Identification of Non-Stationary Communication Channels with a Sparseness Property

The problem of identifying non-stationary communication channels with a sparseness property using the local basis function approach is considered. This sparseness refers to scenarios where a few impulse response coefficients significantly differ from zero. The sparsity-aware estimation algorithms are usually obtained using ℓ1 regularization. Unfortunately, the minimization problem lacks a sometimes closed-form solution; one must rely on numerical search, which is a serious drawback. We propose the fast regularized local basis functions (fRLBF) algorithm based on appropriately reweighted ℓ2 regularizers, which can be regarded as a first-order approximation of the ℓ1 approach. The proposed solution incorporates two regularizers, enhancing sparseness in both the time/lag and frequency domains. The choice of regularization gains is an important part of regularized estimation. To address this, three approaches are proposed and compared to solve this problem: empirical Bayes, decentralized, and cross-validation approaches. The performance of the proposed algorithm is demonstrated in a numerical experiment simulating underwater acoustics communication scenarios. It is shown that the new approach can outperform the classical one and is computationally attractive.