36 results about "Mean square error minimization" patented technology

The waveform equalizing device includes: an FIR filter for generating an equalized signal pattern y(i, n) on the basis of equalization of a waveform of the reproduced signal pattern u(i, n); a Viterbi decoding circuit for detecting a path metric difference s(n) between a correct path determined as a survivor path and an error path which fails to survive the correct path in Viterbi decoding based on the equalized signal pattern y(i, n); a target register for setting a target value ds for the path metric difference s(n); and a tap coefficients update circuit for adapting the equalization according to an error of the detected path metric difference from the target value. The tap coefficients update circuit adapts equalization properties so that the mean square error is minimized, thereby achieving a satisfactory result in lowering the error rate.

The invention provides a flow rate level prediction method based on convolutional neural network deep learning. The method comprises the following steps that: selecting an impact factor which is potentially related to the inflow flow rate of a reservoir as an input set; carrying out sample set classification and original input dataset construction; carrying out standardized processing on the original input dataset in the sample set; building a multilayer convolutional neural network; taking mean square error minimization as a loss function to determine prediction accuracy; carrying out networkparameter training; carrying out network performance testing; checking prediction accuracy; carrying out the rolling learning training on model parameters; automatically saving a learning training achievement, and automatically updating the knowledge record of a real-time library; and through a network model, carrying out calculation to obtain a final flow rate level prediction result. By use ofthe method, low-layer characteristics are combined to form high-layer characteristic fusion, so that an objective can be subjected to advanced abstract description, a data input pattern and a spatialand temporal distribution rule can be found through automatic learning, and therefore, the method can be effectively applied to the field of drainage basin water regimen forecasting.

The waveform equalizing device includes: an FIR filter for generating an equalized signal pattern y(i, n) on the basis of equalization of a waveform of the reproduced signal pattern u(i, n); a Viterbi decoding circuit for detecting a path metric difference s(n) between a correct path determined as a survivor path and an error path which fails to survive the correct path in Viterbi decoding based on the equalized signal pattern y(i, n); a target register for setting a target value ds for the path metric difference s(n); and a tap coefficients update circuit for adapting the equalization according to an error of the detected path metric difference from the target value. The tap coefficients update circuit adapts equalization properties so that the mean square error is minimized, thereby achieving a satisfactory result in lowering the error rate.

The invention, which relates to the field of power grid planning and scheduling operation, provides a daily on-off type peak load operation method of a gas engine set. The power grid and the difference between the peak and valley increase rapidly, so that the peak regulation pressure of the system increases continuously; and the peak regulation pressure of the power grid increases further because of the influence of large-scale feeding of the extra-high-voltage direct-current power. The scheduled electric quantity is used as the single-machine control object; the start-up time range is determined by considering the upper limit and the lower limit of the output; and a feasible continuous start-up time set is searched and determined by using a load value as heuristic information. With consideration of the adjustable output lower limit, the high-peak output process of each start-up scheme is determined in an optimized manner by using an improved load shedding strategy; and an optimal start-up operation scheme and a set generating plane are selected based on a criterion of residual load mean square error minimization. According to the method provided by the invention, the peak regulation space of the gas engine set at the peak load time is released fully, so that the tracking and response capability of the grid peak load is improved effectively; and the grid peak regulation demand and the gas engine set operation restriction are considered simultaneously.

The invention provides a multi-carrier mode low-complexity channel noise-reducing method and a device thereof in a WLAN. According to the method and the device thereof, a known long training sequence section and a highly protected signaling section are used for selecting an optimal impulse response of a channel frequency domain in a frame structure of the multi-carrier mode, and good working threshold of a receiver can be guaranteed in various delay spread environments at a cost of low complexity. By aid of the method, channel response of a time domain is not needed to be detected, but a preset Wiener filtering coefficient is used directly for reducing noise of frequency domain channels containing the noise, a relative optimal frequency domain channel certainly responds, then a follow-up received highly protected signaling sequence section is used for figuring out the frequency domain channel of the signaling section so as to select which one is the optimal, and the selecting criterion may be the minimum mean square error criterion.

