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41 results about "Normalized mean square error" patented technology
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The NMSE (Normalised Mean Square Error) is an estimator of the overall deviations between predicted and measured values. It is defined as: Contrary to the bias, in the NMSE the deviations (absolute values) are summed instead of the differences.
Method for estimating signal-to-noise ratio of time-frequency overlapped signals in cognitive radio
ActiveCN105978641AGood signal to noise ratioImprove estimation performanceTransmission monitoringFrequency spectrumSignal-to-noise ratio (imaging)
The invention discloses a method for estimating the signal-to-noise ratio of time-frequency overlapped signals in cognitive radio. The method includes the following steps that: a normalized higher-order cumulant equation is constructed according to the normalized higher-order cumulant of received signals; all component signal modulation type combinations are traversed, the power of component signals is calculated through the normalized higher-order cumulant equation, and whether the modulation type combinations are correct is judged; and correct component signal modulation type combinations and power are obtained, the noise power of the time-frequency overlapped signals is calculated, so that the signal-to-noise ratio of the time-frequency overlapped signals in the underlay cognitive radio can be estimated. According to method, the normalized mean square error of the estimation of the signal-to-noise ratio is smaller than 0.2 under a high spectrum overlap rate when a signal-to-noise ratio is 0dB. The method has excellent performance in the estimation of the signal-to-noise ratio of the time-frequency overlapped signals in the underlay cognitive radio. With the method of the invention adopted, the measurement of interference temperature can be facilitated, primary users and secondary users can coexist under the interference temperature, and therefore, spectrum efficiency can be improved.
Owner:南京云麒信通智慧科技有限公司
DOA estimation method for moving target echoes under multiple external radiation sources
InactiveCN110967665ARadio wave finder detailsRadio wave direction/deviation determination systemsAlgorithmAutoencoder
The invention belongs to the technical field of communication technology and signal processing, and discloses a DOA estimation method for moving target echoes under multiple external radiation sources, and the method comprises the steps: carrying out the preprocessing of a mixed echo signal received by an antenna array, solving a covariance matrix of the signal, extracting a real part and an imaginary part of an upper triangular element, and constructing a one-dimensional matrix as the input of a sparse auto-encoder; classifying the signals from different regions by using a sparse auto-encoder; p results output by the sparse auto-encoder forming a one-dimensional matrix, then converting the one-dimensional matrix into a covariance matrix form, and dividing the matrix into a real part matrix and an imaginary part matrix to serve as dual-channel input to be sent to P convolutional neural networks; realizing DOA estimation of different subarea signals by using the convolutional neural network, and output layer neurons of the P convolutional neural networks representing the angles of the P sub-regions in the horizontal direction; and when the signal-to-noise ratio is greater than 0dB,the normalized mean square error of signal-to-noise ratio estimation being less than 1.
Owner:XIDIAN UNIV
Hierarchical cooperative combined spectrum sensing algorithm
InactiveCN102638802AReduce Spectrum Sensing OverheadNetwork planningFrequency spectrumData information
The invention relates to the field of cognitive radio communication and provides a multi-layer distributed combined spectrum sensing algorithm based on a Dirichlet process so as to realize dynamic spectrum sensing. The sensing data acquired by secondary users in a plurality of hierarchical centers is fused to search the optimized sensing information. The automatic data packet is realized by employing the Dirichlet process, a shared hyper-parameter and a corresponding divergent probability in each packet are estimated by a bayesian model, the hyper-parameter is acquired by employing a standard Viterbi algorithm, and the hyper-parameter is compared with a decision threshold value to acquire a final spectrum decision result so as to determine whether a channel is available. Due to the design, the space diversity information of the compressed sensing data is fully considered, and the uncertainty of an individual secondary user on the compressed sensing data is reduced, so that the normalized mean squared error performance is high, the compressed sensing data information in each hierarchical center can be effectively obtained through the algorithm, high accurate detection probability and low false alarm probability are acquired, and the spectrum sensing performance is improved.
