Research On Wireless Channel Estimation Method Based On Compressed Sensing

Compressed Sensing theory is a newly signal sampling framework, it has attracted a lot of experts and scholars from many fields. If the signal is compressible, the signal can be sampled with the rate far below the Nyquist frequency and reconstructed without distortion at the receiver. This new theory unites the signal sampling and compression and avoids the large number of redundant sampling data. This great advantage makes the Compressed Sensing theory has a great prospect in the future.This thesis introduces the Compressed Sensing theory from the aspects of sparse representation of signal, the design of measurement matrix and reconstructed algorithms. It focuses on the signal recovery algorithms and has a detail description of several classical algorithms. Next, some simulation experiments carry out. Finally, combining with the sparse characteristics of the wireless channel, the thesis introduces the application of Compressed Sensing techniques to the problem of channel estimation. The main contribution of the thesis is as following two points.(1) The thesis has a detail description of SLO (Smoothed l0norm algorithm) and analysis the "notched effect" in this method. In addition, the step in the process of solving is determined by experience. Which results in the approximation result is not optimal. In order to solve this problem, the thesis presents a new algorithm based on step optimization and the conjugate gradient method. Computer simulations confirm the effectiveness of the introduced algorithm comparisons with the existing methods in terms of run time, reconstructive probability and accuracy.(2) The research includes analyzing the sparsity of the multipath wireless channel and deducing the system model. Providing some experimental results of the algorithm proposed in this thesis and its comparison with conventional SLO, orthogonal matching pursuit, regularized orthogonal matching pursuit, least square. From the results, we can see the advantages of the compressed channel sensing over tradition LS-based methods and the reconstructed quality of the proposed algorithm is better than other methods.