172 results about "Minimum mean square error" patented technology

The invention relates to the technical field of wireless communication, and discloses a compensation method for electronic radio frequency communication. According to the compensation method for the electronic radio frequency communication, the radio frequency signal and the channel state information are acquired, the original data set is generated, and the preprocessing steps of denoising, signal amplitude correction, phase correction and the like are performed, so that the data quality is improved, accurate parameter evaluation is performed by adopting the minimum mean square error, the accuracy of channel characteristic parameters is ensured, and the communication quality is improved. A mathematical model constructed based on the parameters enables calculation of compensation parameter gain and phase deviation to be more accurate, so that attenuation and distortion caused by signal propagation in a channel are effectively compensated, and finally, quality evaluation is carried out on the bit error rate and the signal-to-noise ratio of the compensated signal, so that the compensation effectiveness is ensured, and the signal quality is improved. And if the compensation is invalid, the calculation can be returned in real time for recalculation, so that the quality of the whole signal is improved.

The invention relates to the technical field of communication, and discloses a network communication chip signal interference suppression processing method. The method comprises the following steps: acquiring a local oscillator phase jitter sequence in real time through an on-chip phase noise monitoring unit; estimating Doppler frequency shift and channel coherence time based on the sequence; dynamically adjusting the closed-loop bandwidth of the phase-locked loop; and generating a phase compensation signal, and performing time-varying channel minimum mean square error equalization in combination with a received baseband signal to eliminate inter-symbol interference. The system integrates a phase noise monitoring function module, a channel parameter estimation function module, a phase-locked loop bandwidth regulation function module, a phase compensation generation function module and an interference joint elimination function module. By means of dynamic bandwidth reconstruction and high-precision phase compensation, the bit error rate under high-speed movement is remarkably reduced, the 5G millimeter wave communication standard requirement is met, and the method has the advantages of being achieved on a whole chip, low in delay and high in robustness.

The invention relates to a narrowband minimum mean square error echo suppression method introducing a complex neural network, and belongs to the field of adaptive echo cancellation. An existing neural network method still has the problem of inaccurate priori knowledge modeling, and phase information in complex signals is not fully utilized. According to the method, a plurality of DNNs are adopted to carry out modeling and estimation on step length, loudspeaker nonlinear distortion and error signals respectively. And the adaptive capacity to an echo channel in a complex environment is improved by utilizing the efficient parameter learning capacity of a plurality of neural networks. According to the method, the phase information of complex data is fully utilized, the nonlinear distortion problem of the loudspeaker can be effectively suppressed, meanwhile, the small model parameter quantity is kept, efficient echo suppression is achieved, high practicability and hardware adaptability are achieved, and the method is suitable for various practical application scenes.

The invention relates to the technical field of satellite transceiving, in particular to a Ka-band satellite small station transceiver system which comprises an antenna module, a transceiving switching module, a low-noise amplification module, a frequency conversion module, a signal processing module, a power management module and a system control module. According to the Ka-band satellite small station transceiver system, the accuracy and stability of antenna beam adjustment are improved in combination with adaptive filtering, and many problems existing in Ka-band application of an existing satellite small station transceiver system are solved; the system has the remarkable advantages of high receiving and transmitting link isolation, high noise suppression capability, low power consumption, small size, good compatibility to multi-band multi-mode signals and the like, and has wide application prospects in the field of satellite communication, and meanwhile, the signal processing module applies a power inversion algorithm based on a minimum mean square error (LMS) algorithm to improve the quality of transmitted signals. And the transmission quality of the signal in the satellite small station scene in the transmission process is effectively improved.

