采用残差变化控制的自适应稀疏信道估计

针对传统压缩感知信道估计对稀疏度信息依赖和稀疏度自适应信道估计在低信噪比时抗噪能力较差的问题，提出了一种采用残差变化控制的稀疏度自适应的压缩感知信道估计算法。该算法在传统的压缩感知信道估计的基础上引入残差变化控制，通过比较每次迭代下的残差变化的幅度来控制信道估计的迭代次数，提高信道估计的自适应性和鲁棒性。同时，为解决传统稀疏度自适应压缩感知信道估计抗噪能力较差的问题，利用正交匹配追踪提高算法的抗噪声性能。相比于传统的稀疏度自适应匹配追踪(Sparsity Adaptive Matching Pursuit，SAMP)算法，所提算法约有4 dB的性能优势，且算法复杂度更低。

Adaptive sparse channel estimation based on residual change control

In view of the problem that the traditional channel estimation algorithms based compressed sensing rely on the prior knowledge of the sparsity of the channel,and the sparsity adaptive channel estimation has poor anti-noise ability at low signal-to-noise ratio,a new sparsity adaptive compressed sensing channel estimation algorithm based on residual change control is proposed.In this algorithm,residual change control is introduced based on the traditional channel estimation of compressed sensing.By comparing the amplitude of residual change in each iteration,the number of iterations of channel estimation is controlled,and the adaptability and robustness of channel estimation are improved.In order to solve the problem of poor anti-noise ability of traditional sparse adaptive compressed sensing channel estimation,the orthogonal matching pursuit(OMP) is used to improve the anti-noise performance of the algorithm.Compared with the traditional sparsity adaptive matching pursuit(SAMP) algorithm,the proposed algorithm has a performance advantage of about 4 dB and lower complexity.