the base station. The feedback period can be 10ms or other periods; this application does not impose any restrictions on this. The fed-back CSI includes: the channel gain matrix H between the user equipment and the base station, whose elements h... ij The channel gain between the i-th antenna and the j-th user is represented by the value [0,1]; the channel delay matrix τ has elements τ ij The channel delay between the i-th antenna and the j-th user is represented in nanoseconds; the channel phase matrix φ has elements φ ij This represents the channel phase between the i-th antenna and the j-th user, in radians. Optionally, the base station receives and stores the CSI data transmitted by the user equipment, and establishes a CSI database.

[0018] In some implementations, signal and interference analysis is performed based on channel state information to calculate indicators such as signal strength and interference strength for each user equipment, and the type of user equipment is determined based on the indicators such as signal strength and interference strength. In the embodiments of this application, the types of user equipment include a first type with higher signal strength (which can be understood as the main user equipment) and a second type with higher interference strength (which can be understood as the interfering user equipment).

[0019] S102, based on the type of user equipment, add a non-convex penalty term to the first optimization objective function of beamforming to obtain the second optimization objective function.

[0020] In some implementations, a non-convex penalty term is introduced, depending on the type of user equipment. The non-convex penalty term has the form Σ. (|x|), where x represents the beamforming vector. ( ) represents a non-convex penalty function, using the L1 / 2 norm, i.e. Add it to the first optimization objective function.

[0021] Here, the L1 / 2 norm is a sparsity penalty term between the L1 and L2 norms; the first optimization objective function is the beamforming optimization objective function. Optionally, the beamforming vector is obtained after eigenvalue decomposition of the channel state information.

[0022] Therefore, we can obtain a new optimization objective function, namely the second optimization objective function: Here, `maximize()` is a function that declares the optimization objective and is used to find the variable values ​​that maximize the objective function. Let represent the beamforming vector of the k-th user equipment. Let be the signal-to-noise ratio of the k-th user equipment, and λ be a tradeoff parameter used to control the influence of the non-convex penalty term on the optimization objective. Optionally, the tradeoff parameter can be set to 0.5, and in other implementations, it can also be other values, which are not limited in this application.

[0023] For example, with the tradeoff parameter set to 0.5, for 10 user devices in a commercial area scenario, the second optimization objective function is established as follows: .

[0024] Since the primary user channel has good quality, it can be the focus of optimization, while the interfering user channel has poor quality and is not a primary consideration during the optimization process. In this embodiment, a non-convex penalty term is added to the first optimization objective function of beamforming according to the type of user equipment, so as to facilitate differentiated treatment for the two cases and facilitate the rapid derivation of the optimal solution.

[0025] S103, decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term.

[0026] In some implementations, the second optimization objective function is decomposed and expressed as follows: , where the convex function part Non-convex penalty term And it satisfies the constraint Ax + Bz = c, where A, B, and c are coefficient matrices and vectors, and x and z represent beamforming vector elements. This represents the beamforming vector element of the k-th user equipment.

[0027] In this application, the second optimization objective function is decomposed into two alternately solved subproblems, namely the first solution task and the second solution task, by means of the Alternating Direction Method of Multipliers (ADMM).

[0028] For example, suppose there are 10 user devices (User Equipment) in a commercial area scenario, and the base station has 4 antennas. Each User Equipment corresponds to 4 beamforming vector elements. Then, x and z are both 4×10 matrices (4 antennas, 10 User Equipment). The coefficient matrices A and B can be set according to power constraints and QoS constraints, and the vector c can be set according to the service requirements of the User Equipment. For example, the power constraint is... , Indicates the maximum transmit power limit. It can be set to 10W, or other values ​​in other implementations. For each user device k, the Quality of Service (QoS) constraint is: ≥ _min, Minimum signal-to-noise ratio limit _min can be set to 2dB, or other values ​​in other implementations. Let i be the beamforming vector element of the k-th user equipment on the i-th antenna, where i represents the i-th antenna and takes values ​​from 1 to 4.