The invention relates to a tunnel toxic gas detection method, device, equipment and system and a storage medium, and belongs to the technical field of information detection.The method comprises the steps that concentration measured values of multiple toxic gases collected by multiple gas sensor nodes in a tunnel are obtained; inputting the concentration measurement values of the various toxic gases into a linear minimum mean square error estimation model, and outputting the position of a gas leakage source and an estimated value of a gas release rate; inputting the estimated value of the gas leakage source parameter into a gas diffusion model, and outputting a gas diffusion parameter; the gas diffusion model is a static smoke plume gas diffusion model based on turbulence diffusion, and the gas diffusion parameters comprise the diffusivity of a gas leakage source; and inputting the estimated value of the gas leakage source parameter, the gas diffusion parameter and the standard-exceeding concentration threshold value of the toxic gas into a finite element prediction model, and outputting a toxic gas standard-exceeding prediction result. According to the invention, continuous monitoring and dynamic early warning of the toxic gas in the tunnel can be realized.

The invention provides an intelligent metasurface enabled cellular-free large-scale MIMO (Multiple Input Multiple Output) network resource efficiency optimization method. The method comprises the following steps: establishing an intelligent metasurface enabled cellular-free large-scale MIMO network model under a Rician fading channel; based on the cellular-free large-scale MIMO network model, performing channel estimation by using a linear minimum mean square error method; based on the cellular-free large-scale MIMO network model, constructing a resource efficiency closed expression when a maximum ratio merging receiving scheme is used; constructing an optimization problem formula for maximizing the network resource efficiency according to the resource efficiency closed expression; and according to the optimization problem expression, designing an optimization method for cooperatively controlling a user power control coefficient and intelligent metasurface phase shift. The spectrum efficiency and the energy efficiency of the intelligent metasurface energized cellular-free large-scale MIMO network are improved, the compromise between the spectrum efficiency and the energy efficiency is effectively realized, the optimization of the network performance is realized, and an important theoretical support is provided for the development of a 6G Internet of Things network.

The invention relates to the technical field of radio measurement, and discloses a multi-band radio measurement calibration method and system, and the method comprises the steps: S1, carrying out the sampling and preprocessing of a multi-band radio signal, and obtaining a preprocessed signal; s2, performing signal classification to obtain a mixed signal; s3, performing signal reconstruction on the mixed signal to obtain a pure target signal; s4, performing dynamic sub-band division on the pure target signal, introducing a regularization factor into each sub-band, and iteratively calculating a minimum mean square error until a residual error is smaller than a first target value to obtain an initial calibration parameter; s5, establishing logarithmic interval frequency point distribution based on the initial calibration parameter, and obtaining a compensation calibration coefficient; and S6, verifying the compensation calibration coefficient by adopting the standard calibration signal, and when the root-mean-square error exceeds a second target value, repeatedly executing the steps S4-S6 until the precision requirement is met to obtain a target calibration result, so that the interference signal is effectively suppressed, and the calibration precision is improved.

The invention discloses a magnetic anomaly data de-noising method and de-noising system based on step-by-step U-Net, and relates to the technical field of geophysical exploration, and the method comprises the steps: carrying out the forward modeling calculation based on a magnetic anomaly model in a simulation region, and obtaining a synthetic magnetic anomaly data set; a U-Net step-by-step denoising network model is constructed; designing a self-adaptive multi-stage training mechanism based on mean square error minimization; training the U-Net step-by-step denoising network model through the training set, taking parameters of the U-Net step-by-step denoising network model as initial weights, and performing fine tuning on the U-Net step-by-step denoising network model by utilizing supervised learning; the verification set is used for verifying the effect, and the effectiveness of the whole step-by-step denoising network model is verified according to the prediction effect of the test set; a forward noise adding process and a reverse noise removing process are designed, network behaviors are automatically adjusted according to noise steps, the problems of high training cost and poor performance when a deep learning model processes colored noise are effectively solved, and the problem of low signal-to-noise ratio is better solved.

