Compressed Sensing for Wideband HF Channel Estimation

Compressive sensing theory is suitable for sparse channel estimation, since the acquired measurement can be reduced in comparison with linear estimation methods. In this paper, we analyze the wideband HF channel estimation. Experimental results demonstrate that this channel is sparse in the delay spread domain. Moreover, the use of sparse recovery algorithms achieves better results in terms of Mean-Square Deviation than the Least Square algorithm.