The invention discloses a large-scale MIMO low-complexity channel estimation method based on weighted Kapetyn grade number expansion. Firstly a Kapetyn grade number expansion method is utilized for performing approximate expansion on a channel covariance inverse matrix in a Bayesian-MMSE channel estimation expression. A matrix inversion operation is converted to matrix multiplication and matrix addition operations. Then a weighting manner is performed on each coefficient of a polynomial for optimizing polynomial expansion, establishing a model for solving weighting coefficient vectors alpha and beta for minimizing an estimated mean square error, and estimating the channel matrix by means of solving results of alpha and beta. Experiment results represent a fact that an MSE which is obtained through the channel estimation method based on a weighted Kapetyn grade number expansion is convergent to an MMSE method along with order number increase of the polynomial, and furthermore calculation complexity of the channel estimation method is lower than that of the MMSE method. Compared with a traditional Taylor-MMSE and Kapetyn grade number expansion channel estimation method, the channel estimation method based on the weighted Kapetyn grade number has higher convergence speed to the MMSE method.

A Complex Adaptive Phase Discriminator (PD), as presented in some concepts of the present disclosure, is an adaptive filter that accurately estimates the instantaneous frequency of a dynamic complex signal. The PD can demonstrate accurate instantaneous frequency estimation and rapid convergence in dynamic complex signal environments, even when the frequency of its input may change rapidly. A direct PD architecture can estimate the instantaneous frequency of a complex primary signal, iteratively adapting a phase of a complex exponential by minimizing the mean squared error of a complex error signal. Instantaneous frequency can be directly estimated from the phase of the complex exponential. In contrast, an indirect PD architecture can estimate the instantaneous frequency of a complex primary signal, iteratively adapting a complex coefficient by minimizing the mean squared error of a complex error signal. Instantaneous frequency can be indirectly estimated by extracting the phase of the complex coefficient.

The invention aims to provide a design method suitable for a filter coefficient in a filter-and-forward relay network, relates to the technical field of wireless communication relay transmission, and aims to obtain optimal design of the filter coefficient in the filter-and-forward relay network by following a channel capacity maximization principle. The solution of a channel capacity maximization problem is transformed into the solution of a weighted mean square error minimization problem of a receiver in a virtual channel by constructing the virtual channel between a transmitter and the receiver, so that optimal design of the filter coefficient in the whole filter-and-forward relay network is obtained. A good theoretical basis is laid for the design of the filter coefficient, the processing of data and the allocation of resources in the filter-and-forward relay network.

The present application relates to the field of signal processing, and in particular to a shortwave delay estimation method, system, computer equipment and readable storage medium, wherein the shortwave delay estimation method includes: a parameter initialization step, initializing the initial parameters of the filter, the initial Parameters include: weight coefficient vector W, number of iterations k max and convergence factor μ ;Signal modulo step to obtain the received two-shortwave multipath signal y 1 , y 2 , for two of the shortwave multipath signals y 1 , y 2 After the modulo is taken, the mean square error is obtained; in the step of obtaining the delay estimation result, the weight coefficient vector is iteratively updated based on the principle of minimizing the mean square error, so as to obtain the delay estimation result. Through the present application, the interference of the channel gain function to the signal is overcome, and a more accurate estimation of the relative time delay of the short-wave signal is achieved.

The invention discloses a forced integer precoding method of a visible light communication system. Firstly, a mean square error minimization model is established, then the mean square error minimization model is converted into iterative solution of a non-convex problem, in one iteration, the mean square error minimization model is split to obtain a model for optimizing a transmitting precoding matrix and a model for optimizing a forced integer matrix respectively, the transmitting precoding matrix is solved by using a convex approximation iteration method, a compulsory integer matrix is solvedby using a greedy method, and iteration is stopped when a convergence condition is met, so that the solved precoding matrix and the compulsory integer matrix are final results, otherwise, the next iteration is continued. According to the invention, a smaller mean square error is obtained under the same optical power constraint condition.

The invention discloses a low delay filter design method for sampling rate conversion in an electronic transformer. The method comprises the steps of first adopting cascade connection of an interpolator and an extractor to achieve conversion of random fraction ratio sampling frequency, enabling an anti-image filter in the interpolator and an anti-aliasing filter in the extractor to be combined into a low pass filter; then adopting a mean square error minimization standard to solve a coefficient vector of the filter, enabling a filter transmission band amplitude and a stop band amplitude to serve as constraint conditions, and enabling mean square error minimization to serve as an optimized target; and finally enabling a solving process of the filter coefficient vector based on constraint least square method design to be converted into solving of a positive definite quadratic programming problem so that the filter coefficient vector can be obtained by direct solving. According to the method, the problems that large output delay is caused in the direct linear convolution filtering process and the higher the filter order, the larger the group delay are solved, and rapidity of protection action is greatly improved.

