Who care for channel sparsity? Robust sparse recursive least square based channel estimation

In the next-generation wireless communication systems, the broadband signal transmission over wireless channel often incurs the frequency-selective channel fading behavior and also results in the channel sparse structure which is dominant by very few large coefficients. To ensure the stable wireless propagation, various linear adaptive channel estimation algorithms, e.g., recursive least square (RLS) and least mean square (LMS), have been developed. However, the traditional algorithms are unable to exploit the channel sparsity. Supporting by the compressive sensing (CS) theory, channel estimation performance can be further improved by taking advantage of the sparsity. In this paper, RLS based fast adaptive sparse channel estimation algorithm is proposed by introducing two sparse constraint functions, L1-norm and L0-norm. To improve the flexibility of the proposed algorithms, this paper introduces a regularization parameter selection method to adaptively exploit the channel sparsity. Finally, Monte Carlo based computer simulations are conducted to validate the effectiveness of the proposed algorithms based robust channel estimation either for sparse or non-sparse channel.