OFDM系统双选择性慢衰落信道的压缩感知估计

为了增强压缩感知框架里Sl0(Smoothedl0-norm)重构算法的抗噪性能，该文在其目标函数里添加一个误差容允项，并提出了一种改进型重构算法l2-Sl0(Smoothed l0-norm regularized least-square)。另外通过对多径信道的时延和多普勒频移参数构成的时频2维有界区域进行量化，将OFDM时频双选择性慢衰落信道估计问题建模为压缩感知理论中的稀疏信号重构问题，提出了一种采用l2-Sl0估计信道时频参数的方法。仿真结果表明在相同的噪声环境里，l2-Sl0的重构性能优于Sl010 dB左右；运用l2-Sl0的信道估计方法可获得接近于理想最小二乘法的估计性能，且该方法在低信噪比的场景里也能获得较高的估计准确度。

Compressed Sensing Based on Doubly-selective Slow-fading Channel Estimation in OFDM Systems

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.