A joint beamforming design method in a drone integrated network

Abstract

This invention relates to the field of wireless communication transmission technology, specifically to a joint beamforming design method in an integrated UAV network. The method includes the following steps: acquiring spatial coordinates, time deviation, and channel response values, calculating error factors, and superimposing them to construct a composite channel state matrix; constructing a discrete phase value set based on the number of low-precision quantization bits and selecting initial transmit waveform parameters; calculating energy based on the composite channel state matrix and inputting it into an algorithm to calculate the receive filter weights; calculating the quantized signal-to-interference-plus-noise ratio (SINR) and Cramer-Rao bound and updating the transmit waveform parameters; and generating a locally optimal low-precision quantized transmit waveform sequence and receive filter weight vector when a convergence threshold is met. In this invention, by constructing a composite channel state matrix to compensate for channel estimation deviations, constructing a discrete phase value set based on hardware quantization accuracy, and jointly optimizing parameters using the quantized SINR and Cramer-Rao bound, quantization errors are overcome and interference nulls are achieved, improving beam pointing accuracy and link transmission robustness.

Description

Technical Field

[0001] This invention relates to the field of wireless communication transmission technology, and in particular to a joint beamforming design method in an integrated UAV network. Background Technology

[0002] The field of wireless communication transmission technology specifically involves wireless communication transmission technology, which refers to a communication method that uses electromagnetic waves to propagate signals in free space to achieve information exchange. It encompasses the complete physical link process from signal modulation and transmission, spatial channel propagation to reception and demodulation. Among these methods, the joint beamforming design method in traditional UAV integrated networks refers to the process in a phased array antenna system onboard the UAV. First, channel state information is obtained through pilot signals fed back from the receiver. The baseband processor calculates the weighting coefficients of each antenna element based on these physical channel parameters and a preset fixed codebook or zero-forcing criterion. Then, it drives the electronic phase shifter and power amplifier in the RF link to independently adjust the phase and amplitude of each transmitted signal, thereby superimposing them in physical space to form a directional electromagnetic wave beam pointing towards a specific user terminal.

[0003] Traditional UAV beamforming relies on pilot feedback from the receiver to obtain channel status. In the high-dynamic flight of UAVs, due to rapid changes in physical spatial position and signal transmission delays, there is a spatiotemporal lag between the obtained channel parameters and the actual physical channel. Furthermore, baseband processing calculates weighting coefficients based only on idealized physical channel parameters and a preset fixed codebook or zero-forcing criterion, ignoring the limitations of phase quantization accuracy of electronic phase shifters in the actual hardware link and the energy distribution of multipath interference noise in complex electromagnetic environments. This makes it difficult for the beam pointing weight calculation results to accurately adapt to the real spatial environment at the current moment. As a result, the main lobe of the beam energy cannot be accurately focused on the target terminal, and the sidelobe interference signal cannot be effectively suppressed. This seriously restricts the actual signal-to-noise ratio gain and information transmission bit error rate performance of mobile communication links in dynamic scenarios. At the same time, traditional UAVs use high-precision transmission waveforms, but with the surge in the number of UAVs and the increase in data volume, high-precision transmission waveforms will result in higher power consumption and longer data processing time for UAVs. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a joint beamforming design method for an integrated UAV network.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a joint beamforming design method in an integrated UAV network, comprising the following steps:

[0006] S1: Obtain spatial coordinates, time deviation, pointing angle and channel response value; calculate position error, time error and amplitude factor based on the spatial coordinates, time deviation and pointing angle; superimpose the position error, time error and amplitude factor onto the channel response value to construct a composite channel state matrix.

[0007] S2: Divide the phase points at equal intervals on a circle with a radius equal to the modulus of the transmitted signal element according to the number of quantization bits, construct a discrete phase value set, select the initial transmitted waveform parameters from the discrete phase value set, and set the convergence threshold value.

[0008] S3: Calculate the signal energy and interference noise energy based on the composite channel state matrix and the transmitted waveform parameters, and input the signal energy and the interference noise energy into the minimum variance distortionless response algorithm to calculate the receiving filter weight value;

[0009] S4: Based on the received filter weight values, search within the set of discrete phase values, calculate the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, update the transmitted waveform parameters according to the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, encode and transform the transmitted waveform parameters when the convergence threshold value is met, and generate a locally optimal low-precision quantized transmitted waveform sequence and a received filter weight vector.

[0010] As a further aspect of the present invention, step S1 specifically comprises:

[0011] S11: Obtain the spatial coordinates of the UAV and the ground user, as well as the time deviation of the system synchronization clock. Calculate the relative distance vector between the UAV and the user using the Euclidean distance algorithm. Combine the pointing angle to calculate the geometric projection component of the beam on the propagation path. Then, derive the position error and time error based on the preset error distribution model.

[0012] S12: Based on the free space path loss model and the relative distance vector, calculate the signal attenuation coefficient to generate an amplitude factor, convert the position error and time error into corresponding phase rotation terms, use the amplitude factor to perform weighted correction on the channel response value, and superimpose the phase rotation terms into the corrected channel response value to construct the composite channel state matrix.

