Compressed Sensing Based on Doubly-selective Slow-fading Channel Estimation in OFDM Systems
Ye Xin-rong①② Zhu Wei-ping① Zhang Ai-qing①② Meng Qing-min①
①(Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ②(The College of Physics and Electronic Information, Anhui Normal University, Wuhu 241000, China)
Abstract：In order to improve the reconstruction accuracy of smoothed l0-norm (Sl0) algorithm in the presence of noise, a modified algorithm named smoothed l0-norm regularized least-square (l2-Sl0) is proposed in this paper, which permits a small perturbation. Further, through placing a finite grid in the planar time-frequency bounded region, the problem of doubly-selective slow-fading channel estimation in OFDM system is modeled as the problem of sparse signal reconstruction in compressed sensing framework, and then the l2-Sl0 algorithm is applied to reconstruct the channel parameters. A number of computer-simulation-based experiments show that reconstruction accuracy of the l2-Sl0 algorithm is improved by approximately 10 dB as compared with the Sl0 algorithm in the presence of noise. The performance of the proposed doubly-selective slow-fading channel estimation method using l2-Sl0 algorithm is nearly close to that of the ideal Least Square (ideal-LS) method. Moreover, the proposed method has higher estimation uccuracy well in the case of low SNR.
2015, Vol. 37 