[0029] S104, iteratively solve the first and second solution tasks to obtain the first beamforming vector.

[0030] In this embodiment of the application, the complex large-scale non-convex optimization problem is decomposed into multiple sub-problems that are easy to solve, namely the first solution task and the second solution task. By iteratively solving the first solution task and the second solution task, the optimal beamforming vector is gradually approximated to obtain the first beamforming vector.

[0031] S105, determine the downlink transmission beam pointing to the user equipment based on the first beamforming vector.

[0032] In this embodiment of the application, the beamforming vector of each user equipment is calculated based on the first beamforming vector to determine the beam direction and power allocation.

[0033] Optionally, the calculated beamforming vector for each user device can be applied to the transmitter of a 5G base station. By adjusting parameters such as the amplitude and phase of the transmitted signal, downlink transmission beams pointing to different user devices can be generated to achieve precise downlink transmission.

[0034] In this embodiment, the complex beamforming optimization problem is decomposed into multiple easily handled sub-problem solving tasks, and the algorithm parameters are dynamically adjusted to adapt to changes in the channel, which reduces computational complexity and significantly improves convergence speed. At the same time, it effectively suppresses interference between user equipment, thereby significantly improving the system's spectral efficiency. It also distinguishes between primary user equipment and interfering user equipment by user equipment type, improving the overall system performance and enhancing the system's scalability.

[0035] Figure 2 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application, as follows: Figure 2 As shown, the method includes the following steps: S201: For one user equipment or each of multiple user equipments, obtain the channel state information transmitted by the user equipment, and determine the type of user equipment based on the channel state information.

[0036] S202, based on the type of user equipment, a non-convex penalty term is added to the first optimization objective function of beamforming to obtain the second optimization objective function.

[0037] S203, decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term.

[0038] For a description of steps S201 to S203, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0039] S204, based on the Alternating Direction Multiplier Method (ADMM) algorithm, iteratively solves the first and second solution tasks. When the beamforming vector of the current iteration satisfies the convergence condition, the iteration stops and the first beamforming vector is obtained.

[0040] like Figure 3 As shown in this embodiment, during the iterative solution process of the first and second solution tasks, the rate of change of channel state information and the service priority of user equipment are determined. Based on the rate of change and service priority, the parameters in the ADMM algorithm are adaptively iterated. Optionally, in response to the rate of change being greater than a set speed threshold, and / or the service priority being higher than a set priority, specified parameters in the ADMM algorithm are increased. In response to the rate of change being less than or equal to the set speed threshold, and / or the service priority not being higher than the set priority, the parameters in the ADMM algorithm are maintained. If the convergence condition is not met, iteration continues until the convergence condition is met, at which point iteration stops to obtain the first beamforming vector. Optionally, the parameters in the ADMM algorithm include a step size factor, and / or a trade-off parameter. A detailed explanation follows.

[0041] For each user device k, initialize the variable and dual variables In each iteration, first fix... ,renew By solving the convex optimization subproblem:

[0042] The gradient descent method is used to solve the problem, with a step size of 0.1 and 100 iterations. Other values ​​may be used in other implementations.

[0043] Furthermore, fix and ,renew By solving the nonconvex optimization subproblem:

[0044] The non-convex optimization algorithm (proximal gradient method) is used, with a step size of 0.05 and 50 iterations. Other values ​​can be used in other implementations.

[0045] Furthermore, update the dual variable:

[0046] Where ρ is the step size factor in the ADMM algorithm, set to 0.2, but it can be other values ​​in other implementations.

