A beamforming precision control method

Abstract

The application relates to the technical field of radio frequency communication control, and discloses a beamforming precision control method, which comprises the following steps: acquiring service quality parameters of a user and frequency domain channel information; generating a user priority sequence; constructing a three-dimensional resource scheduling matrix; outputting a beamforming coefficient matrix and a power distribution result; and performing beam precision closed-loop regulation and control of a radio frequency array. Compared with the prior art, the method can combine dynamic changes of user QoS to perform real-time resource scheduling and fine beam regulation, and can solve the technical problem that it is difficult to realize stable beamforming control, especially in the scene that a chip supporting large-scale user concurrency needs to dynamically adjust the beam according to the QoS requirement. According to the application, a dynamic resource scheduling matrix is constructed by combining power perception, and a multi-dimensional feedback calibration mechanism based on amplitude and phase perturbation is combined, so that high-precision beam control is realized, and the communication stability and regulation flexibility are improved.

Description

Technical Field

[0001] This invention relates to the field of radio frequency communication control technology, and in particular to a method for precise beamforming control. Background Technology

[0002] Currently, in 5G and next-generation 6G communication systems, massive MIMO antenna arrays have become a key technology for improving spectrum utilization and communication capacity. To enhance signal quality and anti-interference capabilities in multi-user scenarios, beamforming control technology based on user channel state information (CSI) is widely used at the base station level. Beamforming adjusts the amplitude and phase of multiple antenna signals in the spatial dimension to focus signal energy towards the target user, thereby increasing effective radiated power and suppressing interference signals from non-target directions. However, most existing solutions suffer from the following shortcomings: decoupling of beam control and power control; most systems treat beam direction determination and transmit power scheduling as two independent modules, lacking a collaborative optimization mechanism for service QoS requirements, resulting in some users still not receiving stable service guarantees in low-power beams. QoS-insensitive scheduling models; current scheduling methods are mostly based on channel gain or static frequency reuse strategies, making it difficult to dynamically perceive user rate requirements, latency tolerance, and other service level indicators, especially in high-density access scenarios with heterogeneous QoS requirements, failing to provide differentiated resource allocation. The lack of real-time closed-loop feedback in the control process means that, in actual deployments, most beamforming coefficients are derived from preset templates built based on ideal channel models or coarse-grained feedback. This lack of real-time correction mechanisms for non-ideal factors such as amplitude errors, phase mismatches, and electromagnetic disturbances leads to decreased beamforming accuracy and increased bit error rate. For example, in multi-user communication scenarios such as industrial IoT, vehicle-to-everything (V2X) communication, and dense urban areas, frequently changing link conditions and high-concurrency scheduling requests pose significant challenges to traditional static beam allocation mechanisms. In such scenarios, it is necessary to dynamically generate beamforming coefficients within millisecond-level scheduling cycles, accurately adapt to the QoS priorities of various services, and adjust the radiation direction and energy distribution of the radio frequency array in real time.

[0003] Therefore, there is an urgent need for a QoS-aware beamforming precision control method that can integrate service requirements and channel status to achieve joint optimization of beam direction and power, so as to effectively improve communication stability, service elasticity and dynamic scheduling capabilities. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, the present invention aims to propose a precise beamforming control method, which seeks to solve the technical problem in existing technologies where it is difficult to combine dynamic changes in user QoS for real-time resource scheduling and fine beam control, especially in scenarios where chips supporting large-scale user concurrency need to dynamically adjust the beam according to QoS requirements, making it difficult to achieve stable beamforming control.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for precise beamforming control.

[0006] The beamforming precision control method includes:

[0007] Step S10: Obtain the service quality set Q and channel state information set H for the current user i; the service quality set Q includes rate demand parameters. and tolerance delay parameter The channel state information set H includes subcarrier frequency channel vectors. ;

[0008] Step S20: Based on the demand parameter vector, tolerance delay parameter vector, and subcarrier frequency channel vector, the instantaneous achievable rate is calculated using an adaptive QoS dynamic weight control mechanism. and priority scoring And score the priority. Arrange them in descending order to obtain the priority sequence S;

[0009] Step S30: Based on the priority sequence S, a three-dimensional resource scheduling matrix M(t,f,m) is established using a dynamic scheduling mechanism based on three-dimensional weighted mapping, where t is the scheduling time slot index, f is the frequency resource block number, and m is the spatial beam number; and under the preset beam parallel constraint, a decision-making process to maximize the target resources is executed using a combination of local greedy and global correction mechanisms, and finally, a user resource allocation table is output.

