80 results about "Least mean square error" patented technology

The invention discloses a method and system for testing the electrical performance of a semiconductor chip, and the method comprises the steps: building a mapping relation between dielectric loss and loss frequency through obtaining the dielectric loss data, internal material characteristics and dynamic load power factors of the chip under each frequency band, recognizing a loss abnormal region, and analyzing the influence of the loss abnormal region on the performance of the chip; correcting test data by adopting a minimum mean square error algorithm, and optimizing the impedance of the power supply network and the dynamic switching characteristic of a functional module; calculating the breakdown voltage distribution of each layer in the chip, identifying a high-voltage risk region, optimizing the voltage distribution by using a gradient descent algorithm, and dynamically adjusting the working voltage and current; power factor fluctuation is analyzed through a sliding window algorithm, an abnormal area is identified, a power factor is optimized through a PID algorithm, and the working state and transient response of a chip in a dynamic load are improved. According to the method, the problems of nonlinearity of high-frequency dielectric loss, non-uniform breakdown voltage distribution and unstable power factor response under a dynamic load are solved, and comprehensive and accurate evaluation and optimization of chip performance are realized.

The invention discloses a multi-path cooperative communication method, belongs to the technical field of wireless communication, and is used for solving the problems of inaccurate path selection, unreasonable power distribution and inaccurate signal combination caused by dynamic change of a channel state in multi-path communication. The method comprises the following steps: acquiring channel state information of at least three communication paths in real time through a transmitting end, wherein the channel state information comprises signal strength, frequency offset and noise power spectral density; dynamically dividing and distributing the data stream to each path based on the path quality factor, and adjusting the transmitting power by combining the signal strength and the noise difference value; a receiving end carries out time domain alignment by adopting a sliding window cross-correlation algorithm, and carries out phase compensation based on a minimum mean square error criterion; path reallocation is triggered when the continuous bit error rate exceeds a threshold. According to the method, the multi-path resource utilization rate can be optimized, and the communication stability and the data transmission efficiency are improved.

The invention provides a method for assisting a movable antenna to realize secure communication by STAR-RIS, and relates to the technical field of wireless communication. According to the method, the safety and the rate of the system are maximized by jointly optimizing the beam forming of the base station, the position of the movable antenna and the STAR-RIS coefficient matrix. The method specifically comprises the following steps: constructing a downlink MIMO system and establishing a joint optimization problem; decomposing the joint optimization problem into three sub-problems by adopting an alternate optimization framework; respectively utilizing a particle swarm optimization algorithm and a weighted minimum mean square error method to be combined with a Majoritization-Minimalization algorithm to solve each sub-problem, and respectively utilizing the particle swarm optimization algorithm and the weighted minimum mean square error method to be combined with the Majoritization-Minimalization algorithm to solve each sub-problem; and alternately executing related steps until system security and rate convergence to obtain a solution of the joint optimization problem. By the adoption of the method, the safety communication performance of the system is remarkably improved, full-space coverage and precise beam regulation are achieved, the algorithm convergence is high, the solving efficiency is high, and the method is suitable for a downlink system with multiple users and multiple eavesdropping nodes.

The invention belongs to the technical field of artificial intelligence, and discloses an energy data acquisition encryption and privacy protection method and system. The power grid frequency deviation FREQ of a power line carrier communication system is obtained at the time of power grid data acquisition, and deviation information at different time points is combined into a random number sequence by using a hash function; a specific pulse signal is sent at a data acquisition point to obtain PLC channel pulse response CIR, a minimum mean square error estimation algorithm is used for processing, and key features such as the maximum pulse amplitude are extracted to serve as channel feature values. And then, inputting the random number sequence and the channel characteristic value into a deep reinforcement learning model trained by a large amount of historical data to generate an encryption key. And finally, encrypting the acquired energy data by adopting an AES algorithm based on the key, transmitting the energy data through an OFDM technology, and meanwhile, generating the key again according to the change of a channel characteristic value by setting a self-adaptive security mechanism. According to the method, the safety and privacy of energy data acquisition and transmission are remarkably improved.

