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An alternating minimization method for sparse channel estimation
 in Proc. 9th Int. Conf. Latent Variable Anal. Signal Seperation (LVAICA, Formerly Known as ICA
"... Abstract. The problem of estimating a sparse channel, i.e. a channel with a few nonzero taps, appears in many fields of communication including acoustic underwater or wireless transmissions. In this paper, we have developed an algorithm based on Iterative Alternating Minimization technique which it ..."
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Abstract. The problem of estimating a sparse channel, i.e. a channel with a few nonzero taps, appears in many fields of communication including acoustic underwater or wireless transmissions. In this paper, we have developed an algorithm based on Iterative Alternating Minimization technique which iteratively detects the location and the value of the channel taps. In fact, at each iteration we use an approximate Maximum A posteriori Probability (MAP) scheme for detection of the taps, while a least square method is used for estimating the values of the taps at each iteration. For approximate MAP detection, we have proposed three different methods leading to three variants for our algorithm. Finally, we experimentally compared the new algorithms to the CramérRao lower bound of the estimation based on knowing the locations of the taps. We experimentally show that by selecting appropriate preliminaries for our algorithm, one of its variants almost reaches the CramérRao bound for high SNR, while the others always achieve good performance. 1
Adaptive and Nonadaptive ISI Sparse Channel Estimation Based on SL0 and Its Application in ML SequencebySequence Equalization
"... Abstract. In this paper, we firstly propose an adaptive method based on the idea of Least Mean Square (LMS) algorithm and the concept of smoothed l0 (SL0) norm presented in [1] for estimation of sparse Inter Symbol Interface (ISI) channels which will appear in wireless and acoustic underwater transm ..."
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Abstract. In this paper, we firstly propose an adaptive method based on the idea of Least Mean Square (LMS) algorithm and the concept of smoothed l0 (SL0) norm presented in [1] for estimation of sparse Inter Symbol Interface (ISI) channels which will appear in wireless and acoustic underwater transmissions. Afterwards, a new nonadaptive fast channel estimation method based on SL0 sparse signal representation is proposed. ISI channel estimation will have a direct effect on the performance of the ISI equalizer at the receiver. So, in this paper we investigate this effect in the case of optimal Maximum Likelihood Sequencebysequence Equalizer (MLSE) [2]. In order to implement this equalizer, we propose a new method called prefiltered Parallel Viterbi Algorithm (or prefiltered PVA) for general ISI sparse channels which has much less complexity than ordinary Viterbi Algorithm (VA) and also with no considerable loss of optimality, which we have examined by doing some experiments. Indeed, Simulation results clearly show that the proposed concatenated estimationequalization methods have much better performance than the usual equalization methods such as Linear Mean Square Equalization (LMSE) for ISI sparse channels, while preserving simplicity at the receiver with the use of PVA. 1
detector with radar experimentations in implusive noise, ” presented at
"... processing methods for heterogeneous radar clutter scenarios, ” Signal Process., vol. 84, pp. 1653–1665, 2004. [12] I. Kirsteins and D. Tufts, “Adaptive detection using a low rank approximation to a data matrix, ” IEEE Trans. Aerosp. Electron. Syst., vol. 30, no. 1, pp. 55–67, 1994. [13] A. Haimovic ..."
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processing methods for heterogeneous radar clutter scenarios, ” Signal Process., vol. 84, pp. 1653–1665, 2004. [12] I. Kirsteins and D. Tufts, “Adaptive detection using a low rank approximation to a data matrix, ” IEEE Trans. Aerosp. Electron. Syst., vol. 30, no. 1, pp. 55–67, 1994. [13] A. Haimovich, “Asymptotic distribution of the conditional signaltonoise ratio in an eigenanalysisbased adaptive array,”
1Sparse Channel Estimation by Factor Graphs
, 2014
"... The problem of estimating a sparse channel, i.e. a channel with a few nonzero taps, appears in various areas of communications. Recently, we have developed an algorithm based on iterative alternating minimization which iteratively detects the location and the value of the taps. This algorithms invo ..."
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The problem of estimating a sparse channel, i.e. a channel with a few nonzero taps, appears in various areas of communications. Recently, we have developed an algorithm based on iterative alternating minimization which iteratively detects the location and the value of the taps. This algorithms involves an approximate Maximum A Posteriori (MAP) probability scheme for detection of the location of taps, while a least square method is used for estimating the values at each iteration. In this work, based on the method of factor graphs and message passing algorithms, we will compute an exact solution for the MAP estimation problem. Indeed, we first find a factor graph model of this problem, and then perform the wellknown minsum algorithm on the edges of this graph. Consequently, we will find an exact estimator for the MAP problem that its complexity grows linearly with respect to the channel memory. By substituting this estimator in the mentioned alternating minimization method, we will propose an estimator that will nearly achieve the CramérRao bound of the genieaided estimation of sparse channels (estimation based on knowing the location of nonzero taps of the channel), while it can perform faster than most of the proposed algorithms in literature.