[0010] Step S40: For the user resource allocation table, a collaborative mechanism of adaptive beamforming and dynamic power feedback is adopted to perform real-time spatial beamforming and power allocation processing, and output the beamforming coefficient matrix W and the power allocation result P;

[0011] Step S50: Based on the output beamforming coefficient matrix W and power allocation result P, a multi-dimensional feedback-driven beam amplitude and phase perturbation correction mechanism is used to perform precise closed-loop beam control operation for the radio frequency array.

[0012] Preferably, in step S20, the instantaneous achievable rate is calculated using an adaptive QoS dynamic weight control mechanism based on the demand parameter vector, the tolerance delay parameter vector, and the subcarrier frequency channel vector. and priority scoring And score the priority. The steps for obtaining the priority sequence by arranging in descending order specifically include:

[0013] Step S201: For the subcarrier frequency channel vector of the current user i Perform amplitude square summation to obtain the subcarrier channel gain vector. Obtain the transmit power vector P and noise power vector N, and then use the transmit power vector P, noise power vector N, and subcarrier channel gain vector as the basis for the acquisition. The signal-to-noise ratio vector is calculated. Finally, the signal-to-noise ratio vector Perform logarithmic domain geometric averaging to obtain the equivalent average signal-to-noise ratio (SNR), and use the equivalent average SNR as the instantaneously achievable rate. ;

[0014] Step S202: Based on the instantaneously achievable rate The historical average rate parameter of the current user i is obtained using the sliding window statistical method. Based on historical average rate parameters Rate requirement parameters and tolerance delay parameter Constructing Adaptive QoS Dynamic Weights ;

[0015] Step S203: Adjust the adaptive QoS dynamic weights With instantaneous achievable rate Perform element-by-element fusion processing to generate priority scores. Finally, priority scores were assigned. Perform a descending sort to generate a priority sequence S.

[0016] Preferably, in step S20, ;in, This is a rate deviation weighting factor used to reflect the degree to which user rates meet the standards; This is a delay deviation weighting factor used to reflect the urgency of real-time services.

[0017] Preferably, in step S30, the step of establishing a three-dimensional resource scheduling matrix M(t,f,m) based on a three-dimensional weighted mapping dynamic scheduling mechanism according to the priority sequence S, where t is the scheduling slot index, f is the frequency resource block number, and m is the spatial beam number; and under the preset beam parallel constraint, the step of executing the decision-making process to maximize the target resources using a combination of local greedy and global correction mechanisms, and finally outputting the user resource allocation table, specifically includes:

[0018] Step S301: Set the scheduling slot index t according to the priority sequence S, and set the scheduling slot index t according to the subcarrier frequency channel vector. Set the space beam number based on the instantaneous achievable rate. Set the frequency resource block number f; establish a three-dimensional resource scheduling matrix M(t,f,m);

[0019] Step S302: Select the first M users from the priority sequence S according to the order of the scheduling slot index t, and execute the first mapping operation, the second mapping operation, and the third mapping operation in sequence; output the temporary resource mapping matrix. ;in:

[0020] First mapping operation: For the first M users in the frequency domain, select the largest frequency resource block number from all preset available frequency blocks as the optimal allocation result;

[0021] The second mapping operation: In the spatial domain, for the first M users, based on the subcarrier frequency channel vector... The normalized cross-correlation method is used to calculate the beam mutual interference cost matrix C between any two users; the criterion of minimizing the beam mutual interference cost matrix C is used to reassign spatial beam numbers to each user based on a local greedy algorithm;

[0022] The third mapping operation: In the time domain, record the historical scheduling count of the first M users for their reallocation of spatial beam numbers. When the historical scheduling count is greater than the preset scheduling count threshold, the rescheduling is postponed to the next scheduling time slot index t+1.

