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This paper suggests to use a block MAP-LMS (BMAP-LMS) adaptive filter instead of an adaptive filter called MAP-LMS for estimating the sparse channels. Moreover to faster convergence than MAP-LMS, this block-based adaptive filter enables us to use a compressed sensing version of it which exploits the sparsity of the channel outputs to reduce the sampling rate of the received signal and to alleviate...
We use the particle filtering approach to develop a new Bayesian equalizer for turbo equalization under conditions of imperfect estimation of the channel impulse response (CIR) and the noise variance. This new equalizer is derived on the basis of fixed-lag smoothing, using the SIS (Sequential Importance Sampling) methodology to approximate the conditional probability distribution of a block of transmitted...
In this paper, optimal training sequence design for multiple-input multiple-output (MIMO) intersymbol interference channels is addressed, and several novel low-complexity channel estimators are proposed, using uncorrelated Golay complementary sets of polyphase sequences. A polyphase sequence is a sequence of complex numbers, each of unit magnitude. The theoretical analysis and simulation show that,...
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