[0013] As a further aspect of the present invention, the derivation process of the position error in S11 includes:

[0014] A three-dimensional spherical coordinate system is established with the center of the UAV antenna array as the origin. The spatial coordinates are converted into azimuth and pitch angle values. The angular deviation between the antenna array normal direction and the line-of-sight path is calculated based on the UAV's flight attitude data. The influence of atmospheric turbulence on beam pointing is simulated using a Gaussian random process. The angular deviation and random disturbance components are linearly superimposed to calculate the angular offset in the horizontal and vertical dimensions. The angular offset is then mapped to an equivalent path difference value to generate the position error.

[0015] As a further aspect of the present invention, step S2 specifically comprises:

[0016] S21: Obtain the preset number of quantization bits, divide the circle on the complex plane with a radius equal to the modulus of the transmitted signal element into equally spaced sectors with a number of powers of 2 according to the number of quantization bits, extract the phase angle of the center point of each sector as a candidate phase value for discretization, and form the discrete phase value set by combining all candidate phase values.

[0017] S22: Using a pseudo-random number generator, a set of phase values ​​is randomly selected from the discrete phase value set and assigned to multiple array elements of the antenna array to construct the transmit waveform parameters. At the same time, the convergence threshold value is set according to the minimum performance requirements and maximum iteration limit of the system for beamforming gain.

[0018] As a further aspect of the present invention, step S3 specifically comprises:

[0019] S31: Call the composite channel state matrix and the current transmitted waveform parameters, use the array manifold vector to calculate the power spectral density of the desired signal in the spatial channel to obtain the signal energy, and at the same time calculate the covariance matrix of the sidelobe interference signal and the background noise other than the desired direction, and perform trace operation on the covariance matrix to obtain the interference noise energy.

[0020] S32: Using the constraints of the minimum variance distortionless response algorithm, under the premise of ensuring constant desired directional gain, an optimization equation is established with the goal of minimizing output interference noise power. The covariance matrix of the interference plus noise is inverted, and the inversion result is multiplied with the steering vector of the desired signal and normalized to generate the receiving filter weight value.

[0021] As a further aspect of the present invention, the process of generating the receiving filter weight values ​​in S32 includes:

[0022] The sampling covariance matrix corresponding to the interference noise energy is obtained. In order to avoid the ill-conditioned problem that occurs in the matrix inversion process, a diagonal loading factor is introduced to numerically compensate the main diagonal elements of the sampling covariance matrix to obtain the corrected robust covariance matrix.

[0023] The cost function with the criterion of maximizing the output signal-to-interference-plus-noise ratio is solved using the Lagrange multiplier method. The product of the inverse of the modified robust covariance matrix and the target steering vector is calculated. The product result is then divided by the quadratic product of the steering vector, the inverse matrix, and the conjugate transpose of the steering vector to generate the weight values ​​of the receiving filter.

[0024] As a further aspect of the present invention, step S4 specifically comprises:

[0025] S41: Keep the phases of all array elements except the current array element to be optimized unchanged, traverse each candidate phase value in the discrete phase value set as the trial phase of the current array element, and calculate the quantization signal-to-interference-plus-noise ratio and Cramer-Rao boundary corresponding to the trial phase in combination with the weight value of the receiving filter.

[0026] S42: Construct a weighted objective function including the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, select a candidate phase value that can make the objective function reach an extreme value as the optimal phase, use the optimal phase to update the configuration of the corresponding array element in the transmitted waveform parameters, and perform the above update operation on all array elements in a preset order.

[0027] S43: Calculate the rate of change of the objective function value before and after the update. If the rate of change is lower than the convergence threshold value, stop the iteration, map the transmitted waveform parameters to the corresponding binary index sequence, add a frame header and a check bit, and generate a locally optimal low-precision quantized transmitted waveform sequence and a receiving filter weight vector.

[0028] As a further aspect of the present invention, the calculation process of the quantized signal-to-interference-plus-noise ratio in S41 includes:

[0029] Obtain the received filter weight values, the composite channel state matrix, and the current trial phase parameters, and calculate the quantized signal-to-interference-plus-noise ratio using the following formula:

[0030] ;

[0031] in, This represents the quantized signal-to-interference-plus-noise ratio. This represents the weight value of the receiving filter. Represents the composite channel state matrix, This represents the transmitted waveform vector, including the current probe phase. The covariance matrix representing the interference signal. The power variance representing the background Gaussian white noise. This represents the conjugate transpose operator.

[0032] As a further aspect of the present invention, the update process of the transmitted waveform parameters in S42 includes:

[0033] Obtain the preset Cramer-Rao boundary weight coefficients, calculate the Fisher information matrix under the current trial phase, invert the Fisher information matrix and extract the main diagonal elements to obtain the Cramer-Rao boundary;

[0034] The quantized signal-to-interference-plus-noise ratio is summed with the weighted inverse of the corresponding Cramer-Rao bound to construct a comprehensive performance index. After traversing the set of discrete phase values, the discrete phase value corresponding to the maximum comprehensive performance index is selected and directly replaced with the original value of the current optimization dimension in the transmit waveform parameter vector to complete a single greedy iterative update.