[0023] Step S303: Finally, a global correction mechanism is used to modify the temporary resource mapping matrix. The process involves making decisions to maximize resource utilization and outputting a user resource allocation table.

[0024] Preferably, step S40, which involves performing real-time spatial beamforming and power allocation processing using an adaptive beamforming and dynamic power feedback collaborative mechanism for the user resource allocation table, and outputting the beamforming coefficient matrix W and the power allocation result P, specifically includes:

[0025] Step S401: Obtain the set of users in the current time slot from the user resource allocation table. Construct the corresponding channel matrix It employs a two-stage beamforming mechanism to calculate the user set within the current time slot. Initial beamforming coefficient matrix ;

[0026] Step S402: For the channel matrix Channel feedback information is continuously collected, including frequency domain channel gain, current bit error rate, actual received signal-to-noise ratio, and the deviation between actual and expected rates. The power allocation weight for user i is determined based on the deviation between the actual and expected rates. ;

[0027] Step S403: Assign weights according to power distribution And the total transmit power available in the preset current scheduling time slot Perform power normalization allocation calculation to obtain the power allocation quota for user i. It outputs the power allocation result P.

[0028] Preferably, in step S401, a two-stage beamforming mechanism is used to calculate the initial beamforming coefficient matrix. The steps specifically include: targeting the channel matrix Continuously detect inter-channel interference metrics; when the channel matrix is ​​detected... If the inter-channel interference metric exceeds a preset interference metric threshold, the Zero-Force Interference Cancellation (ZF) method is used to construct the beam matrix vector; otherwise, the Maximum Ratio Transmission (MRT) method is used to construct the beam matrix vector. After normalizing the beam matrix vectors of all users, the initial beamforming coefficient matrix is ​​output. .

[0029] Preferably, in step S402, the power allocation quota for user i in each scheduling time slot is determined. The following normalized weighted model is used for calculation: Where L is the corresponding channel matrix. The size corresponds to the channel matrix. The number of columns; This is the deviation adjustment coefficient, used to control the amplification weight of the deviation on the power allocation ratio, with a range of k∈[0.1,2.0]. This represents the deviation between the actual rate and the expected rate. This is the normalized denominator term, used to represent the total power quota requirement of all users.

[0030] Preferably, in step S50, the step of performing precise closed-loop beam control operation for the RF array using a multi-dimensional feedback-driven beam amplitude and phase perturbation correction mechanism based on the output beamforming coefficient matrix W and power allocation result P specifically includes:

[0031] Step S501: Construct an initial control matrix based on the output beamforming coefficient matrix W, scale the power allocation result P according to the square root of the power and embed it into the initial control matrix, and fuse them to form a fused control matrix. ;

[0032] Step S502: Introduce amplitude error factor and phase correction factor, and integrate the amplitude error factor and phase correction factor with the fusion control matrix using complex domain coherent superposition modulation. Perform joint complex representation to generate the final correction matrix. ;

[0033] Step S503: Construct the channel register control table of the RF array, and use a real-to-imaginary component mapping mechanism to modify the final correction matrix. Write to the RF array channel register control table, output register control array , ,in, Number the m-th antenna; For the final correction matrix The real part in the first The amplitude component of the line; For the final correction matrix The imaginary part in the th The amplitude component of the line; This represents the total number of radio frequency channels;

[0034] Step S504: Transfer the register control array Write the DAC modulation output table for the RF array, apply an amplitude-phase combination drive signal to each sub-channel, and perform performance evaluation within a preset feedback period. If the performance residual index ΔR is detected to be greater than the preset performance residual index threshold within two consecutive periods, the register freeze control is automatically triggered to maintain the state of the channel register control table to avoid oscillation divergence.

[0035] The beneficial effects of this invention are as follows: This invention proposes a three-dimensional resource scheduling mechanism that integrates user QoS weight and channel state, which can dynamically output personalized beamforming coefficients and power allocation results in a multi-user concurrent communication environment, realize cross-level service quality-driven fine-tuning of spatial resources, and significantly improve spectrum utilization and control flexibility.