Owner:HARBIN INST OF TECH
Distributed compressive sensing sparsity adaptive reestablishment method
InactiveCN108418769AImprove performanceShort runtimeBaseband system detailsCode conversionNormalized mean square errorRunning time
The invention discloses a distributed compressive sensing (DCS) improved sparsity adaptive matching pursuit (DSC-Improved Sparsity Adaptive Matching Pursuit, DCS-IMSAMP) reestablishment method. The estimation precision is improved by utilizing the joint sparsity of the signal and importing a dynamic threshold on the basis of the existing DSC sparsity adaptive matching pursuit (DCS-SAMP) algorithm;and the running time is saved by combining the clipping technology and the variable step length. The adaptivity can be ensured in the reestablishment process by using the algorithm, and the lower normalized mean square error (NMSE) and faster running speed can be acquired. The algorithm disclosed by the invention is applied to a channel estimation problem; compared with existing other algorithms,the excellent channel estimation effect can be obtained.
Owner:NANJING UNIV OF POSTS & TELECOMM
Combined cooperative multi-satellite weak echo signal time delay and doppler frequency shift estimation method
ActiveCN106597407AImprove estimation performanceWave based measurement systemsPeak valueSignal-to-quantization-noise ratio
The invention discloses a combined cooperative multi-satellite weak echo signal time delay and doppler frequency shift estimation method. According to the method, multiple nonstop wave signals in a reference channel are separated; adaptive inhibition for nonstop wave signals and multi-path signals in an echo channel is carried out; fourth-order cyclic cumulant mutual fuzzy function processing based on four weighted fractional Fourier transform for the signals in the echo channel and the different nonstop wave signals is respectively carried out to acquire multiple characteristic vectors; after spectral peak extraction for fuzzy function peak values is carried out, corresponding coordinates are time delay and doppler frequency shift estimates; the multiple sets of time delay and doppler frequency shift estimates are converted into distances from targets to receivers and speeds, data fusion is further carried out, the distances and the speed values are converted into multiple sets of time delay and doppler frequency shift estimates after data fusion, and thereby time delay and doppler frequency shift of cooperative multi-satellite weak echo signals can be estimated; when a signal to noise ratio is greater than 10dB, a normalization mean square error of signal to noise ratio estimation is smaller than 1.
Owner:XIDIAN UNIV +1
Hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior
The invention discloses a hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior. The hyperspectral unmixing compressive sensing method is used for solving the technical problem that an existing hyperspectral image compressive sensing algorithm in combination with spectrum unmixing is low in precision. According to the technical scheme, a random observation matrix is adopted for extracting a small number of samples from original data as compression data. In the reconstruction process, according to an unmixing compressive sensing model, appropriate spectrums are selected from a spectrum library as an end member matrix in the model, then the three-dimensional total variation sparse prior of an abundance value matrix is introduced, and the abundance value matrix is accurately solved through solving a limited linear optimization problem. Finally, a linear mixing model is used for reconstructing the original data. When the compression ratio of urban data shot through a HYICE satellite is 1:20, the normalize mean squared error (NMSE) is smaller than 0.09, when the compression ratio is 1:10,the NMSE is smaller than 0.08, and compared with an existing compressive sensing algorithm, precision is promoted by more than 10%.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Millimeter wave channel estimation method based on compressed sensing in height moving scene
ActiveCN110519189AGood refactoringReduce the impact of cumulative errorsTransmission monitoringRadio transmissionPattern recognitionSpatial correlation
The invention provides a millimeter wave channel estimation method based on compressed sensing in a high-speed moving scene. The method comprises the following steps: step 1, analyzing downlink communication in a high-speed moving scene, considering the influence of Doppler frequency shift, carrying out channel modeling based on the low-rank characteristic and the spatial correlation characteristic of millimeter wave communication, and writing into a matrix form; step 2, after the channel matrix in the step 1 is quantized, designing the channel matrix shows sparse characteristics, and a perception matrix and a measurement matrix; and step 3, performing vectorization processing on the channel matrix presenting the sparse characteristic in the step 2, reconstructing the vectorized channel model based on a compressed sensing algorithm and the sensing matrix and the measurement matrix in the step 2, and calculating a normalized mean square error. The calculation cost is reduced, and the performance is improved.
