Recursive least square-based fast sparse multipath 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 supported only by few large coefficients. For the stable wireless propagation to be ensured, linear adaptive channel estimation algorithms, eg, recursive least square and least mean square, have been developed. However, these traditional algorithms are unable to exploit the channel sparsity. Actually, channel estimation performance can be further improved by taking advantage of the sparsity. In this paper, 2 recursive least square-based fast adaptive sparse channel estimation algorithm is proposed by introducing sparse constraints, L1-norm and L0-norm, respectively. 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.