[0047] For example, suppose there are 10 user devices in a commercial area scenario, and the base station has 4 antennas. In the first iteration, initially... It is a matrix of all ones. It is a matrix of all zeros. It is a matrix consisting entirely of zeros. It is updated using gradient descent. Calculate the gradient ,in Let be the channel matrix of the k-th user equipment. Then, update it using the proximal gradient method. Calculate non-convex proximal operations:

[0048] Last Update Repeat the iteration until the convergence condition is met (the convergence condition can be, for example, the difference between the beamforming vectors of two consecutive iterations being less than 1e-4).

[0049] Furthermore, by adaptively iteratively adjusting parameters, the parameters in the ADMM algorithm are dynamically adjusted based on the rate of change of channel state information and the priority of user service needs (service priority). For example, if the rate of change is greater than a set speed threshold, it indicates that the channel state is changing rapidly (e.g., user equipment is moving at high speeds exceeding 30 km / h). The step size factor ρ is then increased (e.g., from 0.2 to 0.3) to accelerate the convergence speed of the algorithm and track channel changes in a timely manner.

[0050] In some implementations, for high-priority user equipment services (such as real-time video conferencing, where the service priority is set to the highest), a larger tradeoff parameter λ is assigned (e.g., increasing the tradeoff parameter from 0.5 to 0.7), making the non-convex penalty term more inclined to protect the user equipment's signal and reduce interference. Optionally, channel state changes are determined by CSI data fed back by the user equipment, fed back every 10ms, observing the difference between the current and previous CSI data; a larger difference within a preset time indicates a faster channel state change.

[0051] For example, suppose there are 10 user devices in a commercial area scenario, and the base station has 4 antennas. If user device 5 (the 5th user device) is detected to be moving at a speed of 40 km / h, exceeding the set speed threshold of 30 km / h, then ρ will be adjusted from 0.2 to 0.3. Furthermore, if user device 5 is conducting a real-time video conference with the highest service priority, then λ will be adjusted from 0.5 to 0.7. The adjusted parameters are used in subsequent ADMM iterations.

[0052] Furthermore, the convergence of the ADMM algorithm is monitored. This is done by calculating the difference between the beamforming vectors of two consecutive iterations (e.g., ...). The difference between the objective function value and the target function value (e.g.) The algorithm determines whether the convergence condition is met (e.g., the difference is less than 1e-4). If not, it continues to adjust the parameters and iterate; if so, it outputs the converged beamforming vector. Specific example: In a commercial area scenario, after 20 iterations, the calculated... and Determine if the convergence condition is met, and output the first beamforming vector after convergence. and ).

[0053] S205, based on the first beamforming vector, determine the control parameters corresponding to each antenna in the network device.

[0054] In some implementations, power constraints and / or quality of service constraints are determined, and a first beamforming vector is optimized based on these constraints to obtain a second beamforming vector. Specifically, the amplitude and phase information of the beam corresponding to the user equipment are determined based on the first beamforming vector, and the amplitude and phase information of the beam corresponding to the user equipment are optimized based on the constraints to obtain the second beamforming vector.

[0055] Furthermore, based on the second beamforming vector, the control parameters corresponding to each antenna in the network device are determined; specifically, the total transmit power of the network device is determined, the beamforming vector components corresponding to each antenna in the network device are determined based on the second beamforming vector, and the control parameters corresponding to each antenna in the network device are determined based on the total transmit power and the beamforming vector components corresponding to each antenna.

[0056] Specifically, based on the converged first beamforming vector obtained from ADMM decomposition ( and This will be further processed and integrated. The elements in the vector correspond to the beamforming vectors of different user equipment, and the amplitude and phase values ​​of the second beamforming vector of each user equipment are calculated.

[0057] For example, for the second beamforming vector of the j-th user equipment Its magnitude | According to The modulus of the corresponding element is determined, and the phase arg( )according to The angle information of the corresponding element is determined.

[0058] Assuming there are 10 user devices in a commercial area scenario and the base station has 4 antennas, the second beamforming vector of user device 1 is obtained. The amplitude is The phase is arg( =angle( [1,1])=π / 4. Where, This indicates that the i-th antenna corresponds to the k-th user equipment. .

