基于压缩采样匹配追踪的稀疏度和稀疏信道联合估计

原始的压缩采样匹配追踪算法依赖于已知稀疏度，因此本文研究了一种稀疏度和稀疏信道联合估计算法。首先提出了一种新的稀疏向量的替代，能够在有限长度的训练序列下，达到较好的稀疏度和信道估计效果。然后通过对稀疏信道估计中的噪声分量的分析，提出了一种稀疏度估计算法，结合信道估计最终给出了一种稀疏度和稀疏信道联合估计算法。仿真结果表明：新的稀疏向量的替代在稀疏度和信道估计方面都有明显的优势，并且提出的稀疏度和稀疏信道联合估计算法在性能上好于mCoSaMP算法。

Joint Sparsity and Sparse Channel Estimation Algorithm Based on CoSaMP

Since the original compressive sampling matching pursuit relies on known sparsity and sparsity is often difficult to obtain, a joint sparsity and sparse channel estimation apgorithm is studied in this paper. A new proxy of sparse signal is proposed that can achieve better channel estimation results in a limited length of the training sequence. Through the analysis of the noise component in sparse channel estimation, we propose a sparsity estimation algorithm. Combined with channel estimation a joint sparsity and sparse channel estimation algorithm is proposed. Simulation results show that: The new proxy of sparse signal has obvious advantages in both sparsity and channel estimation. The performance of joint sparsity and sparse channel estimation algorithm is better than mCoSaMP algorithm.