基于改进恢复算法的双选稀疏信道估计

高速移动情况下,正交频分复用系统(orthogonal frequency division multiplexing,OFDM)无线通信信道可建模为时间-频率双选信道,其响应在时延-多普勒域呈现稀疏性,使压缩感知技术得以应用到稀疏信道估计中。当稀疏度提高时,压缩感知(compressed sensing,CS)中正则化正交匹配追踪恢复算法(regularized orthogonal matching pursuit,ROMP)的复杂度增大。提出了有严格计算约束的改进恢复算法,该算法每次迭代选择固定数目的原子使支撑集为非奇异矩阵来降低原子选择和最小二乘(least squares,LS)法计算上的复杂度,并且每次迭代更新支撑集来保证精度。仿真结果表明,和ROMP算法比较,改进恢复算法的运行时间明显降低,并且在一定的迭代次数下精确度得以保证。

Double Selective Sparse Channel Estimation Based on A Optimized Recovery Algorithm

The wireless communication channels within orthogonal frequency-division multiplexing systems could be modeled as time-frequency doubly selective channels introduced by high mobility. The application of compressed sensing is considered to the sparse channel estimation because of its sparsity on delay-doppler domain. The complexity of Regularized Orthogonal Matching Pursuit(ROMP) recovery algorithm increases with higher sparsity. To reduce the complexity of atom selection and solving least squares problem in ROMP, a optimized recovery algorithm with a rigorous computational bound is proposed in this paper, which identifies a fixed number of atoms to make the recovery submatrix be a nonsingular matrix. In addition, the recovery submatrix is renewed at the end of each iteration to improve the precision. Simulation results demonstrate that compared with ROMP algorithm, elapsed time in the optimized recovery algorithm is decreased evidently and the accuracy could be ensured with proper iteration times.