[0059] Furthermore, considering power constraints and Quality of Service (QoS) constraints, the beamforming vectors are finally adjusted and optimized. This ensures that the second beamforming vector for each user equipment meets the base station's maximum transmit power limit (e.g., ...). =10W), and ensure the minimum data rate requirement of user equipment (e.g., the minimum data rate of user equipment 1 is 10Mbps). For example, by adjusting the amplitude and phase of the beamforming vector, the power distribution of each user equipment is optimized. For each user device k, its corresponding ≥ _min (e.g.) _min=2dB). This represents the amplitude of the second beamforming vector corresponding to the k-th user equipment for the i-th antenna.

[0060] Furthermore, the second beamforming vector is converted into control parameters for the network device (base station) transmitter. For each antenna, the power amplifier and phase controller of the transmitted signal are adjusted according to the amplitude and phase values ​​of the second beamforming vector. Assuming there are 10 user equipment units in a commercial area scenario and the base station has 4 antennas, for the i-th antenna, its transmit power is set... for:

[0061] in This represents the total transmit power of the base station, which can be set to 40W or other values. The phase is... =arg( ).

[0062] S206 controls the transmitting end of the network device according to the control parameters corresponding to the antenna to generate a downlink transmission beam pointing to the user equipment.

[0063] The base station's transmitter generates beams directed at different user devices based on the control parameters corresponding to the antennas. By having multiple antennas work together, signal energy is concentrated in a specific direction, enabling precise downlink transmission to different user devices.

[0064] Furthermore, real-time monitoring of beam transmission performance, such as the signal quality received by user equipment and user data rate, provides feedback for subsequent beamforming optimization. In real-world 5G networks, channel conditions can change rapidly due to various factors such as user equipment movement and environmental variations. This application combines the channel characteristics of 5G multi-user MIMO systems with user needs to dynamically adjust the parameters of the ADMM algorithm, such as the step size factor and penalty parameters, to improve the algorithm's convergence speed and performance, enhance beamforming performance in dynamic environments, and adapt to rapid channel changes. The system can find the optimal beamforming method in a shorter time, quickly respond to channel changes, adapt to complex communication environments, and improve the system's real-time performance and flexibility. By adjusting parameters such as the amplitude and phase of the transmitted signal, beams pointing towards different user equipment can be generated, enabling precise downlink transmission and reducing the risk of signal interruption.

[0065] Figure 4 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application, as follows: Figure 4 As shown, the method includes the following steps: S401: For one user equipment or each of multiple user equipments, obtain the channel state information transmitted by the user equipment.

[0066] For a description of steps S401 to S403, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0067] S402, preprocesses the channel state information.

[0068] In some implementations, CSI data may be preprocessed to improve data quality and usability. For example, invalid data packets can be removed from CSI data, and data with too low signal strength or incomplete structure can be eliminated; for another example, missing items in CSI data can be filled by using linear interpolation or forward padding to supplement missing CSI values, etc.

[0069] S403 performs signal and interference analysis on the preprocessed channel state information to obtain the signal-to-noise ratio of the user equipment.

[0070] In some implementations, signal and interference analysis is performed based on the preprocessed CSI data to obtain signal power and interference power. Optionally, the signal power and interference power can be calculated based on the signal gain of the user equipment. Here, signal power refers to the signal gain between each antenna and the user, and interference power refers to the sum of the signal gains between each antenna and other users.

[0071] For example, for the k-th user equipment, calculate the received signal power. With interference power The ratio, optionally, can be obtained by using the following formula to obtain the signal-to-noise ratio of the k-th user. :

[0072] S404 determines the type of user equipment based on the signal-to-noise ratio.

[0073] Optionally, the type of user equipment can be determined based on a preset signal-to-noise ratio threshold and the relationship between the signal-to-noise ratio and the value of the signal-to-noise ratio.