[0035] As a further aspect of the present invention, the generation process of the locally optimal low-precision quantized transmit waveform sequence and the receive filter weight vector in S43 includes:

[0036] Obtain the index number of each discrete phase value in the set of discrete phase values ​​in the converged transmitted waveform parameters, and convert the index number into a low-precision quantized transmitted waveform sequence that conforms to the Gray code rule;

[0037] A control frame structure including a synchronization frame header, user identifier, and instruction type is constructed based on the communication protocol standard. The low-precision quantized transmit waveform sequence is filled into the data payload field of the control frame, and a cyclic redundancy check code is calculated and appended to the end of the frame. The locally optimal low-precision quantized transmit waveform sequence and the receiving filter weight vector are generated through parallel-to-serial conversion operation.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0039] In this invention, a composite channel state matrix containing position and amplitude error factors is constructed by combining multi-dimensional parameters such as spatial coordinates and time deviation. This effectively predicts and compensates for channel estimation deviations caused by spatiotemporal lag in high-dynamic scenarios. A discrete phase numerical set is constructed based on the hardware quantization bit precision to deeply fit the real physical link constraints. The signal and interference noise energy under the composite channel are substituted into the minimum variance distortionless response model to solve for the receiving filter weights. On this basis, iterative search and parameter optimization are performed by combining the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao bound to generate a locally optimal low-precision quantized transmit waveform sequence and receiving filter weight vector adapted to the hardware characteristics. This overcomes phase quantization errors while achieving deep nulling of interference signals, significantly improving the beam pointing accuracy and link transmission robustness of UAV communication networks under non-ideal channel conditions. It solves the problems of high power consumption, long signal processing time, and various error situations affecting perception and communication performance in the actual use of UAVs, such as multi-UAV cluster collaborative operation. Attached Figure Description

[0040] Figure 1 This is the main flowchart of a joint beamforming design method in an integrated UAV network according to the present invention;

[0041] Figure 2 This is a flowchart illustrating the construction process of the composite channel state matrix of this invention.

[0042] Figure 3 This is a flowchart of the initialization process for the transmitted waveform parameters of this invention;

[0043] Figure 4 This is a flowchart of the receiver filter weight generation process of the present invention;

[0044] Figure 5 This is a flowchart of the waveform parameter update and instruction generation process of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0046] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0047] Please see Figure 1 and Figure 2 This invention provides a technical solution: a joint beamforming design method in an integrated UAV network, comprising the following steps:

[0048] S1: Obtain spatial coordinates, time deviation, pointing angle and channel response values. Calculate position error, time error and amplitude factor based on spatial coordinates, time deviation and pointing angle. Add position error, time error and amplitude factor to channel response values ​​to construct composite channel state matrix.

[0049] The specific steps of S1 are as follows:

[0050] S11: Obtain the spatial coordinates of the UAV and the ground user, as well as the time deviation of the system synchronization clock. Calculate the relative distance vector between the UAV and the user using the Euclidean distance algorithm. Combine the pointing angle to calculate the geometric projection component of the beam on the propagation path. Then, derive the position error and time error based on the preset error distribution model.

[0051] The derivation process of the position error in S11 includes:

[0052] A three-dimensional spherical coordinate system is established with the center of the UAV antenna array as the origin. The spatial coordinates are converted into azimuth and pitch angle values. The angle deviation between the antenna array normal direction and the line-of-sight path is calculated based on the flight attitude data of the UAV. The influence of atmospheric turbulence on beam pointing is simulated using a Gaussian random process. The angle deviation and random disturbance components are linearly superimposed to calculate the angle offset in the horizontal and vertical dimensions. The angle offset is then mapped to an equivalent path difference value to generate the position error.

[0053] S12: Based on the free space path loss model and relative distance vector, the signal attenuation coefficient is calculated to generate an amplitude factor. The position error and time error are converted into corresponding phase rotation terms. The amplitude factor is used to weight and correct the channel response value. The phase rotation terms are then superimposed on the corrected channel response value to construct a composite channel state matrix.

[0054] This step aims to establish an accurate physical layer link model. The process begins by initializing the data acquisition interface in the UAV's onboard processing unit. The specific data acquisition process is as follows: The UAV uses its onboard high-precision real-time dynamic carrier phase differential positioning module to read the current longitude, latitude, and altitude data in real time at a frequency of 10Hz. The latitude and longitude coordinates are then converted into spatial coordinates in a geocentric rectangular coordinate system using Gauss-Kruger projection. The unit is meters; simultaneously, the UAV receives GPS location information uploaded by the ground user terminal via the downlink control channel. Time deviation of the system synchronization clock It is obtained through a precise time protocol. The system records the timestamp difference between the master clock and the slave clock, and obtains a stable time deviation value after multiple smoothing and filtering processes.