A method includes determining estimated features comprising second order statistics based on at least one received signal. The method also includes classifying, using a machine learning network, each channel of the at least one received signal into a channel profile based on the estimated features. The method also includes obtaining multiple minimum mean square error (MMSE) channel estimation weights from a database based on the estimated features, the database storing (i) representative MMSE estimation weights and (ii) channel cluster representatives indexed by the estimated features. The method also includes applying a respective MMSE channel estimation weight for each channel.

The invention discloses an archive room full life cycle management system based on a radio frequency identification technology, and relates to the field of archive management, the optimal weight vector of each reader-writer antenna array is obtained through a minimum mean square error algorithm, the transmitting phase and amplitude of each node signal are accurately adjusted according to the optimal weight vector, and in the spatial dimension, the optimal weight vector of each reader-writer antenna array is obtained. Through cooperative control and spatial filtering of beams transmitted by a plurality of distributed reader-writer nodes, beam main lobe energy is converged to a target passive RFID electronic tag, a radiation zero point is formed in the direction of a metal file cabinet, meanwhile, spatial power superposition and directional interference suppression are realized, multipath reflection interference in a metal environment is reduced, and the reliability of the metal file cabinet is improved. The signal-to-noise ratio of the label is fundamentally improved, the response rate of the label is ensured to be improved to 98% or above through a dynamic frequency modulation mechanism, and the problem of signal multipath reflection caused by a metal file cabinet is effectively solved.

The invention discloses a multi-target tracking method based on asynchronous weighted time sequence optimization, a program, equipment and a storage medium, and belongs to the field of underwater multi-UUV cooperative detection of multiple targets. The method comprises the following steps: firstly, setting a tolerance time threshold value related to a target speed and an allowable error, approximating asynchronous data at a short time interval as synchronous data, identifying and screening active and passive sonar information from the same target by virtue of an association algorithm, and eliminating random clutters; then, the target detection precision is improved through a minimum mean square error weighted fusion method; and finally, inputting the optimized detection data into a probability hypothesis density filter to complete updating estimation of the multi-target state and number. According to the algorithm, the multi-target state estimation precision is improved, the excessive estimation phenomenon of the number of the targets is effectively reduced, the problem of time sequence asynchronization of active and passive sonar detection information in a multi-target detection environment can be solved, and the challenges of high-density clutter aliasing on multi-target state estimation precision reduction and excessive estimation of the number of the targets can be solved.

The invention provides an elastic wave CT (Computed Tomography) signal denoising method for improving the inversion precision of karst cave exploration, which belongs to the technical field of engineering exploration and comprises the following steps of: performing normalization preprocessing on a received signal, suppressing random noise by adaptive filtering, converting a time domain signal into an F-K domain and identifying a surface wave, filtering a surface wave signal by a Gaussian band elimination filter, and converting the F-K domain signal back to the time domain. The method comprises the following steps of: highlighting a take-off characteristic of a weak P wave covered by a strong surface wave through normalization; using a minimum mean square error adaptive filter to accurately filter random noise; subsequently converting to an F-K domain, and smoothly attenuating the surface wave through a Gaussian band elimination filter by utilizing the frequency wave number distribution difference of the surface wave and the P wave; the method realizes layered removal of random noise and surface waves, finally outputs high-purity direct P-wave signals, greatly improves P-wave travel time pickup and wave velocity calculation precision, and remarkably improves inversion precision of an elastic wave CT exploration method in detection of geologic bodies such as karst caves and fracture zones.