[0036] This invention constructs a closed-loop beam amplitude and phase control process for radio frequency arrays, and introduces a fusion control matrix and amplitude and phase disturbance feedback mechanism, which can correct beam pattern deviation and amplitude mismatch in real time at the hardware scale, thereby improving beamforming accuracy and communication stability in high dynamic and multipath interference environments. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the first embodiment of a beamforming precision control method according to the present invention.

[0039] Figure 2 This is a schematic diagram comparing the spatial gain directivity of different beam control schemes in the first embodiment of the beamforming precision control method of the present invention.

[0040] Figure 3 This is a schematic diagram of the equipment for a beamforming precision control method according to the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the beamforming precision control method of the present invention, which presents the first embodiment of the beamforming precision control method of the present invention.

[0043] In the first embodiment, the beamforming precision control method includes:

[0044] Step S10: Obtain the service quality set Q and channel state information set H for the current user i; the service quality set Q includes rate demand parameters. and tolerance delay parameter The channel state information set H includes subcarrier frequency channel vectors. ;

[0045] It should be noted that in this step, the "service quality set Q" refers to the dynamic QoS parameter set maintained by the network side for each user, which mainly reflects the performance requirements corresponding to the user's current service type (such as high bandwidth eMBB or low latency URLLC). Among them, the rate requirement parameter is used to characterize the minimum transmission rate required by the user, and the tolerable latency parameter is used to reflect the user's service sensitivity to latency. The "channel state information set H" refers to the channel quality characteristics on each frequency resource block obtained by the base station or core network based on uplink feedback or downlink estimation, which usually includes the frequency domain channel gain or CSI information corresponding to each subcarrier.

[0046] It should be understood that most existing scheduling schemes only focus on one of the channel state or service requirements, making it difficult to achieve fine-grained service differentiation in dynamic environments. For example, simply matching resources based on channel quality may result in low-latency services being frequently delayed in weak channels; while allocating resources solely based on service type scoring may lead to spectrum waste in extremely poor channel conditions. This step, by simultaneously introducing both Q and H information, provides a "dual-indicator-driven" basis for subsequent scheduling optimization.

[0047] Step S20: Based on the demand parameter vector, tolerance delay parameter vector, and subcarrier frequency channel vector, the instantaneous achievable rate is calculated using an adaptive QoS dynamic weight control mechanism. and priority scoring And score the priority. Arrange them in descending order to obtain the priority sequence S;

[0048] It should be noted that the "adaptive QoS dynamic weight control mechanism" described in this step is a scoring strategy that dynamically adjusts weight factors based on user service quality requirements and channel conditions. Its core lies in determining the scheduling priority of each user in real time according to their current service type and transmission environment. This mechanism integrates rate requirement parameters, tolerable delay parameters, and current frequency domain channel conditions with adjustable weights for evaluation, thereby achieving more differentiated and flexible scheduling decisions in multi-user resource contention scenarios.

[0049] Understandably, by introducing "instantaneous reachability" as a service availability indicator and incorporating it along with "service urgency" into the scoring process, this step establishes a unified and quantifiable priority scoring system. This system reflects both physical layer reachability and service quality constraints at the service layer, enabling rapid assessment of each user's "scheduling urgency" under dynamic channel conditions. This avoids delays or misjudgments of high-value services, improving scheduling efficiency and service fairness.

[0050] It should be understood that existing scheduling schemes often employ static classification (such as GBR / Non-GBR) when handling QoS differences, lacking the ability to adjust weights in real time for individual services. This leads to scheduling delays and resource waste in large-scale concurrent scenarios. This invention significantly enhances the fine-grained scheduling capability through a "dynamic weight control mechanism," making it particularly suitable for high-concurrency wireless communication systems with complex service types, such as IoT, vehicular networks, or 5G URLLC scenarios.

[0051] For example, in a typical 5G base station scenario, user A is a high-definition video user, and user B is a vehicle-mounted low-latency terminal. If A's channel quality is better than B's, traditional scheduling methods might prioritize allocating resources to A, resulting in B's low-latency requirements not being met. However, in this step, the weight of B's ​​"tolerable latency parameter" is dynamically increased. Even if its channel is slightly worse, it can still obtain a priority score advantage, thus prioritizing B's scheduling and ensuring timely transmission of its low-latency services, thereby improving overall QoS satisfaction.