[0055] The Gauss-Kruger projection mentioned above is an equal-angle transverse cylindrical projection method that projects points on the Earth's ellipsoid onto a plane while keeping the angles constant, thus facilitating the calculation of distance and orientation in a Cartesian coordinate system.

[0056] After acquiring the aforementioned basic data, the system executes the Euclidean distance algorithm. The processor calls the mathematical operation library to calculate the relative distance vector between the drone and the ground user. And calculate its modulus. Set the drone's coordinates as follows: User coordinates are Then the relative distance The calculation result is Meters. Next, combining the current pointing angle of the UAV, the geometric projection of the beam on the propagation path is calculated. Regarding the derivation of the position error, the system establishes a local three-dimensional spherical coordinate system with the center of the UAV antenna array as the origin. The relative coordinates are converted into the theoretical azimuth angle using a coordinate transformation matrix. With pitch angle To simulate the effects of vibration and atmospheric turbulence in actual flight, a Gaussian stochastic process model is introduced into the system. The azimuth deviation is set. Follows a mean of 0 and a variance of Normal distribution, pitch angle deviation Follows a mean of 0 and a variance of The normal distribution, where and Based on the current level 3 wind speed corresponding to The standard deviation is obtained by looking up a table. The system generates two random numbers following this distribution as perturbation components, which are then superimposed on the theoretical angle. When mapping the angular offset to an equivalent path difference value, the formula is used. Calculations are made regarding the spacing between antenna elements. Set to 5 millimeters.

[0057] Subsequently, the amplitude factor and phase rotation term are calculated. Amplitude factor Calculations are based on the free-space path loss model. The system's operating frequency is set. 28GHz, the speed of light for Meters per second. Path loss. The calculation formula is Substitute the numerical values ​​into the calculation: dB dB dB, summed to obtain the total path loss dB. Calculated Converted to linear voltage attenuation coefficient Calculation results Time error Transformed into a phase rotation term .set up If it is 1 nanosecond, then Radius. Path difference corresponding to positional error. This is also converted into phase error. .

[0058] The aforementioned free-space path loss model is a mathematical model that describes the attenuation of signal power as the propagation distance increases when electromagnetic waves propagate in an ideal, unobstructed environment. The attenuation is mainly proportional to the square of the frequency and the distance.

[0059] Finally, a composite channel state matrix is ​​constructed. For equipment A uniform planar array of elements; the system first generates an ideal line-of-sight channel vector. , of which The elements are , For wave vectors, This represents the element position. Then, the vector is weighted using an amplitude factor, i.e. Finally, the calculated total phase error Superimposed in the form of rotation factors, i.e. This composite channel state matrix It accurately reflects the combined impact of physical environment and hardware non-idealities on signal transmission, providing high-fidelity environmental parameters for subsequent beamforming.

[0060] Please see Figure 1 and Figure 3 S2: Divide the phase points into equally spaced points on a circle with a radius equal to the modulus of the transmitted signal element according to the number of quantization bits, construct a discrete phase value set, select the initial transmitted waveform parameters from the discrete phase value set, and set the convergence threshold value.

[0061] The specific steps of S2 are as follows:

[0062] S21: Obtain the system's preset number of quantization bits, divide the circle on the complex plane with a radius equal to the modulus of the transmitted signal element into equally spaced sectors with a number of powers of 2 according to the number of quantization bits, extract the phase angle of the center point of each sector as a candidate phase value for discretization, and form a discrete phase value set by combining all candidate phase values.

[0063] S22: Use a pseudo-random number generator to randomly select a set of phase values ​​from the discrete phase value set and assign them to multiple array elements of the antenna array to construct the transmit waveform parameters. At the same time, set the convergence threshold value according to the minimum performance requirements of the system for beamforming gain and the maximum number of iterations.

[0064] The core of this step lies in discretizing the continuous beamforming phase space to accommodate the hardware limitations of the digital phase shifter and to provide an initial state for the iterative optimization algorithm. First, step S21 is executed, where the system reads the quantization bit depth parameter stored in the configuration register. This parameter is determined by the accuracy of the hardware phase shifter. This embodiment sets... Then the circle in the complex plane with radius equal to the modulus of the transmitted signal element is divided into: Five equally spaced sectors. The angular width of each sector is... The system calculates the phase at the center point of each sector as a candidate value, using the following formula: ,in Values ​​range from 0 to 7. Discrete phase numerical set. The specific calculations are as follows: All values ​​are stored in the system's lookup table in double-precision floating-point format.

[0065] Next, step S22 is executed to initialize the transmit waveform parameters. The system initializes the transmit waveform parameters for each element of the antenna array (set to...). (A uniform planar array of array elements) is used, and the Mason rotation algorithm is called to generate an integer index between 0 and 7. This index is then used to select from the discrete phase value set. Extract the corresponding phase value and assign it to the initial transmitted waveform vector. The corresponding element. Let the random index of the first element be 3, then... The value assigned is To ensure the diversity of initial solutions and avoid getting trapped in local optima, the system generates multiple sets of initial vectors in parallel.