The invention particularly relates to a model-free deflection angle estimation method based on a radial basis function neural network. The method comprises the following steps: acquiring measurement data of a master inertial navigation system and a slave inertial navigation system; based on the distribution characteristics of the measurement data in the training set, updating the clustering centers of the hidden layer radial basis function to obtain the number of the clustering centers; constructing a radial basis function neural network based on the measurement data of the training set and the number of the clustering centers, and training the radial basis function neural network by adopting a minimum mean square error algorithm to obtain a trained radial basis function neural network; and inputting measurement data in the test set into the trained radial basis function neural network to obtain a predicted deflection angle, performing calculation compensation on the pose amount of the master-slave inertial navigation system by combining a transfer alignment error equation according to the predicted deflection angle, and outputting a final alignment result based on a Kalman filtering model of a model-free deflection angle. The dynamic deformation angle modeling compensation method solves the problems that in an existing dynamic deformation angle modeling compensation method, model parameters are difficult to determine, and transfer alignment is caused when the parameters are time-varying.

The invention relates to a dynamic self-adaptive communication, sensing and calculation integrated method for an air-space-ground integrated network. The method comprises the following steps: S1, constructing an air-space-ground integrated network model fusing a low earth orbit satellite, an unmanned aerial vehicle and ground users / edge nodes; s2, establishing a cross-layer communication-perception-calculation integrated joint optimization model, and respectively forming two modes of binary unloading and partial unloading according to whether a task can be segmented or not; s3, for a binary unloading mode, realizing distributed solution and generating an unloading decision and a beam / power / bandwidth / computing resource scheme by using a dynamic adaptive prediction alternating direction multiplier (DAP-ADMM); and S4, aiming at a partial unloading mode, adopting a weighted minimum mean square error alternative optimization method, introducing a receiving equalizer, error weight and other auxiliary variables, executing block coordinate descent iteration, and outputting an unloading proportion, power distribution and a beam forming result. According to the DA-ISCC architecture and the optimization algorithm thereof provided by the invention, cross-domain calculation unloading with low time delay, high throughput and low overhead can be realized in a high-dynamic space-air-ground integrated network environment, and the DA-ISCC architecture and the optimization algorithm thereof can be widely applied to emergency communication, low-altitude logistics, ocean monitoring and other scenes.

A DNN-based OTFS time-frequency domain situation awareness and channel estimation method models the frequency domain selective fading channel in wireless communication as an autoregressive process, and then converts the estimation problem into an autoregressive coefficient estimation process. Training data is obtained through processes such as discrete sampling of the channel impulse response function, DFT transformation, feature matrix representation, and channel prior information generation. The preprocessing of the training data is completed, and a data and model driven neural network is designed. The neural network is trained with a large amount of prior channel data. The optimal frequency domain correlation parameter estimation under the minimum mean square error of the network is solved by offline training and online estimation, and then the corresponding channel response is obtained. The algorithm can achieve lower bit error rate and complexity under the same signal-to-noise ratio.

The invention discloses a communication, sensing and computing resource allocation device in a space multilayer network, and the method comprises the following steps: constructing an air-space-ground communication, sensing and computing integrated system, integrating three functions of sensing, communication and computing by an unmanned aerial vehicle of an air-base layer, and performing communication and sensing interference management between the unmanned aerial vehicle and the ground layer through an NOMA-MIMO technology, the tasks can be further unloaded to the low-orbit satellites at the sky level; in order to minimize the weighted energy consumption of the system, a joint precoding, beam forming and resource allocation iterative optimization algorithm is provided; according to the algorithm, an original problem is decomposed into a precoding design sub-problem, a beam forming design sub-problem, an unloading proportion sub-problem and a computing resource allocation sub-problem, and the sub-problems are solved through fractional programming based on quadratic transformation, a weighted minimum mean square error and a continuous convex approximation method. The method shows remarkable superiority in the aspect of optimizing the weighted total energy consumption of the system, and solves the problems of common inductance computing resource allocation, interference management and the like in the spatial multilayer network.