[0052] Step S30: Based on the priority sequence S, a three-dimensional resource scheduling matrix M(t,f,m) is established using a dynamic scheduling mechanism based on three-dimensional weighted mapping, where t is the scheduling time slot index, f is the frequency resource block number, and m is the spatial beam number; and under the preset beam parallel constraint, a decision-making process to maximize the target resources is executed using a combination of local greedy and global correction mechanisms, and finally, a user resource allocation table is output.

[0053] It should be noted that the "three-dimensional weighted mapping dynamic scheduling mechanism" described in this step is a scheduling scheme that simultaneously considers the time, frequency, and spatial domains. In the scheduling matrix, each matrix unit represents a set of adjustable resource units, and their allocation is processed by weighted mapping based on user priority scores, historical scheduling data, current resource occupancy status, and beam distribution characteristics. This mechanism achieves refined resource scheduling and dynamic fairness control among users by dynamically adjusting the scheduling weights of each dimension.

[0054] Understandably, the introduction of a "combination of local greedy and global correction mechanism" aims to balance computational efficiency and resource allocation quality in a highly complex scheduling space. The local greedy mechanism can quickly select the current optimal resource unit to meet the needs of high-priority users, while the global correction mechanism evaluates the overall load balancing and resource utilization efficiency based on local decisions and performs rollback corrections for potential conflicts or duplicate allocations, thereby avoiding the global performance degradation caused by local optima.

[0055] It should be understood that traditional scheduling mechanisms mostly operate within a two-dimensional resource space. Considering only the time-frequency domain, they lack the ability to finely control beam space resources. Especially in large-scale multi-user beamforming communication systems, beam spacing, cross-interference, and parallelism limitations place higher demands on scheduling schemes. This invention extends to a three-dimensional scheduling space and constructs a dynamic mapping and correction process, effectively mitigating spatial resource allocation conflicts.

[0056] Step S40: For the user resource allocation table, a collaborative mechanism of adaptive beamforming and dynamic power feedback is adopted to perform real-time spatial beamforming and power allocation processing, and output the beamforming coefficient matrix W and the power allocation result P;

[0057] It should be noted that the "adaptive beamforming" in this step refers to dynamically generating beam control parameters based on the users and corresponding resource units determined in the resource allocation table, under different time slots and frequency bands, and considering the spatial channel characteristics of each user, thereby forming a beam pointing structure with directional enhancement effects. Simultaneously, the "dynamic power feedback coordination mechanism" adjusts the transmit power of each beam by real-time acquisition of link quality and signal-to-noise ratio feedback information from the previous cycle, improving the signal coverage quality for high-priority users or users in weak channels.

[0058] Understandably, the beamforming coefficient matrix W and power allocation result P in this step are the core input parameters for subsequent hardware array control. Matrix W determines the directionality and relative phase of the transmitted signal of each array element, ensuring that the signal accurately covers the target user area in space; matrix P controls the output power of each beam channel, ensuring that the total power does not exceed the limit while achieving adaptation and optimization for different user link conditions.

[0059] It should be understood that existing solutions often employ fixed beam patterns or predefined power strategies, which are insufficient to handle real-world communication scenarios such as dynamic user access, channel fluctuations, or interference changes. This invention, by introducing a "real-time adaptive and closed-loop feedback" mechanism, can regenerate beamforming parameters and power allocation strategies in each scheduling cycle based on the actual communication status of each user, significantly improving the flexibility of beam control and link stability.

[0060] For example, if user E is located at a remote location and is subject to multipath interference in a certain time slot, and the signal-to-noise ratio (SNR) of its previous feedback cycle is below the threshold, the beam directivity of user E is adjusted according to its resource allocation table to further point the main lobe towards user E. At the same time, the transmit power of the corresponding beam of user E is increased through the feedback power mechanism, while reducing the power ratio of low-priority users, thereby ensuring the communication quality of user E and avoiding dropped calls or insufficient data rates.