The invention relates to a land subsidence prediction method which comprises the following steps: assuming that n subsidence monitoring points, including n1 known points and n2 points to be inserted, are arranged in a region; according to the spatial position information of the known points, the ground subsidence of all the subsidence monitoring points is fitted by using a quadric surface fitting method; calculating the settlement deviation of n1 known points, and taking the spatial position information and the settlement deviation of the known points as training samples; a PPLN network model is constructed, the input is spatial position information and a quadric surface fitting value, the output is settlement deviation, the training sample is utilized to train the PPLN network model, and a land subsidence prediction model is obtained; and predicting the predicted settlement of the n2 points to be inserted by using the trained land subsidence prediction model. According to the method, the advantages of the quadric surface fitting method and the PPLN method are fully utilized, the gross error influence can be reduced, the signal estimation value with the minimum mean square error can be fitted by adjusting the parameters through self-learning, and therefore the interpolation precision is improved.

The invention discloses a short-term scheduling method for a cascade hybrid pumped storage power station based on pump station addition, which comprises the following steps of: adding an energy storage pump station between cascade series hydropower stations, and dynamically drawing up a reservoir capacity constraint by adopting a reservoir capacity dynamic control strategy considering the cascade hybrid pumped storage power station; constructing a cascade hybrid pumped storage power station short-term scheduling model based on the added energy storage pump station, wherein the cascade hybrid pumped storage power station short-term scheduling model takes the minimum mean square error of the residual load of a power grid as a target; solving the scheduling model based on an improved step-by-step optimization algorithm POA; and a comprehensive risk-benefit evaluation system is constructed, multi-dimensional targets of a system scheduling level and a power station operation level are considered, and the influence of additionally arranging an energy storage pump station on the risk and benefit of the power grid is deeply analyzed. The method can be used for guiding daily scheduling of the cascade hybrid pumped storage power station based on the additionally-arranged pump station, wind and light abandoning phenomena are reduced to the maximum extent while safe and stable operation of a power system is guaranteed, and the absorption capacity of wind and light resources and the power supply reliability of the power system are improved.

The invention relates to a motion MPSK communication radiation source speed estimation method. The method comprises the following steps of: firstly, dividing a received MPSK signal into two paths, estimating motion phase deviation in one path by utilizing a minimum mean square error phase discrimination algorithm, compensating the motion phase deviation and demodulating a message symbol carried by the signal; carrying out signal segmentation on the other MPSK signal according to symbol synchronization information and a symbol period, and compensating a phase of a symbol waveform corresponding to a demodulated message symbol according to the demodulated message symbol; and finally, combining symbol waveform data after phase compensation into a two-dimensional matrix, and solving Doppler frequency for data of different symbols at the same sampling moment through a vertical Fourier transform method so as to obtain the speed of a moving target. Compared with a traditional sensing integrated speed measurement method, the method has the advantages of being low in probability of being found and long in operating distance.

The invention relates to the technical field of agricultural machinery load control, in particular to a corn harvester operation load control system and method, and the system comprises a power extraction module, a speed regulation and control module, a trend analysis module, a parameter optimization module and a load closed-loop module. According to the method, frequency characteristics are extracted in a periodic and segmented mode through header power signals, load dynamic sensing is carried out, advancing speed adjustment is more real-time and prospective, response sensitivity and stability are improved in combination with PID control, equipment stably operates at multiple crop densities and plots, the torque increment of a main shaft is monitored in real time, and wavelet transformation multi-scale analysis is carried out; load fluctuation fine-grained identification is carried out, a roller gap and power output are dynamically adjusted to optimize parameters with a minimum mean square error, frequency and torque characteristic closed-loop comparison is constructed, control self-matching and continuous self-adjustment are carried out, error minimization and trend tracking feedback are fused, the intelligence of the harvester is enhanced, and load sudden change performance fluctuation and energy consumption loss are reduced.

The invention belongs to the technical field of laser vibration measurement, and provides a self-focusing laser Doppler vibration measurement method and system capable of resisting self-vibration of an optical fiber, and the method comprises the steps: adding a compensation light path which is used for sensing and generating reference light which is equal to an interference signal in amplitude and is opposite to the interference signal in real time in a Doppler vibration measurement light path, and taking the minimum mean square error as a self-adaptive matching criterion; interference phases obtained by synchronous demodulation of the compensation light path and the main light path are subjected to adaptive matching and cancellation in amplitude and phase dimensions, residual errors of vibration of the vibration measurement system and other environmental interferences are suppressed below noise of the vibration measurement system, and the purpose of balancing and weakening optical fiber vibration interferences in an instrument is achieved. The influence of the vibration factor of the instrument is reduced, and the interference influence is accurately eliminated through amplitude and phase matching by a demodulation algorithm corresponding to a compensation light path; meanwhile, by dynamically observing and updating the return light intensity threshold, low-frequency disturbance is resisted, and accurate automatic focusing is achieved.