Owner:SOUTHEAST UNIV
Method for predicating long correlation sequences by utilizing short correlation model
InactiveCN102891770AReduce complexityImprove forecast accuracyData switching networksDecompositionNetwork model
The invention relates to a method for predicating long correlation sequences by utilizing a short correlation model. Aiming at self-similarity network flow, the invention provides an ARMA (autoregressive moving average model) self-similarity sequence predicating method based on EMD (empirical mode decomposition). The method comprises the following steps of: firstly decomposing the self-similarity network flow into a plurality of IMFs (Intrinsic Mode Functions) by adopting the EMD method, wherein due to the narrow-band characteristic of the IMF, the IMF is provided to be a short correlation sequence, so that the problem of modeling predication of the long correction sequences is converted into the modeling and predicating for the plurality of short correlation sequences, and the complexity of the model is effectively reduced; secondly predicating the decomposed IMF sequences by utilizing excellent short correlation modeling predication capacity of an ARMA model; and finally providing a method for improving the predication precision of the model, so as to effectively reducing the normalization error of mean square of the predication result. The method provided by the technical scheme of the invention has the advantages of high predication precision and low complexity, and the predication precision of self-similarity flow is higher than that of a neural network model.
Owner:HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
LFM signal modulation parameter estimation method under Alpha stable distribution noise
ActiveCN107800659AImprove estimation performanceImpulse noise suppressionFrequency-modulated carrier systemsPilot signal allocationTime–frequency analysisChirplet transform
The invention belongs to the technical field of non-stationary signal modulation and analysis, and discloses an LFM signal modulation parameter estimation method under alpha stable distribution noise.The method comprises the following steps of carrying out generalized extension linear chirplet transform on a received LFM signal to obtain a time-frequency analysis image; carrying out Radon transform on the time-frequency analysis image, calculating a maximum value of the time-frequency analysis image, and estimating an angle estimation frequency modulation slope corresponding to the maximum value; and constructing a demodulation reference signal by utilizing the frequency modulation slope, multiplying the demodulation reference signal by an original signal to obtain a demodulation signal,carrying out generalized Fourier transform on the demodulation signal, and estimating an initial frequency by utilizing the position of the maximum value. When a generalized signal-to-noise ratio is larger than 0dB, a normalized mean square error of the frequency modulation slope estimation of the LFM signal is smaller than -33dB; and when the generalized signal-to-noise ratio is larger than -6dB,the normalized mean square error of the initial frequency estimation of the LFM signal is less than or equal to -22.4dB.
Owner:XIDIAN UNIV +1
Broadband collaboration spectrum sensing method
ActiveCN105119670AImprove detection rateIncrease the normalized mean square errorTransmission monitoringFrequency spectrumNormalized mean square error
The invention discloses a broadband collaboration spectrum sensing method, which utilizes the block sparse structure of the broadband signal, and utilizes fast marginal likelihood function maximization to perform fast parameter estimation, so as to improve the detection probability, reduce the normalized mean squared error and decrease the detection time consumption of the conventional algorithm. Moreover, a frequency diversity effect is achieved among the nodes of the multi-node wideband cooperative spectrum sensing algorithm, so as to overcome the disadvantages of low detection accuracy and poor real-time performance caused by single node detection.