[0074] In this application, the user equipment types include a first type with higher signal strength (which can be understood as the primary user equipment) and a second type with higher interference strength (which can be understood as the interfering user equipment). For user equipment with a signal-to-noise ratio (SNR) greater than or equal to the SNR threshold, the user equipment type is determined to be the first type; for user equipment with a SNR less than the SNR threshold, the user equipment type is determined to be the second type, thereby determining whether the user equipment is the primary user equipment or the interfering user equipment.

[0075] Optionally, the signal-to-noise ratio threshold can be 3dB, or other values ​​in other implementations, and this application does not impose any restrictions on this.

[0076] In some implementations, the signal-to-noise ratio (SNR) of a user equipment (UE) can be directly obtained from the channel gain matrix in the preprocessed CSI data. Taking the first UE as an example with an SNR threshold of 3dB, if the SNR of the first UE... for: , in, [i, j] represents the channel gain matrix corresponding to the k-th user equipment for the i-th antenna. If the value is >3db, then the type of the first user device is the first type, and the first user device is the primary user device.

[0077] S405, based on the type of user equipment, a non-convex penalty term is added to the first optimization objective function of beamforming to obtain the second optimization objective function.

[0078] S406, decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term.

[0079] S407, iteratively solve the first and second solution tasks to obtain the first beamforming vector.

[0080] S408, based on the first beamforming vector, determines the downlink transmission beam pointing to the user equipment.

[0081] For a description of steps S405 to S408, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0082] In this embodiment of the application, the main user equipment and interfering user equipment are distinguished by the type of user equipment, which can effectively suppress interference, effectively reduce mutual interference between user equipment, significantly improve communication quality, ensure the stability and reliability of data transmission of user equipment, improve the overall performance of the system, and enhance the scalability of the system.

[0083] Figure 5 This is a flowchart of a downlink transmission beam determination method according to an embodiment of this application, as follows: Figure 5 As shown, the method includes the following steps: S501: For one user equipment or each of multiple user equipments, obtain the channel state information transmitted by the user equipment, and determine the type of user equipment based on the channel state information.

[0084] S502, based on the type of user equipment, a non-convex penalty term is added to the first optimization objective function of beamforming to obtain the second optimization objective function.

[0085] S503, decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term.

[0086] S504, iteratively solve the first and second solution tasks to obtain the first beamforming vector.

[0087] S505, based on the first beamforming vector, determines the downlink transmission beam pointing to the user equipment.

[0088] For a description of steps S501 to S505, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0089] S506 collects performance feedback information from user equipment.

[0090] During beamforming, performance feedback information from user equipment is continuously collected. This includes metrics such as the signal strength received by the user equipment (e.g., the signal strength of user equipment 1 is -60dBm), bit error rate (e.g., the bit error rate of user equipment 1 is 1e-5), and data throughput (e.g., the data throughput of user equipment 1 is 15Mbps).

[0091] S507 performs a quality assessment of the downlink transmission beam of user equipment based on performance feedback information.

[0092] The actual effect of beamforming is evaluated through performance feedback information, such as the degree of reduction in interference between users and the improvement in system spectral efficiency. This application can reduce interference between user equipment and improve system spectral efficiency.

[0093] S508 optimizes the downlink transmission beam of user equipment based on performance feedback information.

[0094] In some implementations, the downlink transmission beam of the user equipment can be quality assessed based on performance feedback information, and the downlink transmission beam of the user equipment can be optimized based on the quality assessment results.

[0095] For example, if the spectral efficiency of user equipment (UE) in a specific area is found to be low (e.g., UE 7's spectral efficiency is 8 bps / Hz, lower than the average of 12 bps / Hz), it may be due to poor channel conditions or high UE density in that area. Adjusting the parameter λ of the non-convex penalty term to 0.6 or the parameter ρ of the ADMM algorithm to 0.25 can optimize the beamforming vector calculation process. Alternatively, for a specific UE, adjusting the antenna weights corresponding to its beamforming vector can improve the signal reception quality of that UE. Simultaneously, this feedback information is stored in a database to provide more accurate channel information and UE requirement data for subsequent beamforming design.

