Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmissio...Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.展开更多
Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri...Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.展开更多
Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative ad...Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.展开更多
Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effec...Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effective solution for channel estimation in wireless communication system,spe-cifically in different environments is Deep Learning(DL)method.This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder(CNNAE)classifier for MIMO-OFDM systems.A CNNAE classi-fier is one among Deep Learning(DL)algorithm,in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another.Improved performances are achieved by using CNNAE based channel estimation,in which extension is done for channel selection as well as achieve enhanced performances numerically,when compared with conventional estimators in quite a lot of scenar-ios.Considering reduction in number of parameters involved and re-usability of weights,CNNAE based channel estimation is quite suitable and properlyfits to the video signal.CNNAE classifier weights updation are done with minimized Sig-nal to Noise Ratio(SNR),Bit Error Rate(BER)and Mean Square Error(MSE).展开更多
In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrins...In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrinsic messages,it is concluded that the Probabilistic Data Association(PDA) algorithm is equivalent to the Soft Interference Cancellation plus Minimum Mean Square Error algo-rithm(SIC-MMSE) .展开更多
A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE...A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.展开更多
In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic su...In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.展开更多
The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and sym...The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.展开更多
The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding ...The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding algorithm without matrix inversion is proposed, by which the computational complexity can be reduced directly but the decoding performance is not affected.展开更多
In this paper, the effect of imperfect channel state information at the receiver, which is caused by noise and other interference, on the multi-access channel capacity is analysed through a statistical-mechanical appr...In this paper, the effect of imperfect channel state information at the receiver, which is caused by noise and other interference, on the multi-access channel capacity is analysed through a statistical-mechanical approach. Replica analyses focus on analytically studying how the minimum mean square error （MMSE） channel estimation error appears in a multiuser channel capacity formula. And the relevant mathematical expressions are derived. At the same time, numerical simulation results are demonstrated to validate the Replica analyses. The simulation results show how the system parameters, such as channel estimation error, system load and signal-to-noise ratio, affect the channel capacity.展开更多
In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recove...In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data.In wavelet domain,if Bayesian estimator is used for denoising problem,the solution requires a prior knowledge about the distribution of wavelet coeffcients.Indeed,wavelet coeffcients might be better modeled by super Gaussian density.The super Gaussian density can be generated by Gaussian scale mixture(GSM).So,we present new minimum mean square error(MMSE)estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise(AWGN).We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.展开更多
The channel estimation technique is investigated in OFDM communication systems with multi-antenna Amplify-and-Forward(AF) relay.The Space-Time Block Code(STBC) is applied at the transmitter of the relay to obtain dive...The channel estimation technique is investigated in OFDM communication systems with multi-antenna Amplify-and-Forward(AF) relay.The Space-Time Block Code(STBC) is applied at the transmitter of the relay to obtain diversity gain.According to the transmission characteristics of OFDM symbols on multiple antennas,a pilot-aided Linear Minimum Mean-Square-Error(LMMSE) channel estimation algorithm with low complexity is designed.Simulation results show that,the proposed LMMSE estimator outperforms least-square estimator and approaches the optimal estimator without error in the performance of Symbol Error Ratio(SER) under several modulation modes,and has a good estimation effect in the realistic relay communication scenario.展开更多
最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩...最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩阵分块思想和理查德森(Richardson,RI)算法,提出了一种预处理的理查德森(Pretreatment-Richardson,P-RI)迭代算法,该算法首先基于矩阵分块思想构造了一种新形式的线性迭代,然后用此线性迭代对理查德森算法进行预处理,有效提升了算法的收敛速度。实验结果显示,与现有的RI算法相比,该算法的检测性能更好。展开更多
基金supported by the 2011 China Aerospace Science and Technology Foundationthe Certain Ministry Foundation under Grant No.20212HK03010
文摘Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.
基金This work is supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0467)Jiangsu Province,China.During the revision of this paper,the author is supported by China Scholarship Council(No.201906840021)China to continue some research related to data processing.
