MIMO-OFDM中稀疏度自适应的信道估计方法

Channel estimation method of adaptive sparsity for MIMO-OFDM

将压缩感知应用在稀疏度未知的多天线正交频分复用(multiple-input multiple-output orthogonal frequency division multiplexing,MIMO-OFDM)系统信道估计中,提出一种2级阈值的变步长自适应匹配追踪(variable step size adaptive matching pursuit,Vss AMP)算法,利用残差值确定第1级阈值调整稀疏度步长,提高信道稀疏度的估计精确度,利用噪声能量和信噪比(signal to noise ratio,SNR)确定第2级阈值控制算法迭代条件,降低小信噪比时信道重构误差。理论分析和仿真结果表明,该算法减小了初始步长对信道稀疏度估计精确度的影响,解决了Vss AMP算法阈值难以确定的问题,相比原算法提高了信道估计精确度。

In order to estimate the unknown sparse channel in MIMO-OFDM system using compressive sensing,a two-stage threshold variable step size adaptive matching pursuit（ Vss AMP） algorithm is proposed. Through adjusting the step size of sparsity by the first threshold confirmed with the residue value,the estimation accuracy of channel sparsity can be greatly improved. By utilizing the noise energy and signal-to-noise ratio（ SNR） to confirm the second threshold and then control the algorithm halting conditions,the channel reconstruction error can be reduced when SNR is low. Theory analysis and simulation result indicate that,the initial step size has less effect on the estimation accuracy of channel sparsity and the difficult problem of confirming threshold in Vss AMP is almost resolved. In addition,compared with the traditional algorithm,the new algorithm can improve the channel estimation accuracy.