Adaptive signal processing
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Atomic Decomposition by Basis Pursuit
SIAM Review
Robust-SL0 for stable sparse representation in noisy settings
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Sparse LMS for system identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Sparse Channel Estimation with Zero Tap Detection
IEEE Transactions on Wireless Communications
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In this paper, which is an extended version of our work at LVA/ICA 2010 [1], the problem of Inter Symbol Interface (ISI) Sparse channel estimation and equalization will be investigated. We firstly propose an adaptive method based on the idea of Least Mean Square (LMS) algorithm and the concept of smoothed l"0 (SL0) norm presented in [2] for estimation of sparse ISI channels. Afterwards, a new non-adaptive 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 Sequence-by-Sequence Equalizer (MLSE) [3]. In order to implement this equalizer, we first introduce an equivalent F-model for sparse channels, and then using this model we propose a new method called pre-filtered parallel Viterbi algorithm (or pre-filtered 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 in Matlab/Simulink. Indeed, simulation results clearly show that the proposed concatenated estimation-equalization 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.