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; DOI: 10.1109/TWC.2010.02.081694
Source: IEEE Xplore

ABSTRACT 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: This paper proposes a framework for joint blind timing and carrier offset estimation and data detection using a Sequential Importance Sampling (SIS) particle filter in Additive White Gaussian Noise (AWGN) channels. We assume baud rate sampling and model the intractable posterior probability distribution functions for sampling timing and carrier offset particles using beta distributions. To enable the SIS approach to estimate static synchronization parameters, we propose new resampling guidelines for dealing with the degeneracy problem and fine tuning the estimated values. We derive the Weighted Bayesian Cramer Rao Bound (WBCRB) for joint timing and carrier offset estimation, which takes into account the prior distribution of the estimation parameters and is an accurate lower bound for all considered Signal to Noise Ratio (SNR) values. Simulation results are presented to corroborate that the Mean Square Error (MSE) performance of the proposed algorithm is close to optimal at higher SNR values (above 20 dB). In addition, the bit error rate performance approaches that of the perfectly synchronized case for small unknown carrier offsets and any unknown timing offset. The advantage of our particle filter algorithm, compared to existing techniques, is that it can work for the full range acquisition of carrier offsets.
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    ABSTRACT: A new approach of smoothing the white noise for nonlinear stochastic system was proposed. Through presenting the Gaussian approximation about the white noise posterior smoothing probability density function, an optimal and unifying white noise smoothing framework was firstly derived on the basis of the existing state smoother. The proposed framework was only formal in the sense that it rarely could be directly used in practice since the model nonlinearity resulted in the intractability and infeasibility of analytically computing the smoothing gain. For this reason, a suboptimal and practical white noise smoother, which is called the unscented white noise smoother (UWNS), was further developed by applying unscented transformation to numerically approximate the smoothing gain. Simulation results show the superior performance of the proposed UWNS approach as compared to the existing extended white noise smoother (EWNS) based on the first-order linearization.
    03/2013; 20(3). DOI:10.1007/s11771-013-1532-9
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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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