Sparse channel estimation based on a p-norm-like constrained least mean fourth algorithm

In this paper, a p-norm-like constraint is utilized to develop a sparse least mean fourth algorithm for sparse channel estimation. By incorporating the p-norm-like constraint into the cost function of conventional least mean fourth (LMF) algorithm, a p-norm-like constraint least mean fourth (PNC-LMF) algorithm is achieved to exploit the sparsity property of the broadband sparse wireless communication channel. The proposed PNC-LMF algorithm aims to seek a tradeoff between the sparsity effects and the channel estimation errors, which is also verified by the simulation and compared with conventional LMF and previously reported popular sparse LMF algorithms. The simulated results show that the proposed PNC-LMF algorithm has faster convergence speed and lower channel estimation errors when the channel is sparse.