[0066] At the same time, set the convergence threshold value. With maximum number of iterations These two parameters determine the termination condition for the subsequent optimization process. Convergence threshold. This is set as the lower limit of the relative rate of change of the objective function. Based on the experimental data shown in Table 1, when... When the value is set too high, the algorithm stops prematurely, resulting in insufficient gain; when the value is set too low, the computation time increases exponentially but the performance improvement is slight. Therefore, this solution selects... As a balance point, the maximum number of iterations is set to... This is to prevent the algorithm from entering an infinite loop under extreme channel conditions. Finally, the system initializes the transmit waveform parameters. Discrete phase numerical set The threshold parameters are loaded into the cache, ready for computation in the S3 stage.

[0067] Table 1. Performance and overhead comparison experimental data under different convergence thresholds.

[0068]

[0069] As shown in Table 1, the experimental data indicate that when the convergence threshold is set to... At the same time, the system can maintain a high beam gain (18.2 dBi) while keeping the convergence time in the millisecond range (8.1 ms), meeting the real-time requirements of UAV communication. Compared to The threshold was adjusted, and the gain was improved by 2.4dB, which is a significant effect.

[0070] Please see Figure 1 and Figure 4 S3: Calculate signal energy and interference noise energy based on composite channel state matrix and transmitted waveform parameters, and input the signal energy and interference noise energy into minimum variance distortionless response algorithm to calculate the receiving filter weight value;

[0071] The specific steps for S3 are as follows:

[0072] S31: Call the composite channel state matrix and the current transmitted waveform parameters, use the array manifold vector to calculate the power spectral density of the desired signal in the spatial channel to obtain the signal energy, and at the same time calculate the covariance matrix of the sidelobe interference signal and the background noise other than the desired direction, and perform trace operation on the covariance matrix to obtain the interference noise energy.

[0073] S32: Utilizing the constraints of the minimum variance distortionless response algorithm, under the premise of ensuring constant desired directional gain, an optimization equation is established with the goal of minimizing output interference noise power. The covariance matrix of interference plus noise is inverted, and the inversion result is multiplied with the steering vector of the desired signal and normalized to generate the receiving filter weight value.

[0074] The process of generating the receiver filter weight values ​​in S32 includes:

[0075] To obtain the sampling covariance matrix corresponding to the interference noise energy, in order to avoid the ill-conditioned problem that occurs in the matrix inversion process, a diagonal loading factor is introduced to numerically compensate the main diagonal elements of the sampling covariance matrix to obtain the corrected robust covariance matrix.

[0076] The cost function with the criterion of maximizing the output signal-to-interference-plus-noise ratio is solved by using the Lagrange multiplier method. The product of the inverse of the modified robust covariance matrix and the target steering vector is calculated. The product result is divided by the quadratic product of the steering vector, the inverse matrix, and the conjugate transpose of the steering vector, thereby generating the receiving filter weight values.

[0077] This step utilizes the composite channel constructed in S1 and the waveform initialized in S2 to calculate the optimal filter weights at the receiver based on the minimum variance distortionless response criterion, in order to suppress interference and enhance the signal. In S31, the processor first calls the composite channel state matrix. and current transmit waveform parameters Calculate the energy of the desired signal. This is estimated using the squared inner product of the array manifold vector and the emitted waveform. The system acquires data samples from 200 snapshots. The sample data contains the desired signal, with the direction being... The system calculates the sampling covariance matrix of the received signal, which is used to detect interference signals and noise. To separate the interference noise energy, the system estimates the interference plus noise covariance matrix during the known quiet period. The total interference noise energy level can be obtained by performing a trace operation on this matrix.

[0078] The aforementioned minimum variance distortionless response refers to an adaptive beamforming algorithm. Its principle is to minimize the total power of the array output under the constraint that the gain in the desired signal direction is constant (distortion-free), thereby suppressing interference and noise from undesired directions to the greatest extent.

[0079] In S32, the core task is to generate the receiving filter weight values. Because the sampling covariance matrix may become ill-conditioned in real-world environments due to insufficient snapshots or signal source coherence, the system must incorporate diagonal loading techniques. This is used to obtain the interference noise covariance matrix. Calculate the diagonal loading factor. The system first measures the noise floor power. for Watt, setting It is 10 times the noise floor power. Construct the corrected robust covariance matrix. ,in Let be the identity matrix. The optimization problem is solved using the Lagrange multiplier method; the closed-form solution to this equation is: The system uses the Cholesky decomposition method to... Perform the inverse operation to obtain .

[0080] in, This represents the weight values ​​of the receiving filter; The inverse matrix representing the modified robust covariance matrix; The steering vector representing the direction of the desired signal; Represents the conjugate transpose of the directional vector; superscript Represents the matrix inversion operation; superscript This represents the conjugate transpose operation.