Various aspects of the present disclosure generally relate to wireless communication. Some aspects more specifically relate to discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) waveform pre-equalization for MIMO communications. A receiver device, such as an extended reality (XR) device, may be configured to offload a portion of data processing to a companion device, such as a user equipment (UE). In some aspects, the UE may generate a DFT-S-OFDM waveform and may apply a pre-equalization to the DFT-S-OFDM waveform. Applying the pre-equalization to the DFT-S-OFDM waveform may include applying a minimum mean square error (MMSE) pre-equalization to the DFT-S-OFDM waveform. The receiver device may receive the pre-equalized DFT-S-OFDM waveform from the UE and may process the pre-equalized DFT-S-OFDM waveform. For example, the receiver device may decode the pre-equalized DFT-S-OFDM waveform without performing fast Fourier transform (FFT) or inverse discrete Fourier transform (IDFT) operations.

The invention discloses a cognitive function screening system and method based on an MMSE (Minimum Mean Square Error) prediction model, and relates to the technical field of data analysis, the method comprises the following steps: collecting physical examination index data, removing missing records, adopting a multiple interpolation method for interpolation, and obtaining multiple sets of complete data sets; a continuous prediction model is constructed, MMSE continuous prediction values are obtained, and a sensitivity analysis report is generated; constructing a first-stage classification model, adaptively dividing an optimal threshold combination, and dividing a sample into a high-confidence region, a to-be-discriminated region and a low-confidence region; if the sample size of the to-be-discriminated region is higher than a preset training threshold value, constructing an enhanced feature set, and constructing a second-stage classification model; when the prediction probability reaches the optimal re-discrimination threshold value, the classification result in the first stage is corrected, and otherwise, the classification result is maintained; if not, maintaining the classification result; and integrating the classification results to obtain a final classification result of all the samples.

The invention relates to the technical field of wireless communication, and particularly discloses a 5G NR frequency fading channel estimation method and system based on deep learning assistance, a channel estimation model based on deep learning assistance is constructed, the model adopts a low-level feature extraction network to learn low-level features of an LS channel response matrix, and the low-level features of the LS channel response matrix are extracted. The method comprises the following steps of: firstly, extracting global correlation features of a time domain and a frequency domain by using a multi-scale feature extraction network, so that the state perception of a 5G NR receiver channel is more comprehensive, finally, improving the fitting capability in combination with an output layer to solve the problem of insufficient noise estimation of a traditional LS algorithm, and improving the accuracy of the state information of a receiver communication channel at a high frequency band and different delays. Simulation experiments show that the channel response mean square error is obviously superior to the LS algorithm, and the robustness is higher than that of a linear minimum mean square error algorithm.

The invention relates to the field of material fatigue life prediction, and particularly discloses a rubber material fatigue life prediction method based on physical-data fusion driving, and the method comprises the following steps: S1, obtaining rubber fatigue life data through a test; s2, constructing a fatigue life prediction physical model based on continuous medium mechanics; s3, preprocessing the predicted life and test data of the physical model; s4, determining the number of nodes of an input layer, a hidden layer and an output layer in the BP neural network model according to the preprocessed data; s5, determining a connection weight and a threshold value of the BP neural network model by adopting a gradient descent method by taking the minimum mean square error between the actually measured life and the predicted life as a target; and S6, in combination with the connection weight and the threshold, establishing a rubber material fatigue life prediction model based on physical-data fusion driving. According to the physical-data fusion driving method in the scheme, the rubber fatigue life prediction precision can be improved, and the failure risk of rubber products in practical application is reduced.