[0096] In some implementations, the downlink transmission beam of the user equipment can be quality assessed based on performance feedback information. In some implementations, the downlink transmission beam of the user equipment can be optimized based on performance feedback information. In some implementations, the downlink transmission beam of the user equipment can be quality assessed based on performance feedback information, and the downlink transmission beam of the user equipment can be optimized based on the quality assessment results; this application does not impose any limitations on this.

[0097] This application assesses the quality of the downlink transmission beam of user equipment (UE) based on performance feedback information. Following this assessment, it optimizes the downlink transmission beam, achieving more efficient communication within limited spectrum resources and meeting the communication needs of a wider range of UEs, particularly suitable for scenarios with high UE density. Furthermore, this application improves system stability and reliability, better responding to rapid channel changes, ensuring the continuous effectiveness of beamforming, and reducing the risk of signal interruption.

[0098] Figure 6 This is a structural block diagram of a downlink transmission beam determination device according to an embodiment of this application, as shown below. Figure 6 As shown, the downlink transmission beam determination device 600 includes: The information acquisition module 610 is used to acquire channel state information sent by a user equipment for one user equipment or each of multiple user equipments, and to determine the type of user equipment based on the channel state information. The non-convex optimization processing module 620 is used to add a non-convex penalty term to the first optimization objective function of beamforming according to the type of user equipment, so as to obtain the second optimization objective function; The decomposition module 630 is used to decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term; The iterative solution module 640 is used to iteratively solve the first solution task and the second solution task to obtain the first beamforming vector; The beamforming processing module 650 is used to determine the downlink transmission beam pointing to the user equipment based on the first beamforming vector.

[0099] In some implementations, the iterative solution module 640 is also used for: Based on the Alternating Direction Multiplier (ADMM) algorithm, the first and second solution tasks are solved iteratively. The iteration stops when the beamforming vector of the current iteration satisfies the convergence condition, and the first beamforming vector is obtained.

[0100] In some implementations, the iterative solution module 640 is used for: Determine the rate of change of channel state information and the service priority of user equipment; The parameters in the ADMM algorithm are adaptively iterated based on the rate of change and business priority.

[0101] In some implementations, the iterative solution module 640 is used for: In response to a change rate greater than a set speed threshold, and / or a service priority higher than a set priority, the parameters in the ADMM algorithm are increased; In response to a change rate less than or equal to a set speed threshold, and / or a service priority not higher than a set priority, the parameters in the ADMM algorithm are maintained.

[0102] In some implementations, the information acquisition module 610 is further configured to: Preprocess the channel state information; Signal and interference analysis is performed on the preprocessed channel state information to obtain the signal-to-noise ratio of the user equipment. The type of user equipment is determined based on the signal-to-noise ratio.

[0103] In some embodiments, the beamforming processing module 650 is further configured to: Based on the first beamforming vector, determine the control parameters corresponding to each antenna in the network device; Based on the control parameters corresponding to the antenna, the transmitting end of the network device is controlled to generate a downlink transmission beam pointing to the user equipment.

[0104] In some embodiments, the beamforming processing module 650 is further configured to: Determine power constraints and / or quality of service constraints; Based on power constraints and / or quality of service constraints, the first beamforming vector is optimized to obtain the second beamforming vector; Based on the second beamforming vector, determine the control parameters corresponding to each antenna in the network device.

[0105] In some embodiments, the beamforming processing module 650 is further configured to: Determine the total transmit power of the network equipment; Based on the second beamforming vector, determine the beamforming vector components corresponding to each antenna in the network device; Based on the total transmit power and the beamforming vector components corresponding to each antenna, the control parameters corresponding to each antenna in the network device are determined.