Owner:GUILIN UNIV OF ELECTRONIC TECH
Behavior level modeling and verification method of power amplifier underlying circuit
InactiveCN106126804ASolve complex problems with direct simulationCAD circuit designSpecial data processing applicationsValidation methodsNormalized mean square error
The invention relates to a behavior level modeling and verification method of a power amplifier underlying circuit. The method comprises the following steps: S1: establishing a power amplifier underlying circuit model, and collecting the input signal and the output signal of a power amplifier as experimental data; S2: establishing a behavioral model based on a mathematical expression, and utilizing the experimental data for distinguishing the behavioral model; S3: establishing the same input signal exciting circuit, and calculating a normalized mean square error between the output signal of the power amplifier underlying circuit model and the output signal of the behavioral model; and S4: judging whether the normalized mean square error meets accuracy requirements or not, verifying the accurate description of the input / output characteristics of the power amplifier underlying circuit by the behavioral model, and regulating the behavioral model again until the normalized mean square error meets the accuracy requirements if the normalized mean square error does not meet the accuracy requirements. By use of the method, the problem that the power amplifier underlying circuit is complex in direct simulation solving is solved, the method can be applied to a simulation situation that the detail information of the underlying circuit is not completely known due to technical security, and an effective means is provided for the system-level electromagnetic compatibility analysis of a simulation and digital mixing circuit.
Owner:SHANGHAI RADIO EQUIP RES INST
Combined channel and carrier frequency offset estimation method for millimeter wave MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system
ActiveCN110022274AHigh precisionAvoid base mismatch problemChannel estimationMulti-frequency code systemsCommunications systemChannel parameter
The invention relates to the field of wireless communication systems, in particular to a combined channel and carrier frequency offset estimation method for a millimeter wave MIMO-OFDM (Multiple InputMultiple Output-Orthogonal Frequency Division Multiplexing) system. The method mainly comprises the following steps of: S1, converting a combined channel and carrier frequency offset matrix into a form which conforms to the atomic expression of a constructed two-dimensional sequence and is shown in the specification; S2, estimating a joint channel and carrier frequency offset matrix shown in thespecification by adopting a sparse signal reconstruction algorithm based on sequence atom norm minimization; S3, giving an estimated value shown in the specification, based on compressibility of carrier frequency offset, decomposing to obtain a transceiving direction, channel gain and carrier frequency offset component of the channel, and then directly obtaining a channel matrix by utilizing the estimated channel transceiving direction and channel gain; and S4, performing performance evaluation on the estimated channel matrix and the carrier frequency offset vector by adopting a normalized mean square error evaluation standard. According to the method, the sparsity of channel parameters can be highly and closely approximated in a continuous angle space domain, the base error matching problem is avoided, and then the channel estimation accuracy is improved.
Owner:SHENZHEN INST OF ADVANCED TECH
Multi-antenna system channel estimation method based on deep learning
ActiveCN113381952AImprove estimation accuracyHigh gain performanceBaseband system detailsRadio transmissionAlgorithmGenerative adversarial network
The invention discloses a multi-antenna system channel estimation method based on deep learning, which is suitable for estimating an uplink multipath channel and is realized based on a conditional generative adversarial network, wherein the conditional generative adversarial network comprises a generator and a discriminator based on a deep learning network. The method comprises an offline training part and an online testing part: the offline training part comprises the steps: generating a training sample according to a real channel measurement value, and then acquiring an estimated channel corresponding to the training sample by utilizing a generator; enabling a discriminator to obtain the discrimination output, and calculating a loss function to update network parameters of the discriminator and the generator; after loop iteration is completed, storing the trained generator neural network at the base station; and an online test stage comprises the steps: inputting the quantized pilot signal and the original pilot signal into the trained generator to obtain estimated channels from a user to all antennas. Compared with the prior art, the normalized mean square error of estimation can be effectively reduced.
Owner:SOUTHEAST UNIV +1
Wireless communication channel estimation method and device
ActiveCN114124623AEfficient estimationReduce complexityBaseband system detailsHigh level techniquesStochastic gradient descentNormalized mean square error
The invention discloses a wireless communication channel estimation method and device, and the method comprises the steps: receiving a pilot signal sent by a user side, and converting the pilot signal into a two-dimensional image to obtain a channel matrix; a residual dense network channel estimation model is established, model network parameters are initialized, the channel matrix serves as an input signal, the noise estimation matrix serves as an output signal, model training is carried out, and the residual dense network channel estimation model is formed by cascading an RDN structure and a CBAM structure; calculating a loss function of the residual dense network channel estimation model through forward transmission; and calculating updated network parameters for the loss function through a chain rule according to a stochastic gradient descent algorithm, updating the residual dense network channel estimation model by using the updated network parameters until the normalized mean square error meets a convergence condition, and recording the current residual dense network channel estimation model and model network parameters. The device is simple in structure and high in applicability.