[0061] Step S50: Based on the output beamforming coefficient matrix W and power allocation result P, a multi-dimensional feedback-driven beam amplitude and phase perturbation correction mechanism is used to perform precise closed-loop beam control operation for the radio frequency array.

[0062] It should be noted that the "multi-dimensional feedback" in this step refers to a set of feedback information constructed through multiple dimensions (such as user received power feedback, bit error rate feedback, phase drift estimation feedback, etc.), which is used to drive the correction and optimization of the current beamforming effect. Among them, the beam amplitude and phase perturbation correction mechanism performs fine-grained adjustment of the amplitude and phase of the array transmitted signal.

[0063] Understandably, current mainstream beam control methods typically lack online fine-tuning mechanisms to address hardware errors and real-time feedback. This leads to issues such as beam direction shift and increased sidelobe leakage under long-term operation or dynamic channel conditions, impacting performance. This invention introduces multiple correction dimensions, such as amplitude factor and phase offset, and integrates them into the update process of the fused control matrix. This achieves continuous, low-disturbance beamforming accuracy compensation, effectively improving the stability and consistency of array pointing control.

[0064] For example, in a densely populated area, suppose a user B experiences a deterioration in channel quality and a continuously increasing bit error rate (BER). After identifying this feedback, the beamforming parameters of the corresponding beam are dynamically analyzed, revealing a shift in the array's main lobe. Power imbalance between array channels is immediately corrected using amplitude perturbation, and phase errors between arrays are corrected using phase perturbation. The correction matrix is ​​then regenerated and sent to the RF array. After correction, user B's signal-to-noise ratio (SNR) significantly improves, and the link BER decreases, verifying the stable control effect of this closed-loop mechanism in dynamic environments.

[0065] For example, such as Figure 2 As shown in the figure, the proposed solution (closed-loop perturbation correction) exhibits significantly higher gain in the main lobe direction than both the comparative solution A (static beamforming) and the comparative solution B (no closed-loop feedback). It also demonstrates higher main lobe sharpness and stronger sidelobe suppression, indicating superior spatial directivity control. Particularly near the target angle, the proposed solution exhibits lower beam offset and stronger interference suppression, effectively improving the signal-to-noise ratio at the receiver.

[0066] Further analysis reveals that while comparative scheme A in the figure possesses some spatial beamforming capability, it lacks a real-time feedback mechanism and cannot adapt to dynamic channel changes, leading to a tendency for the main lobe position to shift. Comparative scheme B, on the other hand, completely lacks feedback adjustment functionality, exhibiting problems such as main lobe broadening and severe side lobe leakage, easily causing multi-user interference. In contrast, the present invention integrates a multi-dimensional feedback driving mechanism to perform real-time perturbation correction on amplitude and phase, maintaining high stability and directivity accuracy while ensuring beam convergence speed. This makes it suitable for beamforming and control requirements in densely populated, large-scale dynamic communication environments.

[0067] Example 2: Furthermore, the beamforming precision control system provided by this invention employs a beamforming precision control method from the above embodiments, which can solve a technical problem related to beamforming precision control. Compared with the prior art, the beneficial effects of the beamforming precision control system provided by this invention are the same as those of the beamforming precision control method provided in the above embodiments, and other technical features in the beamforming precision control system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0068] Example 3: This invention provides a beamforming precision control device, please refer to... Figure 3 A beamforming precision control device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform a beamforming precision control method as described in Embodiment 1 above. The beamforming precision control device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This beamforming precision control device is merely an example and should not limit the functionality or scope of the embodiments of this invention. A beamforming precision control device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of a beamforming precision control device. Processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via bus 1005. I / O interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows a beamforming precision control device to communicate wirelessly or wiredly with other devices to exchange data. Although a beamforming precision control device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented or possessed alternatively.

[0069] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the beamforming precision control method described above. The computer program product provided by this invention can solve a technical problem related to beamforming precision control. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the beamforming precision control method provided in the above embodiments, and will not be repeated here.

