基于梯度追踪的MIMO-OFDM稀疏信道估计算法

MIMO-OFDM Sparse Channel Estimation Algorithm Based on Gradient Pursuit

现有的压缩感知MIMO-OFDM信道估计方法多采用正交匹配追踪算法及其改进的算法。针对该类算法重构大规模的数据存在计算复杂度高、存储量大等问题,提出了基于梯度追踪算法的MIMO-OFDM稀疏信道估计方法。梯度追踪算法采用最速下降法对目标函数解最优解,即每步迭代时计算目标函数的搜索方向和搜索步长,并以此选择原子得到每次迭代重构值的最优解。本文使用梯度追踪算法对信道进行估计,并与传统的最小二乘估计算法、正交匹配追踪算法的性能和计算复杂度进行比较。仿真结果表明,梯度追踪算法能够保证较好的估计效果,减少了导频开销,降低了运算复杂度,提高了重构效率。

The existing MIMO-OFDM channel estimation method based on compressed sensing uses multiple orthogonal matching pursuit algorithm and its improved algorithm.For such a large scale data reconstruction algorithm has high computational complexity,storage capacity and other issues,MIMO-OFDM sparse channel estimation method based on the gradient pursuit algorithm is proposed.Gradient pursuit algorithm uses the steepest descent method for the objective function optimal solution,namely calculating the search direction of the objective function and the search step with each iteration,and selecting the optimal solution every atom iterative reconstruction values.As used herein,the estimation performance of gradient pursuit algorithm is compared with the performance of traditional least squares estimation algorithm and orthogonal matching pursuit algorithm.The simulation results show that the gradient pursuit algorithm can guarantee a better estimate and reduce the pilot overhead and the computational complexity.Therefour,the efficiency of reconstruction is improved.