Owner:CHINA ACADEMY OF INFORMATION & COMM
Band-limited system timing recovery method based on p-moment
ActiveCN105024799AImprove spectrum utilizationIncreased detection gainBaseband system detailsRadio transmissionRecovery methodFrequency spectrum
The invention relates to a band-limited system timing recovery method based on p-moment. In a feedback phase-locked loop, the pth power of a signal module value is adopted for calculating a timing-error detection output value, and p>0 and p is a variable parameter. The method of the invention can be applied to a timing recovery loop, better residual timing error jitter performance can be acquired, and in an AWGN channel, normalized mean square error performance through timing recovery is close to a theoretical Cramer-Rao bound. The method of the invention can be applied to a band-limited communication system with a small roll-off factor, and can be applied to a satellite communication system which needs to improve the spectrum utilization ratio.
Owner:BEIJING RES INST OF TELEMETRY +1
Non-Gaussian noise 3D-MIMO channel estimation algorithm
ActiveCN108199990ASolve the estimation problemRadio transmissionChannel estimationSignal-to-noise ratio (imaging)Mean square
The invention discloses a non-Gaussian noise 3D-MIMO channel estimation algorithm, which comprises the following steps of obtaining a support set of a channel matrix by utilizing a judgment condition,selecting a dictionary matrix under the support set, and carrying out order selection calculation of a Gaussian mixture model according to the characteristics of a received signal; computing a weightleast square matrix; obtaining a coefficient and a variance of the Gaussian mixture model; estimating the channel matrix column by column in order to obtain an estimated value of the first time; judging whether an iteration result tends to be stable or reaches the number of iterations, and obtaining a channel matrix under the support set; otherwise repeating the steps until the condition is met;and generating a full-zero matrix when the iteration result is met, inserting the channel matrix under the support set into the all-zero matrix line by line according to positions where non-zero elements in the support set are located, making the rest of positions unchanged, and obtaining an actual channel matrix. A normalization mean square error of the estimation algorithm is obviously superiorto that of other algorithms, and an ideal estimation performance is still achieved under a condition that a signal-to-noise ratio is low.
Owner:XI AN JIAOTONG UNIV
Uncalibrated airborne SAR image sea peak suppression method based on optimal polarization ratio
ActiveCN112529817AIncrease profitSuppression of high-frequency radar sea spikesImage enhancementImage analysisOriginal dataNormalized mean square error
The invention discloses an uncalibrated airborne SAR image sea peak suppression method based on an optimal polarization ratio. The method comprises the following steps: generating and storing a single-view complex image according to original data of a region of interest received by an SAR; selecting the single-view complex image ocean background sub-blocks, and performing azimuth multi-view processing to obtain dual-polarization intensity data of the background sub-blocks; processing the background sub-blocks, traversing the theoretical value of the polarization ratio, and calculating and obtaining a plurality of polarization difference images in combination with the dual polarization intensity data of the background sub-blocks; calculating a normalized mean square error between each polarization difference image and the HH polarization SAR image, taking a polarization ratio theoretical value corresponding to the maximum normalized mean square error, and defining the polarization ratiotheoretical value as an optimal polarization ratio; and correcting the HH polarization intensity according to the ratio of the optimal polarization ratio to the mean value of the region-of-interest data, and calculating and obtaining a polarization difference image, thereby achieving sea peak suppression.