[0070] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0071] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0072] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for precise beamforming control, characterized in that, The methods include: Step S10: Obtain the service quality set Q and channel state information set H for the current user i; the service quality set Q includes rate demand parameters. and tolerance delay parameter The channel state information set H includes subcarrier frequency channel vectors. ; Step S20: Based on the demand parameter vector, tolerance delay parameter vector, and subcarrier frequency channel vector, the instantaneous achievable rate is calculated using an adaptive QoS dynamic weight control mechanism. and priority scoring And score the priority. Arrange them in descending order to obtain the priority sequence S; Step S30: Based on the priority sequence S, a three-dimensional resource scheduling matrix M(t,f,m) is established using a dynamic scheduling mechanism based on three-dimensional weighted mapping, where t is the scheduling time slot index, f is the frequency resource block number, and m is the spatial beam number; and under the preset beam parallel constraint, a decision-making process to maximize the target resources is executed using a combination of local greedy and global correction mechanisms, and finally, a user resource allocation table is output. Step S40: For the user resource allocation table, a collaborative mechanism of adaptive beamforming and dynamic power feedback is adopted to perform real-time spatial beamforming and power allocation processing, and output the beamforming coefficient matrix W and the power allocation result P; Step S50: Based on the output beamforming coefficient matrix W and power allocation result P, a multi-dimensional feedback-driven beam amplitude and phase perturbation correction mechanism is used to perform precise closed-loop beam control operation for the radio frequency array.

2. The beamforming precision control method as described in claim 1, characterized in that, In step S20, the instantaneous achievable rate is calculated using an adaptive QoS dynamic weight control mechanism based on the demand parameter vector, the tolerance delay parameter vector, and the subcarrier frequency channel vector. and priority scoring And score the priority. The steps for obtaining the priority sequence by arranging in descending order specifically include: Step S201: For the subcarrier frequency channel vector of the current user i Perform amplitude square summation to obtain the subcarrier channel gain vector. Obtain the transmit power vector P and noise power vector N, and then use the transmit power vector P, noise power vector N, and subcarrier channel gain vector as the basis for the acquisition. The signal-to-noise ratio vector is calculated. Finally, the signal-to-noise ratio vector Perform logarithmic domain geometric averaging to obtain the equivalent average signal-to-noise ratio (SNR), and use the equivalent average SNR as the instantaneously achievable rate. ; Step S202: Based on the instantaneously achievable rate The historical average rate parameter of the current user i is obtained using the sliding window statistical method. Based on historical average rate parameters Rate requirement parameters and tolerance delay parameter Constructing Adaptive QoS Dynamic Weights ; Step S203: Adjust the adaptive QoS dynamic weights With instantaneous achievable rate Perform element-by-element fusion processing to generate priority scores. Finally, priority scores were assigned. Perform a descending sort to generate a priority sequence S.

3. The beamforming precision control method as described in claim 2, characterized in that, In step S20, ;in, This is a rate deviation weighting factor used to reflect the degree to which user rates meet the standards; This is a delay deviation weighting factor used to reflect the urgency of real-time services.

4. The beamforming precision control method as described in claim 1, characterized in that, In step S30, a three-dimensional resource scheduling matrix M(t,f,m) is established based on a dynamic scheduling mechanism using a three-dimensional weighted mapping according to the priority sequence S, where t is the scheduling slot index, f is the frequency resource block number, and m is the spatial beam number; and under the preset beam parallel constraint, a decision-making process to maximize the target resources is executed using a combination of local greedy and global correction mechanisms, ultimately outputting the user resource allocation table. Specifically, this includes: Step S301: Set the scheduling slot index t according to the priority sequence S, and set the scheduling slot index t according to the subcarrier frequency channel vector. Set the space beam number based on the instantaneous achievable rate. Set the frequency resource block number f; establish a three-dimensional resource scheduling matrix M(t,f,m); Step S302: Select the first M users from the priority sequence S according to the order of the scheduling slot index t, and execute the first mapping operation, the second mapping operation, and the third mapping operation in sequence; output the temporary resource mapping matrix. ;in: First mapping operation: For the first M users in the frequency domain, select the largest frequency resource block number from all preset available frequency blocks as the optimal allocation result; The second mapping operation: In the spatial domain, for the first M users, based on the subcarrier frequency channel vector... The normalized cross-correlation method is used to calculate the beam mutual interference cost matrix C between any two users; the criterion of minimizing the beam mutual interference cost matrix C is used to reassign spatial beam numbers to each user based on a local greedy algorithm; The third mapping operation: In the time domain, record the historical scheduling count of the first M users for their reallocation of spatial beam numbers. When the historical scheduling count is greater than the preset scheduling count threshold, the rescheduling is postponed to the next scheduling time slot index t+1. Step S303: Finally, a global correction mechanism is used to modify the temporary resource mapping matrix. The process involves making decisions to maximize resource utilization and outputting a user resource allocation table.