The invention discloses an unlicensed access method for distributed energy equipment and related equipment, and belongs to the technical field of wireless communication unlicensed random access, a central processing unit optimizes a non-orthogonal structure pilot frequency matrix by using a complex sequence convex optimization iterative decorrelation algorithm, and allocates obtained low-coherence pilot frequency to equipment; the active equipment sends a signal frame with pilot frequency and data to the wireless access point; the central processing unit estimates the active state of the equipment by using the pilot frequency in the signal frame received by the wireless access point, and performs channel estimation by using a linear minimum mean square error algorithm on the basis; the central processing unit decodes the equipment data by using the continuous interference cancellation technology according to the data in the signal frame received by the wireless access point; and the central processing unit adds a combined filter in each iteration process of continuous interference elimination, compares the signal to interference plus noise ratio of each iteration process with a threshold value, and checks whether decoding succeeds or not.

The invention discloses a 1-bit DAC precoding optimization design method suitable for a large-scale MU-MIMO system, and the method comprises the steps: setting base station data configuration, and modulating an input symbol vector; quantizing the modulated input signal by using a 1-bit DAC (Digital-to-Analog Converter); generating a complex cyclic symmetric Gaussian random channel matrix H by adopting Rayleigh distribution; designing a signal of a receiver end; establishing a 1-bit DAC precoding optimization problem based on a minimum mean square error according to the average transmitting power of the base station and a 1-bit DAC character set constraint; a mean square error between a recovery signal and an input signal is minimized by utilizing a designed ADMM-HB method under the constraint of the average transmitting power of a base station and a 1-bit DAC character set. According to the method, the ADMM-HB method is used in the optimization process, the signal mean square error of the receiving end and the transmitting end can be minimized with low calculation complexity, the system efficiency is improved, the system hardware cost and power consumption are reduced, and the method better conforms to engineering practical application.

The invention discloses a state estimation method and system for a distributed multi-region power system, and relates to the technical field of power management. The method comprises the following steps: acquiring local measurement data, a neighbor measurement signal and a neighbor state estimation value signal of a to-be-measured power system in a target region in the distributed multi-region power system so as to calculate a state priori estimation value, a state error covariance matrix upper bound and a neighbor gain matrix of the to-be-measured power system at a next moment; the method comprises the following steps of: obtaining an upper bound and a neighbor gain parameter for calculating an error covariance, obtaining a weighted error covariance through arbor decomposition, calculating a state error covariance weight matrix and a measurement error covariance weight matrix through a preset Cauchy kernel function, and calculating a gain matrix of a local estimator through a fixed point iteration method, and state estimation of the to-be-tested power system is completed. A reasonable Cauchy kernel maximum relative entropy condition is designed to replace a minimum mean square error condition, so that state estimation in a non-Gaussian noise environment is improved.

The invention provides an IRS-assisted cellular-free large-scale URLLC network optimization method based on sum rate maximization. The IRS-assisted cellular-free large-scale URLLC network optimization method comprises the following steps: establishing an IRS-assisted cellular-free large-scale URLLC network model under Rician channel fading; performing channel estimation by using a linear minimum mean square error method; analyzing a downlink reachable rate when a zero-forcing precoding scheme is used, constructing a non-convex optimization problem by taking maximization of a total rate as a target, and decomposing the non-convex optimization problem into a power control sub-problem and a phase optimization sub-problem; and designing an alternating optimization algorithm, and solving two sub-problems of power control and phase optimization to obtain a power control coefficient and intelligent metasurface phase shift. According to the alternating optimization algorithm designed by the invention, the interference suppression effect is remarkably improved, so that the reliability of the system is greatly enhanced. The technology is specially designed for a 6G scene, supports multi-service dynamic scheduling, can realize low-cost IRS hardware deployment, and provides an extensible and low-delay solution for innovation and performance breakthrough of a next-generation communication network.