Owner:AEROSPACE INFORMATION RES INST CAS
Frequency domain serial correlation channel measurement method based on hypersonic scene
ActiveCN106850099AHigh precisionImplement extractionTransmission monitoringTime domainChannel parameter
The invention provides a frequency domain serial correlation channel measurement method based on a hypersonic scene, for solving the technical problem of low channel parameter measurement accuracy caused by a limited channel measurement bandwidth in the hypersonic scene of the existing time domain serial correlation channel measurement method. The method comprises the following steps: selecting channel measurement parameters, and generating a frequency domain measurement sequence; performing inverse Fourier transform on the measurement sequence to generate a time domain measurement sequence; selecting a hypersonic channel frequency offset, performing Fourier transform to obtain a hypersonic channel frequency domain parameter, and performing hypersonic channel fading and noise pollution on the time domain measurement sequence to obtain time domain channel measurement data; performing Fourier transform on the time domain channel measurement data to obtain channel measurement frequency domain data, and performing sliding correlation processing on the channel measurement frequency domain data and the frequency domain measurement sequence to obtain channel frequency domain impulse response; and comparing the channel frequency domain impulse response with the hypersonic channel frequency domain parameter to obtain a normalized mean square error of channel measurement.
Owner:XIDIAN UNIV
Pilot allocation method based on positioning information
ActiveCN108900290AGood average performanceAverage Normalized Mean Squared Error GoodRadio transmissionPilot signal allocationCommunications systemNormalized mean square error
The invention discloses a pilot allocation method based on positioning information, belongs to the technical field of communication, and in particular relates to a pilot allocation method based on positioning information. The pilot allocation method is based on user location information and base station location information, and has better user average performance (average normalized mean square error of a channel) of an entire massive MIMO system than a method in the prior art. In addition, the pilot allocation technology is also greatly improved in terms of fairness among users of the entirecommunication system.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
A millimeter wave MIMO channel estimation method based on a MUSIC algorithm and precoding
The invention discloses a millimeter wave MIMO channel estimation method based on a MUSIC algorithm and precoding, and the method comprehensively considers the influence of azimuth angle and pitch angle parameters, employs an innovative scheme, and employs a classical MUSIC method to estimate the CSI in an L-shaped array and a UPA. Under the same condition, the channel estimation performance basedon the L-shaped array is superior to that of the UPA. Because there are few methods for researching two-dimensional channel estimation, a channel estimation scheme based on beam training and a channel estimation scheme based on OMP are adopted in the ULA system to carry out performance comparison. Due to the fact that estimation errors of the azimuth angle and the elevation angle can cause normalization mean square errors, under the same condition, the NMSE performance of the UPAs should be higher than that of the ULA. Simulation results show that the proposed method can effectively estimatethe channel state information in the two arrays, even better than the prior art.
Owner:COMMUNICATION UNIVERSITY OF CHINA
Channel estimation method based on improved GAN network in large-scale MIMO
PendingCN114268388AReduce power consumptionAddressing Hardware ComplexityTransmission monitoringNeural architecturesMulti inputMean square
The invention relates to a channel estimation algorithm based on an improved GAN (Generative Adversarial Network) in a large-scale MIMO (Multiple Input Multiple Output) system, and provides a channel estimation method adopting an improved GAN (Generative Adversarial Network) in order to improve the performance when channel estimation based on deep learning is carried out in a one-bit uplink multi-user large-scale MIMO system. In the method, a random quantization method is introduced to improve the input of the GAN network, so that the input data is more real; penalty terms are respectively introduced into a generator and a discriminator to generate a new optimization objective function, so that the network optimization direction is correct, and a network structure is determined through model simulation. The GAN network learns non-trivial mapping from quantitative measurement values to channels by using priori channel estimation observation values; and adversarial training is carried out on the generator and the discriminator to predict a more real channel. A numerical simulation result shows that the method obviously improves the channel estimation accuracy of the large-scale multi-input and multi-output system from the angle of a normalized mean square error (NMSE), and the channel estimation accuracy of the large-scale multi-input and multi-output system is improved by the aid of the method in the aspect of the NMSE (Normalized Mean Squared Error) of the large-scale multi-input and multi-output system.