The application discloses a fluid antenna assisted ISAC system joint beam forming and position optimization method based on a quantum particle swarm optimization algorithm. Specifically, an ISAC multi-user system model equipped with a two-dimensional fluid antenna is established, and a beam forming optimization problem is constructed; a weighted least mean square error algorithm is used to convert a communication and rate target function, so that a complex fractional problem is converted into an integral problem, and solving is simplified; an alternating optimization algorithm is used to decompose the original joint optimization problem into multiple sub-problems; an auxiliary variable and a receiving beam former are alternately solved, an iterative solution of a beam forming vector is solved by using a successive convex approximation algorithm, and a fluid antenna position problem is solved by using a quantum particle swarm optimization algorithm. The method of the application effectively improves the communication and rate under the restrictions of the fluid antenna position, the minimum perceived beam pattern gain and the maximum base station transmission power, and is superior to a traditional ISAC system equipped with a fixed antenna.

The invention discloses a self-calibration optimized image denoising method using convexity, which belongs to the technical field of image processing, is used for image denoising under the condition that the noise level is unknown, and comprises the following steps: generating a noisy image and a secondary noisy image pair, and constructing a conditional denoising network; based on a linear minimum mean square error estimation theory, deducing a denoising estimator and calculating a variance of an estimation error, and introducing a noise ratio parameter to carry out normalization analysis; determining an optimal synthetic noise variance through derivation or a ternary search algorithm by using convexity of an error variance function; performing empirical formula calibration on the optimal synthetic noise variance; and training and executing the denoising network by using the calibrated optimal variance, and outputting a final denoised image. According to the method, the robust training process is constructed under the condition that the noise level is unknown, noise statistical characteristics can be effectively learned, and the limitation that the noise level needs to be known in advance in a traditional method is overcome.

The application discloses a kind of super-network auxiliary data model double-drive MIMO-OFDM signal detection method, comprising: a signal detection network with a set of adjustable parameters, and a trainable super-network.First, using linear least mean square error algorithm obtains channel estimation, will estimated channel matrix H LMMSE As the input of super-network, adjustable parameter Ω hyper Provided to signal detection network;Then, channel matrix H LMMSE And received signal vector y are real value decomposition, together with the equivalent noise variance information derived and input to signal detection network, signal detection network is unfolded by the expectation propagation algorithm enhanced by graph neural network, in combination with real domain channel matrix real domain received signal vector environmental information and the parameter Ω hyper Provided by super-network, give the estimation to sending symbol vectorFinally, demapping is carried out to estimated symbol vector, and the estimation to original sending bit is obtainedThe application has high detection performance, consumes less time, and has strong generalization, and can adapt to scene change.

This invention provides a method, apparatus, and terminal device for estimating transmitted signals, belonging to the field of wireless communication technology. The method includes determining a received signal vector, a channel matrix, and a noise variance; inputting the received signal vector, channel matrix, and noise variance into a signal estimation model to obtain an estimated transmitted signal value output by the signal estimation model; wherein, the signal estimation model is obtained by optimizing a minimum mean square error detector based on a Fourier transform algorithm and a pre-determined weighting factor, and the weighting factor is used to adjust the convergence step size. This invention uses the Fourier transform algorithm to replace matrix inversion in the minimum mean square error detector, avoiding explicit matrix operations, reducing computational complexity, and can be used in M-MIMO systems with large-scale antennas. Furthermore, adjusting the convergence step size based on the weighting factor is beneficial for stable convergence and prevents severe degradation of bit error rate performance under high-dimensional and high-order modulation conditions.

The invention relates to a multi-beam satellite communication system resource allocation method, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, modeling a multi-beam satellite communication scene; s2, modeling inter-satellite and satellite-ground link identifiers; s3, modeling inter-satellite and satellite-ground link channel gains; s4, modeling inter-satellite and satellite-ground link transmission rates; s5, modeling a queue length; s6, modeling a resource allocation constraint condition; s7, modeling a multi-beam satellite communication system data scheduling problem as a Markov decision process; s8, constructing and training a near-end strategy optimization network; s9, determining a precoding strategy based on a weighted minimum mean square error algorithm; and S10, optimizing and determining a data scheduling strategy based on a PPO algorithm. According to the method, the long-term performance optimization of the system is realized through joint optimization of data scheduling and precoding.