[0081] To verify the accuracy of the calculation, a simplified method is used here. Perform calculations and verifications on the matrix. (Settings) Expected signal steering vector First, calculate the inverse matrix, whose determinant is... ,but Next, we calculate the molecule: Calculate the denominator: Finally, the receiving filter weight values ​​are obtained. The calculated receiver filter weights will be used in step S4 to calculate the signal-to-interference-plus-noise ratio (SINR) to assess the quality of the current transmitted waveform.

[0082] Please see Figure 1 and Figure 5 S4: Search within the set of discrete phase values ​​based on the weight values ​​of the receiving filter, calculate the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, update the transmit waveform parameters according to the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, encode and convert the transmit waveform parameters when the convergence threshold value is met, and generate a locally optimal low-precision quantized transmit waveform sequence and a receiving filter weight vector.

[0083] The specific steps for S4 are as follows:

[0084] S41: Keep the phases of all array elements except the current array element to be optimized unchanged, traverse each candidate phase value in the discrete phase value set as the trial phase of the current array element, and calculate the quantization signal-to-interference-plus-noise ratio and Cramer-Rao boundary corresponding to the trial phase by combining the weight values ​​of the receiving filter.

[0085] The calculation process of the quantized signal-to-interference-plus-noise ratio in S41 includes:

[0086] Obtain the receiving filter weight values, the composite channel state matrix, and the current trial phase parameters, and calculate the quantized signal-to-interference-plus-noise ratio using the following formula:

[0087] in, Represents the quantitative signal-to-interference-plus-noise ratio. This represents the weight values ​​of the receiving filter. Represents the composite channel state matrix. This represents the transmitted waveform vector, including the current probe phase. The covariance matrix representing the interference signal. The power variance representing the background Gaussian white noise. This represents the conjugate transpose operator;

[0088] S42: Construct a weighted objective function that includes quantized signal-to-interference-plus-noise ratio and Cramer-Rao bound, select a candidate phase value that can make the objective function reach an extreme value as the optimal phase, use the optimal phase to update the configuration of the corresponding array element in the transmitted waveform parameters, and perform the above update operation on all array elements in a preset order.

[0089] The update process for the transmitted waveform parameters in S42 includes:

[0090] Obtain the preset Cramer-Rao boundary weight coefficients, calculate the Fisher information matrix under the current trial phase, invert the Fisher information matrix and extract the main diagonal elements to obtain the Cramer-Rao boundary;

[0091] The quantized signal-to-interference-plus-noise ratio is summed with the weighted inverse of its corresponding Cramer-Rao bound to construct a comprehensive performance index. After traversing the set of discrete phase values, the discrete phase value corresponding to the maximum comprehensive performance index is selected and directly replaced with the original value of the current optimization dimension in the transmit waveform parameter vector to complete a single greedy iteration update.

[0092] S43: Calculate the rate of change of the objective function value before and after the update. If the rate of change is lower than the convergence threshold value, stop the iteration, map the transmitted waveform parameters to the corresponding binary index sequence, add frame header and check bit, and generate the local optimal low-precision quantized transmitted waveform sequence and the receiving filter weight vector.

[0093] The process of generating the locally optimal low-precision quantized transmit waveform sequence and the receive filter weight vector in S43 includes:

[0094] Obtain the index number of each discrete phase value in the set of discrete phase values ​​in the converged transmit waveform parameters, and convert the index number into a low-precision quantized transmit waveform sequence that conforms to the Gray code rule;

[0095] Based on the communication protocol standard, a control frame structure including a synchronization frame header, user identifier, and instruction type is constructed. The low-precision quantized transmit waveform sequence is filled into the data payload field of the control frame, and a cyclic redundancy check code is calculated and appended to the end of the frame. The locally optimal low-precision quantized transmit waveform sequence and the receiving filter weight vector are generated through parallel-to-serial conversion operation.

[0096] This step involves the jointly designed closed-loop feedback loop, which uses a greedy iterative algorithm to find the optimal combination of discrete phases and converts the result into digital commands. In S41, the system enters the iterative loop. For the transmitted waveform vector... The first in The system iterates through the discrete phase value set sequentially, keeping the phases of the other 63 elements unchanged. Each candidate value in the array is used as the trial phase for the current array element. For each trial phase, the system combines the receiver filter weight values ​​calculated in S3. With the composite channel state matrix Calculate the quantized signal-to-interference-plus-noise ratio .

[0097] Quantization signal-to-interference-plus-noise ratio The calculation formula is: ;

[0098] in, This represents the calculated quantized signal-to-interference-plus-noise ratio (SIR). This represents the receiver filter weight values ​​obtained in step S3. This represents the composite channel state matrix constructed in step S1; Represents the transmitted waveform vector containing the current probe phase; The covariance matrix representing the interference signal; The power variance represents the background Gaussian white noise; superscript Represents the conjugate transpose operation; Represents the modulo square operation for complex numbers.

