Title :
An Improved Particle Smoother for Blind Equalization in Time-Varying MIMO Channels
Author_Institution :
Sch. of Sci., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
An improved fixed-lag particle smoother in the mixture Kalman filter (MKF) framework is developed for the blind equalization in time-varying multiple-input multiple-output (MIMO) channels. The improved particle smoother utilizes the uniform proposal distribution (PD) and the optimal resampling to generate particles. Compared to the particle smoother with the posterior PD, the improved particle smoother can provide more accurate estimation results, while both smoothers have same computational complexity. Simulation results are provided to illustrate the performance of the methods.
Keywords :
Kalman filters; MIMO communication; blind equalisers; particle filtering (numerical methods); time-varying channels; MKF framework; blind equalization; computational complexity; fixed-lag particle smoother improvement; mixture Kalman filter framework; optimal resampling; posterior PD; time-varying MIMO channels; time-varying multiple-input multiple-output channels; uniform proposal distribution; Bit error rate; Blind equalizers; Kalman filters; MIMO; Monte Carlo methods; Receiving antennas; Signal to noise ratio; Equalization; fixed-lag smoother; mixture Kalman filter; multiple-input multiple-output; multiple-input multiple-output (MIMO); particle filter;
Journal_Title :
Communications Letters, IEEE
DOI :
10.1109/LCOMM.2015.2425890