The invention discloses an IQ orthogonal calibration method, device and equipment and a storage medium, and the method comprises the steps: collecting the orthogonal mismatch state information of a current receiving signal and the operation state of a minimum mean square error calibration algorithm, and constructing a state vector; inputting the state vector into a depth Q network to determine a step length adjustment action, and updating a step length parameter of a minimum mean square error calibration algorithm based on the step length adjustment action; based on the step length parameter, calibrating the current received signal through a minimum mean square error calibration algorithm to obtain a calibrated signal and an error signal; determining a reward value according to the error signal, and storing the state vector, the step length adjustment action and the reward value as experience samples in an experience playback pool; and periodically updating the current network parameters of the deep Q network by using the experience samples in the experience playback pool until the error converges. According to the method, the convergence speed of IQ orthogonal calibration is improved, the steady-state residual error is reduced, and the adaptability to environment change is improved.

The application discloses a kind of FSI dynamic ranging methods based on high-order linear prediction-minimum mean square error, mainly solve the problem of slow speed of existing FSI ranging system to the distance to be measured solution, insufficient dynamic measurement capability.Solution includes: 1) construct to generate the optical frequency scanning interference ranging system required for distance solution interference signal;2) set the sweep rate of laser, so that it carries out optical frequency continuous tuning output, after interference, generates the sinusoidal interference signal that changes continuously on time domain after detection;3) the data acquisition device in FSI system carries out real-time acquisition to sinusoidal interference signal, and sample point is transmitted to measurement module;4) measurement module utilizes high-order linear prediction-minimum mean square error cascade algorithm to carry out real-time prediction to sampling data, and utilizes the interference signal frequency obtained to complete the real-time solution of distance.The application can make the distance solution speed of FSI system significantly improve, effectively improve the ranging rate.

The invention belongs to the technical field of wireless communication, and discloses a cellular-removed large-scale MIMO distributed dual-time-scale hybrid precoding method, which comprises the following steps of: constructing a user average channel covariance matrix at each access point (AP); carrying out eigenvalue decomposition on the user average covariance matrix and extracting a main eigenvector to construct an initial simulation precoding matrix; performing constant modulus constraint processing on the initial analog precoding matrix to obtain an analog precoding matrix; a user sends an orthogonal pilot frequency, each AP carries out instantaneous channel state information estimation by adopting a minimum mean square error criterion, and a downlink channel is obtained based on the reciprocity of uplink and downlink channels; each AP designs a digital precoding vector according to a downlink channel; the AP allocates power to the user according to the power constraint; system spectral efficiency is calculated to assess method performance. According to the method, the expandability of the system is effectively improved while the channel estimation overhead and the operation complexity are reduced, and the method is suitable for efficient implementation of the honeycomb-removed large-scale MIMO system.

The application discloses a channel estimation method and related device, wherein the method comprises: performing minimum mean square error (MMSE) channel estimation based on the product of the first amplitude factor and the first noise power of the first dimension, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain the channel estimation value matrix of the first dimension; wherein the channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation; the first amplitude factor is a positive number less than or equal to 1; and performing MMSE channel estimation based on the channel estimation value matrix of the first dimension, the second channel autocorrelation matrix of the last dimension, the second noise power and the second diagonal matrix to obtain the multi-dimensional channel estimation value matrix. The method can improve the performance of the multi-dimensional step-by-step MMSE channel estimation.

