一种OFDM稀疏信道估计系数优化方案

A Coefficient Optimal Scheme for OFDM Sparse Channel Estimation

围绕利用贪婪追踪算法的正交频分复用(orthogonal frequency division multiplexing,OFDM)调制系统的稀疏信道估计,研究了由于原子的错误选择所导致的恢复性能下降问题.基于针对贪婪追踪算法中最小二乘重构运算过程的分析,发现错误原子上存在严重的原子系数的过估计现象.在此基础上,提出了利用相邻符号间信道路径时延相关性的系数优化方案(coefficients optimization scheme,COS),用以提高原子选择错误时稀疏信道估计的准确性.仿真结果表明,将COS与传统的正交匹配追踪(orthogonal matching pursuit,OMP)算法和稀疏度自适应匹配追踪(sparsity adaptive matching pursuit,SAMP)算法融合,都有效地抑制了原子的错误选择对稀疏信道估计的影响,并且在低信噪比下提升了贪婪追踪算法的估计性能,在不同多径信道模型下的仿真测试中均具有较好的鲁棒性.

Focusing on the sparse channel estimation of orthogonal frequency division multiplexing(OFDM)modulation system using greedy pursuit algorithms,the problem of recovery performance degradation caused by wrong selection of atom is studied.Based on the analysis of the least-squares reconstruction process in the greedy pursuit algorithm,it is found that there is a severe overestimation of the atom coefficients on the wrong atoms.On this basis,a coefficient optimization scheme(COS)using the channel path delay correlation between adjacent symbols is proposed to improve the accuracy of sparse channel estimation when the atom selection is wrong.The simulation results show that the combining COS with the traditional orthogonal matching pursuit(OMP)algorithm and sparsity adaptive matching pursuit(SAMP)algorithm can effectively suppress the effect of wrong selection of atom on sparse channel estimation,and increase the estimation performance of greedy pursuit algorithms under low signal-to-noise ratio.The simulation tests under different multipath channel models show great robustness.