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A sparsity adaptive CS-based channel estimation algorithm. (Chinese. English summary) Zbl 1374.94754
Summary: The traditional sparsity adaptive multipath channel estimation algorithms based on compressed sensing (CS) cost too much time. For solving this problem, a sparsity adaptive archiving normalized iterative hard thresholding (SAANIHT) algorithm based on correlation analysis is proposed. The ANIHT algorithm has fewer calculation amount than traditional algorithm only if the channel sparsity degree has been known. In order to make the algorithm possess the ability of blind sparse channel estimation, a sparsity adaptive method optimized by the Gaussian kernel function is introduced. The simulation results show that for the same sparsity degree, the proposed algorithm costs fewer time than other algorithms and has better performance in low signal-to-noise ratio and better convergence performance and stability.
MSC:
94A40 Channel models (including quantum) in information and communication theory
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
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