The invention discloses an AFDM peak-to-average ratio suppression method based on pilot frequency position optimization and pre-chirp. The AFDM peak-to-average ratio suppression method comprises the following steps: executing pilot frequency and data overall cyclic shift in a DAF domain for a pilot frequency frame; performing IDAFT on the processed pilot frequency frame to convert the pilot frequency frame into a time domain signal, calculating the PAPR of the time domain signal, and finding out a group of arrangement modes with the lowest PAPR as output; candidate pre-chirp parameters provided by the data frame are selected, and IDAFT is executed to convert the candidate pre-chirp parameters into time domain signals; performing amplitude limiting processing on a time domain signal of the data frame, performing optimization by using an iterative ACE algorithm, calculating the PAPR of the updated signal, and selecting a group with the lowest PAPR as the output of the data frame; and a receiving end respectively demodulates the pilot frequency frame and the data frame by using a maximum likelihood-minimum mean square error detector. According to the invention, the PAPR of the transmission signal of the AFDM system is effectively suppressed, and the transmission reliability of the system is improved.

The embodiment of the application provides an estimation method of continuous-discrete maximum correlation entropy distributed cubature Kalman filtering, which comprises the following steps: establishing a continuous-discrete target tracking model in a multi-sensor network; based on a maximum correlation entropy criterion, a 1.5-order ITO Taylor expansion, a cubic cubature rule, an average consensus method and a minimum mean square error criterion, calculation is performed to obtain a continuous-discrete maximum correlation entropy distributed cubature Kalman filtering method; and for the continuous-discrete target tracking model, target tracking is performed according to the continuous-discrete maximum correlation entropy distributed cubature Kalman filtering method to obtain accurate target state information. The method can accurately estimate the parameters of an aircraft target and improve reliability.

The invention provides a multi-sensor target fusion method and system based on sequential filtering, and belongs to the technical field of intelligent driving, and the method comprises the steps: obtaining a fusion track of a plurality of target sensors, and calculating a track state at a current moment according to a track state at a previous moment; adopting a greedy algorithm to respectively match the plurality of target sensors with the current time track; and updating the track state by adopting sequential filtering according to the matched measurement value. According to the method, the greedy algorithm is adopted for matching, the time complexity is far smaller than that of a KM algorithm and the like, but an approximate global optimal matching result can be achieved in most cases, and the calculation amount can be reduced to a great extent. According to the method, sequential filtering is adopted to update the flight path state, the method serves as a recursive filtering algorithm, measurement data are comprehensively considered, meanwhile, the method is easy to implement, optimal estimation of the system state can be provided based on the minimum mean square error criterion, and in addition, filtering parameters and measurement weight values can be adjusted according to the actual situation.

The invention provides an anti-eavesdropping distributed tracking detection method for a target tracking system under low communication resources, and belongs to the technical field of distributed tracking detection. The method comprises the following steps: establishing a random dynamic model of a target tracking system under low communication resources; then designing a time-varying distributed filter under the meaning of minimum mean square error according to the dynamic model; designing a filter gain matrix of each sensor node in the system at the moment; substituting the filter gain matrix into a filter to obtain state estimation of each sensor node in the system at the moment; calculating the upper bound of the estimation error covariance of each sensor node in the system at the moment; and repeating the steps until the requirements are met. According to the method, the problem that the anti-eavesdropping distributed tracking detection of the target tracking system with a periodic scheduling mechanism and an encryption and decryption mechanism is difficult to process at the same time by the existing distributed tracking detection method is solved, so that the tracking performance of the tracking system is improved.

The application discloses an anti-retransmission interference capturing method based on fast iterative minimum mean square error filtering and relates to the technical field of satellite navigation and positioning. In the small-delay retransmission interference scene, iterative multi-parameter joint estimation is carried out on the basis of two-dimensional correlation, the parameter estimation precision is improved through RMMSE filtering, the identification difficulty of target signals and retransmission interference signals is reduced, and the signal detection probability is improved. The system used by the anti-retransmission interference capturing method based on fast iterative minimum mean square error filtering comprises the following modules: a global two-dimensional correlation and peak detection module, a single-path signal parameter estimation initialization module, a single-path signal iterative multi-parameter joint estimation module, an interference reconstruction and elimination module, a local two-dimensional correlation and peak detection module, a double-path signal parameter estimation initialization module, a double-path signal iterative multi-parameter joint estimation module and a signal detection and decision module.

