A Fixed-Lag Particle Smoother for Blind SISO Equalization of Time-Varying Channels

Inst. Mil. de Eng., Rio de Janeiro, Brazil
IEEE Transactions on Wireless Communications (Impact Factor: 2.5). 03/2010; 9(2):512 - 516. DOI: 10.1109/TWC.2010.02.081694
Source: IEEE Xplore


We introduce a new sequential importance sampling (SIS) algorithm which propagates in time a Monte Carlo approximation of the posterior fixed-lag smoothing distribution of the symbols under doubly-selective channels. We perform an exact evaluation of the optimal importance distribution, at a reduced computational cost when compared to other optimal solutions proposed for the same state-space model. The method is applied as a soft input-soft output (SISO) blind equalizer in a turbo receiver framework and simulation results are obtained to show its outstanding BER performance.

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    • "In the last decade, there has been an increasing interest in the use of particle filtering (also known as Sequential Monte Carlo algorithms) to solve estimation problems in wireless communication systems in real-time. Traditional applications of particle filters has been in mobility tracking [6]–[8] and channel estimation [9], [10] or equalization [11], [12] for wireless communications systems. Recently, particle filters have been proposed for estimating either timing or carrier offsets in different wireless communication systems [13]–[15]. "
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    ABSTRACT: We propose a fixed-lag particle smoother (FLPS) for time-varying frequency-selective and nonlinear channel estimation. Compared to the standard particle filter (PF) which tracks the filtering distribution of the state variable, the FLPS tracks the fixed-lag smoothing distribution of the state variable. The choice of the proposal distribution and the computation of the importance weights are derived. Simulations are provided to illustrate the performance of the FLPS under different system settings. Performance comparison of the FLPS with the standard PF is provided. Simulation results show that the FLPS outperforms the PF with the similar computational costs.
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