[0106] In some embodiments, the beamforming processing module 650 is further configured to: Based on the first beamforming vector, determine the amplitude and phase information of the beam corresponding to the user equipment; Based on the constraints, the amplitude and phase information of the beam corresponding to the user equipment are optimized to obtain the second beamforming vector.

[0107] In some embodiments, the downlink transmission beam determination device 600 further includes a feedback optimization module 660, used for: Collect performance feedback information from user equipment; Based on performance feedback information, the quality of the downlink transmission beam of the user equipment is assessed; and / or, Based on performance feedback, the downlink transmission beam of the user equipment is optimized.

[0108] In this embodiment, the complex beamforming optimization problem is decomposed into multiple easily handled sub-problem solving tasks, and the algorithm parameters are dynamically adjusted to adapt to changes in the channel, which reduces computational complexity and significantly improves convergence speed. At the same time, it effectively suppresses interference between user equipment, thereby significantly improving the system's spectral efficiency. It also distinguishes between primary user equipment and interfering user equipment by user equipment type, improving the overall system performance and enhancing the system's scalability.

[0109] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0110] like Figure 7 As shown, the electronic device 700 includes: The memory 701 and the processor 702 are connected by a bus 703, which connects different components (including the memory 701 and the processor 702). The memory 701 stores a computer program, and when the processor 702 executes the program, it implements the downlink transmission beam determination method of the present application embodiment.

[0111] Bus 703 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0112] Electronic device 700 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0113] Memory 701 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 704 and / or cache memory 705. Electronic device 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 706 can be used to read and write non-removable, non-volatile magnetic media (… Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 703 via one or more data media interfaces. Memory 701 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0114] A program / utility 708 having a set (at least one) of program modules 707 may be stored, for example, in memory 701. Such program modules 707 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 707 typically perform the functions and / or methods described in the embodiments of this application.

[0115] Electronic device 700 can also communicate with one or more external devices 709 (e.g., keyboard, pointing device, display 711, etc.), and with one or more devices that enable a user to interact with the electronic device 700, and / or with any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 712. Furthermore, electronic device 700 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 713. Figure 7 As shown, network adapter 713 communicates with other modules of electronic device 700 via bus 703. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0116] The processor 702 executes various functional applications and data processing by running programs stored in the memory 701.

[0117] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the method for determining the downlink transmission beam in the embodiments of this application, and will not be repeated here.

[0118] To implement the above embodiments, this application also proposes a computer-readable storage medium.

[0119] When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the downlink transmission beam determination method as described above. Optionally, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0120] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0121] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining a downlink transmission beam, characterized in that, The method includes: For each of one or more user equipments, obtain the channel state information transmitted by the user equipment, and determine the type of the user equipment based on the channel state information; Based on the type of user equipment, a non-convex penalty term is added to the first optimization objective function of beamforming to obtain the second optimization objective function. The second optimization objective function is decomposed to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term; The first and second solving tasks are iteratively solved to obtain the first beamforming vector; Based on the first beamforming vector, a downlink transmission beam pointing to the user equipment is determined; The step of iteratively solving the first and second solving tasks to obtain the first beamforming vector includes: iteratively solving the first and second solving tasks based on the Alternating Direction Multiplier Method (ADMM) algorithm, stopping the iteration to obtain the first beamforming vector in response to the beamforming vector of the current iteration satisfying the convergence condition. In the iterative solution process of the first and second solution tasks, the method further includes: determining the rate of change of the channel state information and the service priority of the user equipment; and adaptively iterating the parameters in the ADMM algorithm according to the rate of change and the service priority, wherein the parameters in the ADMM algorithm include a step size factor ρ and a tradeoff parameter λ. The step of adaptively iterating the parameters in the ADMM algorithm based on the rate of change and the service priority includes: increasing the parameters in the ADMM algorithm in response to the rate of change being greater than a set speed threshold and / or the service priority being higher than a set priority; and maintaining the parameters in the ADMM algorithm in response to the rate of change being less than or equal to the set speed threshold and / or the service priority not being higher than the set priority. The step of determining the type of user equipment based on the channel state information includes: preprocessing the channel state information; performing signal and interference analysis on the preprocessed channel state information to obtain the signal-to-noise ratio (SNR) of the user equipment; and determining the type of user equipment based on the SNR and a preset SNR threshold. Wherein, the non-convex penalty term is Σ (|x|), where x represents the beamforming vector. ( ) represents a non-convex penalty function, using the L1 / 2 norm, where the L1 / 2 norm is... It is a sparsity penalty term that lies between the L1 norm and the L2 norm; The second optimization objective function is: Here, `maximize()` is a function that declares the optimization objective and is used to find the variable values ​​that maximize the objective function. This represents the beamforming vector of the k-th user equipment. Let be the signal-to-noise ratio of the k-th user equipment, and λ be a trade-off parameter used to control the degree of influence of the non-convex penalty term on the optimization objective.