An inertial-based method and system for detecting bending strain in a pipeline, a device, and a medium. The method comprises: acquiring position information of a pipeline of a preset length, collected by an IMU (S1); performing a wavelet transformation on the position information to obtain an initial target position information (S2); performing sliding filtering on the initial target position information through a sliding window according to a preset sequence and window width, and determining a data window at each sliding of the sliding window on the initial target position information (S3); determining a fit coefficient by the minimum mean squared error depending on the respective data windows (S4).Using a numerical value at the center of each data window as a filter value, determining a filter sequence based on the respective filter values ​​and the fit coefficient, and using the filter sequence as the second target position information (S5); determining a pipe bending strain force based on the second target position information (S6); detecting the pipe bending strain based on the pipe bending strain force (S7). This method removes noise twice, thereby improving the accuracy of the position information calculated by an inertial measurement unit (IMU) and solving the problem of noise affecting the position information calculated by the IMU.

The application relates to the technical field of communication, and specifically provides a reconfigurable intelligent surface (RIS) assisted ocean communication method and system based on LR-BCD, which comprises the following steps: constructing an RIS assisted ocean communication system model; constructing an ocean double-path channel model based on the ocean double-path propagation theory and in combination with a sea surface reflection coefficient; extracting low-rank characteristics of the channel by using singular value decomposition (SVD), projecting high-dimensional beamforming and phase shift optimization problems into a low-dimensional subspace; constructing a low-dimensional and rate maximization problem, and converting the problem into a convex subproblem by using a weighted minimum mean square error (WMMSE) criterion; alternately updating a low-dimensional precoding matrix and a phase shift vector by using a block coordinate descent (BCD) algorithm; and finally reconstructing the optimized low-dimensional variables back to a high-dimensional space and performing discretization and quantization. The application utilizes the sparsity of the ocean channel, significantly reduces the calculation complexity of large-scale RIS optimization through dimension reduction processing, and meanwhile, maintains the system and rate performance equivalent to that of a full-rank algorithm.

The invention discloses a large-scale MIMO non-orthogonal multiple access bidirectional relay method based on an unmanned aerial vehicle, and belongs to the field of wireless communication, and the method comprises the steps: constructing a hovering rotor unmanned aerial vehicle working in a full duplex mode as an amplification forwarding relay, and constructing a model of M ground user pairs needing bidirectional communication; the ground user pair sends a superposed signal to the unmanned aerial vehicle by adopting a non-orthogonal multiple access protocol; the unmanned aerial vehicle performs pilot frequency training by using a minimum mean square error method to obtain imperfect channel state information, and adopts maximum ratio merging or maximum ratio transmission linear processing as a relay precoder; and the unmanned aerial vehicle broadcasts the processed signals back to all ground user pairs. The large-scale MIMO non-orthogonal multiple access bidirectional relay system based on the unmanned aerial vehicle is reliable in performance, high in communication capacity, strong in coverage capability and good in anti-noise performance.

This invention belongs to the field of wireless communication, specifically relating to a STAR-RIS-assisted 6G integrated sensing and communication beamforming design method. The method includes constructing a STAR-RIS-assisted integrated sensing and communication system model; under constraints of transmit power, sensing performance, and multi-user service quality, establishing an optimization problem aimed at maximizing multi-user communication and speed by jointly designing the base station transmit beamforming and the transmission-reflection coefficients of STAR-RIS; transforming the optimization problem into an easily tractable equivalent form based on a weighted minimum mean square error equivalent transformation; decoupling the optimization problem based on the equivalent form into two independent sub-problems using an alternating optimization method; solving the sub-problems using a successive convex approximation method and a semi-definite relaxation method, and iteratively updating the algorithm until convergence; this invention effectively improves system communication performance while ensuring sensing performance and multi-user communication quality.