The present invention belongs to the field of electronic countermeasures, and particularly relates to a method for jointly estimating the roll-off coefficient and order of a communication signal shaping filter. This method utilizes the characteristic that the mismatch between the roll-off coefficient and order in the modulator and demodulator has little influence on the bit error performance of the M-PSK signal. A fixed roll-off coefficient and order are selected from the common parameters of the shaping filter to demodulate the communication signal at the receiving end, and the symbol information of the communication signal is obtained. Let a roll-off coefficient range vector alpha with a length of N and a filter order vector S with a length of M be set. An NxM shaping filter parameter matrix is composed of these two vectors, and the symbol information of the communication signal is modulated by the shaping filter determined by all the parameters in this matrix. The mean square error is calculated by comparing the modulated signals with the signals after the lower sideband of the received signal, and a mean square error matrix is obtained. The parameters corresponding to the minimum mean square error are the joint estimation results of the roll-off coefficient and order of the shaping filter.

An embodiment of the present invention discloses a method and device for training an autonomous lane information prediction model. The method includes: obtaining historical vehicle state information and historical lane information, where the historical lane information corresponds to the historical vehicle state information, the historical vehicle state information includes historical vehicle high-precision positioning information and historical vehicle motion information, and the historical lane information is the lane position information passed by the vehicle; training a first model according to the historical vehicle state information, the historical lane information, and a first loss function, where the first model is a long short-term memory recurrent neural network model, the first model is used to autonomously predict the lane position information that the vehicle will pass through, the first model is trained using the backpropagation through time algorithm, and the first loss function is the mean squared error loss function. The present disclosure solves the problem of lane mismatch caused by missing lane information.

The invention discloses a channel estimation method of a time-varying channel in a time delay-Doppler domain pilot mode, which comprises the following steps of: superimposing a hybrid superimposed pilot and a data symbol in a time delay-Doppler domain, the pilot comprising a high-power center pilot and a low-power edge pilot; the signal is converted to a time domain through inverse symplectic Fourier transform and Hisenberg transform; a receiving end obtains a receiving signal through Wigner transform and Sextile Fourier transform; estimating integer time delay and Doppler taps by using a center pilot frequency, and constructing a sparse channel matrix; estimating fractional Doppler and channel gain through a sparse Bayesian learning algorithm; and detecting a data symbol based on a minimum root-mean-square error algorithm, and realizing accurate estimation of channel parameters through iterative interference elimination. According to the method, the peak-to-average power ratio and the algorithm complexity can be effectively reduced, and non-correlation channel components and cross interference are inhibited.

The application relates to a kind of OFDM-based broadband millimeter wave relay system hybrid beamforming design method, comprising: calculating the receiving signal of target node under the condition that each node is all digital processor;The receiving signal is processed using minimum mean square error criterion in target node, and MMSE matrix is calculated;Using the equivalence between maximizing sum rate and minimum weighted mean square error algorithm, the maximum sum rate problem is converted into minimum weighted mean square error problem according to MMSE matrix;Deep unfolding neural network is used to solve the minimum weighted mean square error problem, and all digital processor of each node is obtained;Each node's all digital processor is decomposed based on least square decomposition algorithm, and hybrid beamforming matrix of each node is calculated, the complexity of the algorithm is greatly reduced, and the running time of the system can be effectively improved.

The invention provides a high resource efficiency allocation method in an intelligent reflecting surface assisted cellular-free large-scale MIMO (Multiple Input Multiple Output) system. The method comprises the following steps: establishing an intelligent reflecting surface assisted cellular-free large-scale MIMO system model under a Rician fading channel; based on the system model, performing channel estimation by using a linear minimum mean square error method; based on the system model, constructing a resource efficiency closed expression when the zero-forcing receiving scheme is used; according to the expression, constructing a non-convex optimization problem by taking maximization of system resource efficiency as a target, and decomposing the non-convex optimization problem into a power control sub-problem and a phase optimization sub-problem; and designing an alternative optimization algorithm according to the optimized sub-problems, and solving the power control sub-problem and the phase optimization sub-problem to obtain a power control coefficient and intelligent reflector shift. When a ZF receiving scheme is used, the resource efficiency optimization algorithm has excellent signal processing capacity, the system resource efficiency is remarkably improved, operation steps are convenient to implement, and the dual requirements of a future wireless communication system for high data rate and low energy consumption are met.

