The problem of blind separation of signals in post nonlinear mixture is addressed in this paper. The post nonlinear mixture is formed by a component wise nonlinear distortion after the linear mixture. Hence a nonlin...The problem of blind separation of signals in post nonlinear mixture is addressed in this paper. The post nonlinear mixture is formed by a component wise nonlinear distortion after the linear mixture. Hence a nonlinear adjusting part placed in front of the linear separation structure is needed to compensate for the distortion in separating such signals. The learning rules for the post nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for blind separation of post nonlinearly mixed sub and super Gaussian signals is proposed based on some previous work. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub and super Gaussian probability models during learning according to a stability condition and operates in a block adaptive manner. The effectiveness of the algorithm is verified by experiments on simulated and real world signals.展开更多
In this paper, a quasi-Newton method fbr semi-blind estimation is derived for channel estimation in uplink cloud radio access networks （C-RANs）. Different from traditional pilot-aided estimation, semiblind estimatio...In this paper, a quasi-Newton method fbr semi-blind estimation is derived for channel estimation in uplink cloud radio access networks （C-RANs）. Different from traditional pilot-aided estimation, semiblind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information （CSI） obtained by least-squared （LS） estimation is needed in semi-blind estimation. BFGS （Brayben, Fletcher, Goldfarb and Shanno） algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood （ML） principle. In addition, mean-square-error （MSE） used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semiblind estimation for C-RANs.展开更多
In this paper,application of Sequential Quasi Monte Carlo(SQMC)to blind channel andsymbol joint estimation in cooperative Multiple-Input Multiple-Output(MIMO)system is proposed,which does not need to transmit training...In this paper,application of Sequential Quasi Monte Carlo(SQMC)to blind channel andsymbol joint estimation in cooperative Multiple-Input Multiple-Output(MIMO)system is proposed,which does not need to transmit training symbol and can save the power and channel bandwidth.Additionally,an improved version of SQMC algorithm by taking advantage of current received signal isdiscussed.Simulation results show that the SQMC method outperforms the Sequential Monte Carlo(SMC)methods,and the incorporation of current received signal improves the performance of theSQMC obviously.展开更多
文摘The problem of blind separation of signals in post nonlinear mixture is addressed in this paper. The post nonlinear mixture is formed by a component wise nonlinear distortion after the linear mixture. Hence a nonlinear adjusting part placed in front of the linear separation structure is needed to compensate for the distortion in separating such signals. The learning rules for the post nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for blind separation of post nonlinearly mixed sub and super Gaussian signals is proposed based on some previous work. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub and super Gaussian probability models during learning according to a stability condition and operates in a block adaptive manner. The effectiveness of the algorithm is verified by experiments on simulated and real world signals.
基金supported in part by the the National High Technology Research and Devel-opment Program of China(Grant No.2014AA01A701)National Natural Science Foundation of China(Grant No.61361166005)+2 种基金the State Major Science and Technology Special Projects(Grant No.2016ZX03001020006)the National Program for Support of Top-notch Young Pro-fessionalsthe Science and Technology Development Project of Beijing Municipal Education Commission of China(Grant No.KZ201511232036)
文摘In this paper, a quasi-Newton method fbr semi-blind estimation is derived for channel estimation in uplink cloud radio access networks （C-RANs）. Different from traditional pilot-aided estimation, semiblind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information （CSI） obtained by least-squared （LS） estimation is needed in semi-blind estimation. BFGS （Brayben, Fletcher, Goldfarb and Shanno） algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood （ML） principle. In addition, mean-square-error （MSE） used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semiblind estimation for C-RANs.
基金the National Natural Science Foundation of China(No.60372107)the Ph.D.Innovation Programof Jiangsu Province(No.200670).
文摘In this paper,application of Sequential Quasi Monte Carlo(SQMC)to blind channel andsymbol joint estimation in cooperative Multiple-Input Multiple-Output(MIMO)system is proposed,which does not need to transmit training symbol and can save the power and channel bandwidth.Additionally,an improved version of SQMC algorithm by taking advantage of current received signal isdiscussed.Simulation results show that the SQMC method outperforms the Sequential Monte Carlo(SMC)methods,and the incorporation of current received signal improves the performance of theSQMC obviously.
