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Blind Identification Algorithms for Sparse SIMO Systems


In this work, we are interested in blind identification of sparse single-input multpe-output (SIMO) systems. First, we present a modified cross-relations (CR) technique combined with a $\ell_p$ norm, which is considered as a good sparsity measure. Then, A maximum a posteriori approach is considered using generalized Laplacian distribution for the channel coefficients. This leads to a cost function given by the deterministic maximum likelihood (ML) criterion penalized by 'a sparsity measure' term expressed by the $\ell_p$ norm of the channel coefficient vector. A simple but efficient optimization algorithm using gradient technique with optimal step-size is proposed. The simulations show that the proposed method outperforms the ML technique in terms of estimation error and is robust against channel order overestimation errors.
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hal-01067712 , version 1 (23-09-2014)


  • HAL Id : hal-01067712 , version 1


Abdeldjalil Aissa El Bey. Blind Identification Algorithms for Sparse SIMO Systems. 2014. ⟨hal-01067712⟩
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