Compressed Sensing Channel Estimation Algorithm Combined with Wavelet Denoising

Abstract

In wireless multipath channels, there is sparsity, in which traditional channel estimation algorithms do not take advantage of and ignore the impact of noise. An effective estimation algorithm combining compressed sensing and wavelet de-noising is proposed. Before channel estimation, the pilot signal received by the receiver is de-noised by the wavelet de-noising method to obtain the de-noised measurement vector, and then the original signal is recovered by the compressed sensing technology for sparse channel estimation. This algorithm breaks away from the limitation of traditional algorithms that require predictive sparsity to achieve sparsity adaptive signal reconstruction. Through simulation analysis, it has been proven that the fusion wavelet denoising algorithm can improve the estimation performance, reduce estimation errors, and be more suitable for sparse channel estimation by denoising the signal during channel estimation.