Owner:NANJING UNIV OF POSTS & TELECOMM
Short-wave digital pre-distortion method based on non-uniform quantization lookup table
ActiveCN111901264AHigh precisionImprove performanceSynchronous/start-stop systemsMean squareAlgorithm
The invention discloses a short-wave digital pre-distortion method based on a non-uniform quantization lookup table, and the method comprises the steps: selecting a pre-distortion structure, collecting a current pre-distortion signal and a feedback signal, and carrying out the synchronous alignment of the currently collected signals; calculating a normalized mean square error of the current inputsignal and the pre-distortion output signal; judging whether the currently calculated normalized mean square error is greater than a preset target value or not, if so, calculating a pre-distortion coefficient by adopting a least square method, updating the lookup table according to the pre-distortion coefficient, and performing pre-distortion on the next round of pre-distortion by adopting the updated lookup table; otherwise, adopting the current lookup table to perform pre-distortion in the next round. According to the method, the memory polynomial model of the non-uniform quantization tableindex is adopted, so that the table index precision is improved, and the performance of a digital pre-distortion system is further improved.
Owner:西安烽火电子科技有限责任公司
Orthogonal signal division multiplexing equalization method based on diagonal block strip matrix enhancement
ActiveCN110691048AImprove performanceImproved normalized mean square error performanceMulti-frequency code systemsTransmitter/receiver shaping networksMultiplexingDoppler spread
The invention provides an orthogonal signal division multiplexing equalization method based on diagonal block strip matrix enhancement. The method includes: adopting a time-varying channel OSDM systemmodel to obtain a received signal; completing DBB approximation of a composite channel matrix in the received signal based on Doppler expansion of band limit; performing enhancement processing on a DBB approximate structure in the received signal based on a time domain receiving window function; and desiging a low-complexity OSDM equalization algorithm based on a DBB matrix enhancement method ina received signal. According to the method, the normalized mean square error performance of the system is improved, the error code performance of the system is improved, the performance loss caused bythe Doppler effect is effectively reduced, the performance of the communication system is remarkably improved, the calculated amount is greatly reduced, and the method has a good application prospect.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Method for processing vibration signal on surface of oil tank of power transformer
InactiveCN110132401ASimplify vibration sourceSimplify the delivery processSubsonic/sonic/ultrasonic wave measurementUsing electrical meansTransformerSignal on
The invention relates to a method for processing a vibration signal on the surface of an oil tank of a power transformer. The method comprises the steps of a) firstly collecting the vibration signal on the surface of the oil tank of the power transformer; b) selecting vibrations x1, j(n) and x2 at a measuring point j on the wall of the oil tank under an approximate voltage condition and differentload conditions, and taking the j(n) as a signal separation object; c) using a time-frequency ratio hybrid blind source separation algorithm for the signal, estimating a hybrid matrix, and obtaining aseparated signal; and d) calculating the separated signal to be compared with the vibration in a no-load state of the measuring point, and calculating a normalized mean square error (NMSE) between the separated signal and the vibration of the measuring point in the no-load state. According to the method, the initial judgment can be provided for equipment maintenance, so that the occurrence rate of major accidents is reduced, the maintenance quantity and the maintenance cost of equipment are reduced, and the normal operation of the power transformer is ensured.
Owner:STATE GRID CORP OF CHINA +2
A Non-Gaussian Noise 3d-mimo Channel Estimation Method
ActiveCN108199990BSolve the estimation problemRadio transmissionChannel estimationMean squareSignal-to-noise ratio (imaging)
The invention discloses a non-Gaussian noise 3D-MIMO channel estimation algorithm, which comprises the following steps of obtaining a support set of a channel matrix by utilizing a judgment condition,selecting a dictionary matrix under the support set, and carrying out order selection calculation of a Gaussian mixture model according to the characteristics of a received signal; computing a weightleast square matrix; obtaining a coefficient and a variance of the Gaussian mixture model; estimating the channel matrix column by column in order to obtain an estimated value of the first time; judging whether an iteration result tends to be stable or reaches the number of iterations, and obtaining a channel matrix under the support set; otherwise repeating the steps until the condition is met;and generating a full-zero matrix when the iteration result is met, inserting the channel matrix under the support set into the all-zero matrix line by line according to positions where non-zero elements in the support set are located, making the rest of positions unchanged, and obtaining an actual channel matrix. A normalization mean square error of the estimation algorithm is obviously superiorto that of other algorithms, and an ideal estimation performance is still achieved under a condition that a signal-to-noise ratio is low.