[0099] Specific numerical values ​​are introduced here for calculation examples and verification. The system is simplified to the MISO system. Weights Assuming The equivalent received signal vector is Interference matrix This is a diagonal matrix with diagonal elements of 0 and 1. Noise power. Watt. First, calculate the molecules: (here) For a real number, the conjugate transpose is the transpose, which is the dot product of a complex vector. Take the square modulo: Next, calculate the interference terms in the denominator: The sum of the denominators is The final calculated signal-to-interference-plus-noise ratio (SINNR) is obtained. This result indicates that, under the current trial phase configuration, the signal strength at the receiver is 5.4 times the sum of interference and noise, which is a usable link quality metric.

[0100] In S42, the system considers not only the quantized signal-to-interference-plus-noise ratio (SINR) but also the Cramer-Rao bound in its decision-making. The system obtains preset weight coefficients. Calculate the Fisher information matrix. The Fisher information matrix is ​​calculated based on the expected value of the second derivative of the log-likelihood function of the received signal with respect to the direction of arrival. The calculated Cramer-Rao bound is the main diagonal element of the inverse of the Fisher information matrix. System construction comprehensive performance index. After the system iterates through all eight phases in the discrete phase value set, it compares the eight comprehensive performance index values. The phase corresponding to the maximum index value is selected, and the transmitted waveform parameters are then... The Middle Update the first element. Then process the second element. The process continues until all 64 array elements have been updated, completing one iteration.

[0101] The aforementioned Cramer-Rao bound refers to the theoretical lower bound that the variance of any unbiased estimator can reach in statistical parameter estimation theory, and here it is used to measure the limiting accuracy of beam pointing estimation.

[0102] In S43, the objective function is calculated. The rate of change. If the rate of change is lower than the convergence threshold value. If the iteration stops, the transmitted waveform parameters at this point are the optimal transmitted waveform parameters. The system maps each complex phase in the parameters back to its corresponding value in the set. The index in. For example, The corresponding index is 1, which is binary 001. For 64 array elements, 64 3-bit binary codes are generated, totaling 192 bits. The system constructs a control frame: the frame header is set to 10101010 for physical layer synchronization; the user identifier is set to a 16-bit ID; the data payload is filled with the aforementioned 192-bit phase index stream; and a calculated CRC-16 cyclic redundancy check code is appended to the tail. Finally, through a parallel-to-serial conversion circuit, the frame is sent as a serial digital signal to the phase shifter controller of the RF front end to complete the physical execution of beamforming.

[0103] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A joint beamforming design method in an integrated UAV network, characterized in that, Includes the following steps: S1: Obtain spatial coordinates, time deviation, pointing angle and channel response value; calculate position error, time error and amplitude factor based on the spatial coordinates, time deviation and pointing angle; superimpose the position error, time error and amplitude factor onto the channel response value to construct a composite channel state matrix. S2: Divide the phase points at equal intervals on a circle with a radius equal to the modulus of the transmitted signal element according to the number of quantization bits, construct a discrete phase value set, select the initial transmitted waveform parameters from the discrete phase value set, and set the convergence threshold value. S3: Calculate the signal energy and interference noise energy based on the composite channel state matrix and the transmitted waveform parameters, and input the signal energy and the interference noise energy into the minimum variance distortionless response algorithm to calculate the receiving filter weight value; S4: Based on the received filter weight values, search within the set of discrete phase values, calculate the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, update the transmitted waveform parameters according to the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, encode and transform the transmitted waveform parameters when the convergence threshold value is met, and generate a locally optimal low-precision quantized transmitted waveform sequence and a received filter weight vector.

2. The joint beamforming design method in an integrated UAV network according to claim 1, characterized in that, The specific steps of S1 are as follows: S11: Obtain the spatial coordinates of the UAV and the ground user, as well as the time deviation of the system synchronization clock. Calculate the relative distance vector between the UAV and the user using the Euclidean distance algorithm. Combine the pointing angle to calculate the geometric projection component of the beam on the propagation path. Then, derive the position error and time error based on the preset error distribution model. S12: Based on the free space path loss model and the relative distance vector, calculate the signal attenuation coefficient to generate an amplitude factor, convert the position error and time error into corresponding phase rotation terms, use the amplitude factor to perform weighted correction on the channel response value, and superimpose the phase rotation terms into the corrected channel response value to construct the composite channel state matrix.

3. The joint beamforming design method in an integrated UAV network according to claim 2, characterized in that, The derivation process of the position error in S11 includes: A three-dimensional spherical coordinate system is established with the center of the UAV antenna array as the origin. The spatial coordinates are converted into azimuth and pitch angle values. The angular deviation between the antenna array normal direction and the line-of-sight path is calculated based on the UAV's flight attitude data. The influence of atmospheric turbulence on beam pointing is simulated using a Gaussian random process. The angular deviation and random disturbance components are linearly superimposed to calculate the angular offset in the horizontal and vertical dimensions. The angular offset is then mapped to an equivalent path difference value to generate the position error.