5. The beamforming precision control method as described in claim 1, characterized in that, Step S40, which involves performing real-time spatial beamforming and power allocation processing using an adaptive beamforming and dynamic power feedback collaborative mechanism for the user resource allocation table, and outputting the beamforming coefficient matrix W and the power allocation result P, specifically includes: Step S401: Obtain the set of users in the current time slot from the user resource allocation table. Construct the corresponding channel matrix It employs a two-stage beamforming mechanism to calculate the user set within the current time slot. Initial beamforming coefficient matrix ; Step S402: For the channel matrix Channel feedback information is continuously collected, including frequency domain channel gain, current bit error rate, actual received signal-to-noise ratio, and the deviation between actual and expected rates. The power allocation weight for user i is determined based on the deviation between the actual and expected rates. ; Step S403: Assign weights according to power distribution And the total transmit power available in the preset current scheduling time slot Perform power normalization allocation calculation to obtain the power allocation quota for user i. It outputs the power allocation result P.

6. The beamforming precision control method as described in claim 5, characterized in that, In step S401, the initial beamforming coefficient matrix is ​​calculated using a two-stage beamforming mechanism. The steps specifically include: targeting the channel matrix Continuously detect inter-channel interference metrics; when the channel matrix is ​​detected... If the inter-channel interference metric exceeds a preset interference metric threshold, the Zero-Force Interference Cancellation (ZF) method is used to construct the beam matrix vector; otherwise, the Maximum Ratio Transmission (MRT) method is used. After normalizing the beam matrix vectors of all users, the initial beamforming coefficient matrix is ​​output. .

7. The beamforming precision control method as described in claim 5, characterized in that, In step S402, the power allocation quota for user i in each scheduling time slot is determined. The following normalized weighted model is used for calculation: Where L is the corresponding channel matrix. The size corresponds to the channel matrix. The number of columns; This is the deviation adjustment coefficient, used to control the amplification weight of the deviation on the power allocation ratio, with a range of k∈[0.1,2.0]. This represents the deviation between the actual rate and the expected rate. This is the normalized denominator term, used to represent the total power quota requirement of all users.

8. The beamforming precision control method as described in claim 1, characterized in that, In step S50, based on the output beamforming coefficient matrix W and power allocation result P, a multi-dimensional feedback-driven beam amplitude and phase perturbation correction mechanism is used to perform precise closed-loop beam control operation for the RF array. This specifically includes: Step S501: Construct an initial control matrix based on the output beamforming coefficient matrix W, scale the power allocation result P according to the square root of the power and embed it into the initial control matrix, and fuse them to form a fused control matrix. ; Step S502: Introduce amplitude error factor and phase correction factor, and integrate the amplitude error factor and phase correction factor with the fusion control matrix using complex domain coherent superposition modulation. Perform joint complex representation to generate the final correction matrix. ; Step S503: Construct the channel register control table of the RF array, and use a real-to-imaginary component mapping mechanism to modify the final correction matrix. Write to the RF array channel register control table, output register control array , ,in, Number the m-th antenna; For the final correction matrix The real part in the first The amplitude component of the line; For the final correction matrix The imaginary part in the th The amplitude component of the line; This represents the total number of radio frequency channels; Step S504: Transfer the register control array Write the DAC modulation output table for the RF array, apply an amplitude-phase combination drive signal to each sub-channel, and perform performance evaluation within a preset feedback period. If the performance residual index ΔR is detected to be greater than the preset performance residual index threshold within two consecutive periods, the register freeze control is automatically triggered to maintain the state of the channel register control table to avoid oscillation divergence.

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