Owner:XI AN JIAOTONG UNIV
Channel estimation method for multi-antenna system based on deep learning
ActiveCN113381952BImprove estimation accuracyHigh gain performanceBaseband system detailsRadio transmissionAlgorithmGenerative adversarial network
The invention discloses a multi-antenna system channel estimation method based on deep learning, which is suitable for estimating uplink multipath channels. The invention is realized based on a conditional generation confrontation network. The condition generation confrontation network includes two parts: a generator and a discriminator based on a deep learning network. The method includes two parts: offline training and online testing: offline training first generates training samples based on real channel measurements, and then uses the generator to obtain the estimated channel corresponding to the training samples; secondly, the discriminator obtains the discriminant output, and calculates the loss function to update the discriminator and The network parameters of the generator; after the loop iteration is completed, the trained generator neural network is stored in the base station; in the online test phase, the quantized pilot signal and the original pilot signal are input into the trained generator, and the user to all The estimated channel of the antenna. Compared with the prior art, the estimated normalized mean square error can be effectively reduced.
Owner:SOUTHEAST UNIV +1
A timing recovery method for band-limited systems based on p-order moments
ActiveCN105024799BImprove spectrum utilizationIncreased detection gainBaseband system detailsRadio transmissionRecovery methodCommunications system
The invention relates to a timing recovery method for a band-limited system based on p-order moments. In the method, in a feedback phase-locked loop, the p-th power of the signal modulus is used to calculate the timing error detection output value, wherein p>0 is a variable parameter , the method of the present invention can be applied in a timing recovery loop to obtain better residual timing error jitter performance, and the normalized mean square error performance of the timing recovery of the method of the present invention is close to the theoretical Cramereau limit under the AWGN channel. The method of the invention is suitable for a bandwidth-limited communication system with a small roll-off factor, and is suitable for a satellite communication system that needs to increase spectrum utilization.
Owner:BEIJING RES INST OF TELEMETRY +1
Hierarchical cooperative combined spectrum sensing algorithm
InactiveCN102638802BReduce Spectrum Sensing OverheadData switching by path configurationNetwork planningFrequency spectrumData information
The present invention relates to the field of cognitive radio communication. The present invention provides a multi-layer distributed joint spectrum sensing method based on the Dirichlet process to realize dynamic spectrum sensing, and finds optimal sensing information by fusing sensing data collected by secondary users of multiple hierarchical centers. The Dirichlet process is used to realize the automatic grouping of data. The Bayesian model estimates a shared hyperparameter and the corresponding divergence probability in each group. The standard Viterbi algorithm is used to obtain the hyperparameters, and the hyperparameters are compared with the decision threshold. The comparison is performed to obtain the final spectrum decision result to determine whether the channel is available. The design fully considers the spatial diversity information of compressed sensing data, which reduces the uncertainty of a single secondary user on compressed sensing data, so that the normalized mean square error performance is better, and the algorithm can effectively mine the compressed sensing data information of each hierarchical center , to obtain a higher probability of correct detection and a smaller probability of false alarm, and improve the performance of spectrum sensing.
Owner:HARBIN INST OF TECH
Joint Estimation Method of Weak Echo Signal Delay and Doppler Shift under Multi-satellite Coordination
ActiveCN106597407BImprove estimation performanceWave based measurement systemsPeak valueSignal-to-quantization-noise ratio
Owner:XIDIAN UNIV +1
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