文摘Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
文摘Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.
文摘Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effective solution for channel estimation in wireless communication system,spe-cifically in different environments is Deep Learning(DL)method.This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder(CNNAE)classifier for MIMO-OFDM systems.A CNNAE classi-fier is one among Deep Learning(DL)algorithm,in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another.Improved performances are achieved by using CNNAE based channel estimation,in which extension is done for channel selection as well as achieve enhanced performances numerically,when compared with conventional estimators in quite a lot of scenar-ios.Considering reduction in number of parameters involved and re-usability of weights,CNNAE based channel estimation is quite suitable and properlyfits to the video signal.CNNAE classifier weights updation are done with minimized Sig-nal to Noise Ratio(SNR),Bit Error Rate(BER)and Mean Square Error(MSE).
文摘In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrinsic messages,it is concluded that the Probabilistic Data Association(PDA) algorithm is equivalent to the Soft Interference Cancellation plus Minimum Mean Square Error algo-rithm(SIC-MMSE) .
基金Supported by the National Natural Science Foundation of China （No. 61001105）, the National Science and Technology Major Projects （No. 2011ZX03001- 007- 03） and Beijing Natural Science Foundation （No. 4102043）.
文摘A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.
基金supported by the National Natural Science Foundation of China (6096200161071088)+2 种基金the Natural Science Foundation of Fujian Province of China (2012J05119)the Fundamental Research Funds for the Central Universities (11QZR02)the Research Fund of Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (21104)
文摘In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.
基金the National Natural Science Foundation of China(No.51575328,61503232).
文摘The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.
文摘The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding algorithm without matrix inversion is proposed, by which the computational complexity can be reduced directly but the decoding performance is not affected.
基金Project supported by the National Nature Science Foundation of China (Grant Nos 60773085 and 60801051)
文摘In this paper, the effect of imperfect channel state information at the receiver, which is caused by noise and other interference, on the multi-access channel capacity is analysed through a statistical-mechanical approach. Replica analyses focus on analytically studying how the minimum mean square error （MMSE） channel estimation error appears in a multiuser channel capacity formula. And the relevant mathematical expressions are derived. At the same time, numerical simulation results are demonstrated to validate the Replica analyses. The simulation results show how the system parameters, such as channel estimation error, system load and signal-to-noise ratio, affect the channel capacity.
文摘In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data.In wavelet domain,if Bayesian estimator is used for denoising problem,the solution requires a prior knowledge about the distribution of wavelet coeffcients.Indeed,wavelet coeffcients might be better modeled by super Gaussian density.The super Gaussian density can be generated by Gaussian scale mixture(GSM).So,we present new minimum mean square error(MMSE)estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise(AWGN).We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.
基金Supported by the National Natural Science Foundation of Jiangsu province(No.08KJB510015)
文摘The channel estimation technique is investigated in OFDM communication systems with multi-antenna Amplify-and-Forward(AF) relay.The Space-Time Block Code(STBC) is applied at the transmitter of the relay to obtain diversity gain.According to the transmission characteristics of OFDM symbols on multiple antennas,a pilot-aided Linear Minimum Mean-Square-Error(LMMSE) channel estimation algorithm with low complexity is designed.Simulation results show that,the proposed LMMSE estimator outperforms least-square estimator and approaches the optimal estimator without error in the performance of Symbol Error Ratio(SER) under several modulation modes,and has a good estimation effect in the realistic relay communication scenario.
文摘最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩阵分块思想和理查德森(Richardson,RI)算法,提出了一种预处理的理查德森(Pretreatment-Richardson,P-RI)迭代算法,该算法首先基于矩阵分块思想构造了一种新形式的线性迭代,然后用此线性迭代对理查德森算法进行预处理,有效提升了算法的收敛速度。实验结果显示,与现有的RI算法相比,该算法的检测性能更好。