2. The method according to claim 1, characterized in that, Determining the downlink transmission beam pointing to the user equipment based on the first beamforming vector includes: Based on the first beamforming vector, determine the control parameters corresponding to each antenna in the network device; Based on the control parameters corresponding to the antenna, the transmitting end of the network device is controlled to generate a downlink transmission beam pointing to the user equipment.

3. The method according to claim 2, characterized in that, The step of determining the control parameters corresponding to each antenna in the network device based on the first beamforming vector includes: Determine power constraints and / or quality of service constraints; Based on the power constraints and / or quality of service constraints, the first beamforming vector is optimized to obtain the second beamforming vector; Based on the second beamforming vector, the control parameters corresponding to each antenna in the network device are determined.

4. The method according to claim 3, characterized in that, The step of determining the control parameters corresponding to each antenna in the network device based on the second beamforming vector includes: Determine the total transmit power of the network equipment; Based on the second beamforming vector, determine the beamforming vector components corresponding to each antenna in the network device; Based on the total transmit power and the beamforming vector components corresponding to each antenna, the control parameters corresponding to each antenna in the network device are determined.

5. The method according to claim 3, characterized in that, The step of optimizing the first beamforming vector based on the power constraint and / or quality of service constraint to obtain the second beamforming vector includes: Based on the first beamforming vector, determine the amplitude and phase information of the beam corresponding to the user equipment; Based on the constraints, the amplitude and phase information of the beam corresponding to the user equipment are optimized to obtain the second beamforming vector.

6. The method according to claim 1, characterized in that, After determining the downlink transmission beam pointing to the user equipment based on the first beamforming vector, the method further includes: Collect performance feedback information from the user equipment; Based on the performance feedback information, the downlink transmission beam of the user equipment is quality assessed; and / or, Based on the performance feedback information, the downlink transmission beam of the user equipment is optimized.

7. A downlink transmission beam determination device, characterized in that, include: The information acquisition module is used to acquire channel state information transmitted by one or more user equipments for each of the user equipments, and determine the type of the user equipment based on the channel state information; The non-convex optimization processing module is used to add a non-convex penalty term to the first optimization objective function of beamforming according to the type of user equipment, so as to obtain the second optimization objective function; The decomposition module is used to decompose the second optimization objective function to obtain the first solution task corresponding to the convex function and the second solution task of the non-convex penalty term; An iterative solution module is used to iteratively solve the first solution task and the second solution task to obtain the first beamforming vector; A beamforming processing module is used to determine a downlink transmission beam pointing to the user equipment based on the first beamforming vector. The downlink transmission beam determination device is further configured to implement the method described in any one of claims 1-6.

8. An electronic device, characterized in that, include: processor; Memory for storing the executable instructions of the processor; The processor is configured to execute the instructions to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-6.

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