一种改进的压缩采样匹配追踪算法

An Improved Compression Sampling Matching Pursuit Algorithm

针对正交频分复用(OFDM)系统中,压缩采样匹配追踪(CoSaMP)算法在进行信道估计时选择原子更新残差过程中采用最小二乘(LS)估计忽略了噪声影响的问题,文章利用信道时域的稀疏性提出了一种改进的CoSaMP算法。首先利用CoSaMP算法进行初始信道估计,然后再采用线性最小均方误差(LMMSE)算法对结果进行降噪处理,提高了估计精度。仿真结果表明,在OFDM系统中,改进的CoSaMP算法比传统CoSaMP算法具有更好的均方误差和误码率(BER)估计性能。

Least Square(LS) estimation method in Orthogonal Frequency Division Multiplexing(OFDM) system ignores the noise effect in the process of choosing the residual of atom update. In order to solve this issue, an improved Compressed Sampling Matching Pursuit(CoSaMP) algorithm is proposed by using the sparsity of the time-domain channel. Firstly, the initial channel estimation is performed by CoSaMP algorithm. Then, the Linear Minimum Mean Square Error(LMMSE) is used to reduce the noise, which improves the estimation accuracy. The simulation results show that the improved CoSaMP algorithm has better performance in mean square error and Bit Error Ratio(BER) estimation than traditional CoSaMP algorithm.