The application belongs to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle target searching method and system based on an electromagnetic map. A wireless signal receiving element carried by the unmanned aerial vehicle receives radio frequency signals from a target. Terrain information, scene parameters and the radio frequency signal strength received in the first step are used to construct an electromagnetic map through electromagnetic wave propagation theory. The accurate target position coordinates are estimated through the least mean square error estimation method according to the electromagnetic map obtained in the second step. The unmanned aerial vehicle is controlled to fly to the specified position coordinates. The above steps are repeatedly performed until the search is successful or fails. The application fully utilizes the prior rich real terrain information through the low-cost and low-energy hardware requirement of the unmanned aerial vehicle end signal strength receiving, realizes the accurate positioning and searching of the radio frequency target, and is significantly superior to the algorithm without using the electromagnetic map in terms of accuracy and time efficiency.

The invention provides an uplink MIMO-NOMA (Multiple Input Multiple Output-Non-Orthogonal Multiple Access) system optimization method with a maximized sum rate, which comprises the following steps: firstly, in order to suppress interference between a transmission cluster and a reflection cluster, providing an inter-cluster SIC method: clustering users according to transmission characteristics, adopting space division multiple access in each cluster, and performing inter-cluster SIC of NOMA transmission so as to reduce the interference between the clusters; secondly, a non-convex optimization problem aiming at maximizing the total rate of the system is formulated and solved by using an alternating optimization framework, in the framework, a receiving equalizer is optimized according to a minimum mean square error standard, and transmitting power control of a user is processed through successive convex approximation, so that sub-problems are easy to process; and finally, the beam forming of the active STAR-RIS adopts a sequential rotation design. According to the invention, the dual-fading effect is effectively overcome by adjusting the phase shift and amplitude of electromagnetic waves, an alternating optimization framework is also provided to solve the described non-convex problem, user power is controlled through continuous convex approximation, and active STAR-RIS beam forming is realized through sequential rotation.

The application relates to a weak underwater target azimuth detection pre-tracking method based on belief propagation, which comprises the following steps: converting a weak target tracking problem into solving a joint posterior probability density function (pdf) based on a Bayesian rule; factorizing the joint posterior pdf, constructing a corresponding factor graph through a graph model; solving the transmitted information in the factor graph by using a belief propagation (BP) algorithm, and converting multiple integrals into ordinary integrals by using a Goodman variable substitution principle; constructing a likelihood function by using a complex Wishart distribution to describe the relationship between original sonar data and target states, and simplifying the remaining pdf by using a Gaussian distribution to obtain a confidence approximation of a target state edge posterior pdf; and calculating the number of weak targets and corresponding states by using a minimum mean square error (MMSE) estimator. The application can simultaneously track multiple weak targets without a complex clustering algorithm, has fewer preset parameters, and can accurately and efficiently track target state information including an azimuth, an azimuth angular velocity and target intensity.

The invention discloses an LED lamp driving control method based on broadband power line carrier communication and a controller, and relates to the technical field of intelligent lighting control. By dynamically selecting the anti-interference subcarriers and combining adaptive modulation, CRC (cyclic redundancy check) and MMSE (minimum mean square error) equalization algorithms, channel sensing communication is realized, and the bit error rate is reduced; pWM and analog dimming weights are dynamically distributed by using an LSTM network and a sparrow optimization algorithm, so that the dimming efficiency and accuracy are improved; driving signals are calibrated in real time through PID closed-loop control, multi-parameter fusion diagnosis and OTA remote repair are combined, and the fault prediction accuracy is improved; the method supports multi-node high-concurrency control, improves the signal injection efficiency, reduces the comprehensive energy efficiency, is suitable for scenes of smart home, industrial lighting and the like, and remarkably improves the system reliability and the user experience.