Blind System Identification Using Fourth Order Spectral Analysis of Complex Signals
SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
Blind channel identification based on second order cyclostationary statistics
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Underwater acoustic communication channels: propagation models and statistical characterization
IEEE Communications Magazine
Distributed Blind Adaptive Algorithms Based on Constant Modulus for Wireless Sensor Networks
ICWMC '10 Proceedings of the 2010 6th International Conference on Wireless and Mobile Communications
Blind equalization using higher order cumulants and neural network
IEEE Transactions on Signal Processing
Blind equalization of digital communication channels usinghigh-order moments
IEEE Transactions on Signal Processing
New criteria for blind deconvolution of nonminimum phase systems (channels)
IEEE Transactions on Information Theory
Super-exponential methods for blind deconvolution
IEEE Transactions on Information Theory
Using recurrent neural networks for adaptive communication channel equalization
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This study proposed a fractionally spaced equalizer constant modulus algorithm (CMA) for improving wireless sensor network (WSN) transmission, which could suppress noise amplification, reduce the sensibility to time phase errors and converge to the expected global minimum point, so as to reach the effectiveness of a global equalization communication channel. Based on the multi-channel equalization model in the WSN transmission system, various experimental analyses and performance comparisons proved that the convergence rate of the method proposed in this study was higher than that of general existing algorithms. The final experimental analysis proved that the higher the sampling rate was, the higher the convergence rate and the smaller the mean square error would be. The sampling rate had an important effect on the blind equalization, and it was proved that the method proposed in this study had an improved blind equalization algorithm to some extent.