4. The joint beamforming design method in an integrated UAV network according to claim 3, characterized in that, The specific steps of S2 are as follows: S21: Obtain the preset number of quantization bits, divide the circle on the complex plane with a radius equal to the modulus of the transmitted signal element into equally spaced sectors with a number of powers of 2 according to the number of quantization bits, extract the phase angle of the center point of each sector as a candidate phase value for discretization, and form the discrete phase value set by combining all candidate phase values. S22: Using a pseudo-random number generator, a set of phase values ​​is randomly selected from the discrete phase value set and assigned to multiple array elements of the antenna array to construct the transmit waveform parameters. At the same time, the convergence threshold value is set according to the minimum performance requirements and maximum iteration limit of the system for beamforming gain.

5. The joint beamforming design method in an integrated UAV network according to claim 4, characterized in that, The specific steps in S3 are as follows: S31: Call the composite channel state matrix and the current transmitted waveform parameters, use the array manifold vector to calculate the power spectral density of the desired signal in the spatial channel to obtain the signal energy, and at the same time calculate the covariance matrix of the sidelobe interference signal and the background noise other than the desired direction, and perform trace operation on the covariance matrix to obtain the interference noise energy. S32: Using the constraints of the minimum variance distortionless response algorithm, under the premise of ensuring constant desired directional gain, an optimization equation is established with the goal of minimizing output interference noise power. The covariance matrix of the interference plus noise is inverted, and the inversion result is multiplied with the steering vector of the desired signal and normalized to generate the receiving filter weight value.

6. The joint beamforming design method in an integrated UAV network according to claim 5, characterized in that, The process of generating the receiving filter weight values ​​in S32 includes: The sampling covariance matrix corresponding to the interference noise energy is obtained. In order to avoid the ill-conditioned problem that occurs in the matrix inversion process, a diagonal loading factor is introduced to numerically compensate the main diagonal elements of the sampling covariance matrix to obtain the corrected robust covariance matrix. The cost function with the criterion of maximizing the output signal-to-interference-plus-noise ratio is solved using the Lagrange multiplier method. The product of the inverse of the modified robust covariance matrix and the target steering vector is calculated. The product result is then divided by the quadratic product of the steering vector, the inverse matrix, and the conjugate transpose of the steering vector to generate the weight values ​​of the receiving filter.

7. The joint beamforming design method in an integrated UAV network according to claim 6, characterized in that, The specific steps of S4 are as follows: S41: Keep the phases of all array elements except the current array element to be optimized unchanged, traverse each candidate phase value in the discrete phase value set as the trial phase of the current array element, and calculate the quantization signal-to-interference-plus-noise ratio and Cramer-Rao boundary corresponding to the trial phase in combination with the weight value of the receiving filter. S42: Construct a weighted objective function including the quantized signal-to-interference-plus-noise ratio and the Cramer-Rao boundary, select a candidate phase value that can make the objective function reach an extreme value as the optimal phase, use the optimal phase to update the configuration of the corresponding array element in the transmitted waveform parameters, and perform the above update operation on all array elements in a preset order. S43: Calculate the rate of change of the objective function value before and after the update. If the rate of change is lower than the convergence threshold value, stop the iteration, map the transmitted waveform parameters to the corresponding binary index sequence, add a frame header and a check bit, and generate a locally optimal low-precision quantized transmitted waveform sequence and a receiving filter weight vector.

8. The joint beamforming design method in an integrated UAV network according to claim 7, characterized in that, The calculation process of the quantized signal-to-interference-plus-noise ratio in S41 includes: Obtain the received filter weight values, the composite channel state matrix, and the current trial phase parameters, and calculate the quantized signal-to-interference-plus-noise ratio using the following formula: ； in, This represents the quantized signal-to-interference-plus-noise ratio. This represents the weight value of the receiving filter. Represents the composite channel state matrix, This represents the transmitted waveform vector, including the current probe phase. The covariance matrix representing the interference signal. The power variance representing the background Gaussian white noise. This represents the conjugate transpose operator.

9. The joint beamforming design method in an integrated UAV network according to claim 8, characterized in that, The update process of the transmitted waveform parameters in S42 includes: Obtain the preset Cramer-Rao boundary weight coefficients, calculate the Fisher information matrix under the current trial phase, invert the Fisher information matrix and extract the main diagonal elements to obtain the Cramer-Rao boundary; The quantized signal-to-interference-plus-noise ratio is summed with the weighted inverse of the corresponding Cramer-Rao bound to construct a comprehensive performance index. After traversing the set of discrete phase values, the discrete phase value corresponding to the maximum comprehensive performance index is selected and directly replaced with the original value of the current optimization dimension in the transmit waveform parameter vector to complete a single greedy iterative update.

10. The joint beamforming design method in an integrated UAV network according to claim 9, characterized in that, The process of generating the locally optimal low-precision quantized transmit waveform sequence and the receive filter weight vector in S43 includes: Obtain the index number of each discrete phase value in the set of discrete phase values ​​in the converged transmitted waveform parameters, and convert the index number into a low-precision quantized transmitted waveform sequence that conforms to the Gray code rule.

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