The invention provides a Massive Multiple-Input Multiple-Output (Massive MIMO) pre-coding method, which aims to solve the problems of high error rate and low energy efficiency of a Massive MIMO system in the prior art. The method comprises the following steps: obtaining a mathematical optimization model of the Massive MIMO system; designing a receiving end base band decoding matrix; designing a sending end base band pre-coding matrix; designing a sending end radio frequency pre-coding matrix; and outputting the base band and radio frequency hybrid pre-coding matrixes. The Massive MIMO pre-coding method provided by the invention has the benefits that the base band and radio frequency hybrid pre-coding method adopts the principle of mean square error minimization, and combines the convex optimization method with the Particle Swarm Ant Colony Optimization (PSACO) method, so as to achieve a high iteration rate and obtain the hybrid pre-coding matrixes; compared with the traditional pre-coding method, the method can better reduce the error rate of the system; meanwhile, compared with the full-digital base band pre-coding, the method has the advantages of low hardware implementation cost and high energy efficiency.

The invention discloses a large-scale MIMO low-complexity channel estimation method based on weighted Kapetyn grade number expansion. Firstly a Kapetyn grade number expansion method is utilized for performing approximate expansion on a channel covariance inverse matrix in a Bayesian-MMSE channel estimation expression. A matrix inversion operation is converted to matrix multiplication and matrix addition operations. Then a weighting manner is performed on each coefficient of a polynomial for optimizing polynomial expansion, establishing a model for solving weighting coefficient vectors alpha and beta for minimizing an estimated mean square error, and estimating the channel matrix by means of solving results of alpha and beta. Experiment results represent a fact that an MSE which is obtained through the channel estimation method based on a weighted Kapetyn grade number expansion is convergent to an MMSE method along with order number increase of the polynomial, and furthermore calculation complexity of the channel estimation method is lower than that of the MMSE method. Compared with a traditional Taylor-MMSE and Kapetyn grade number expansion channel estimation method, the channel estimation method based on the weighted Kapetyn grade number has higher convergence speed to the MMSE method.

The invention discloses a FIR filter design method based on the particle swarm algorithm. The unit impulse response of the FIR filter is used as the particle position vector of the particle swarm algorithm, and the signal-to-noise ratio of the filtered signal based on the FIR filter and the FIR filter The fitness function of the particle swarm algorithm is designed by using the mean square error of the filter, and the FIR filter is obtained iteratively. The invention designs the fitness function of the particle swarm algorithm based on the criterion of minimizing the signal-to-noise ratio and the mean square error, adopts the particle swarm algorithm to design the FIR filter, and obtains the FIR filter taking into account the performance of the signal-to-noise ratio.

The invention discloses a method for estimating 2x mode channel coefficients of 802.11ax protocol. Based on the linear interpolation method, the method adopts a feedback method to correct unknown channel coefficients in 2x mode, so as to achieve the mean square error of the estimated coefficient values. minimize. Compared with the linear interpolation / polynomial interpolation algorithm, the method improves the accuracy of channel estimation in the 11ax system through multiple iterations, and better meets the requirements of signal reception.

The invention discloses a low-complexity bit loading method for an OFDM-based PLC system, comprising the following steps: first, the sending end determines the used subcarrier set, the total transmission power constraint value, and the maximum transmission power constraint value allowed by each subcarrier , introducing weights, the continuous throughput optimization problem is equivalent to the weighted mean square error minimization problem, and the problem is solved iteratively by combining the block coordinate descent algorithm and the dichotomy method, and then the obtained non-integer subcarrier bit number is calculated Integerization, on this basis, and then use the greedy idea to iteratively obtain the integer bit and power allocation scheme, and finally the sending end maps the signal bit stream to each subcarrier according to the allocation scheme and sets the transmission power of the subcarrier to realize the PLC system Service transmission at the sending and receiving ends. The method of the invention can not only optimize the system performance but also greatly reduce the number of iterations of the bit loading problem, thereby reducing the computational complexity of the system.

The invention discloses a forced integer precoding method of a visible light communication system. Firstly, the mean square error minimization model is established, and then the mean square error minimization model is transformed into an iterative solution to non-convex problems. In one iteration, the mean square error minimization model is split first, and the optimized transmission precoding matrix is ​​obtained respectively. The model of the model and the model of optimizing the forced integer matrix, and then use the convex approximation iterative method to solve the transmission precoding matrix, and use the greedy method to solve the forced integer matrix. When the convergence condition is satisfied, the iteration terminates, and the obtained precoding matrix and forced integer matrix The number matrix is ​​the final result, otherwise continue to the next iteration. The present invention obtains a smaller mean square error under the same optical power constraint condition.