OFDM系统中一种双选择性稀疏信道压缩感知方法

针对OFDM系统,提出了一种基于压缩感知（CS）的双选择性稀疏信道估计新方法.为解决传统二维插值算法无法准确估计双选择性稀疏信道的问题,通过利用信道在时频域的稀疏特性,将OFDM系统下的双选择性信道模型转化为CS可解的BPIC数学模型,并最终利用基追踪算法对稀疏信道的脉冲冲激响应实现估计.仿真结果显示,新方法能有效减少导频数,提高频谱利用率;在传统的FFT-Linear和FFT-FFT二维联合插值算法无法正确估计出信道响应时,基追踪算法仍能实现对稀疏信道的精确估计.

A Compressive Sensing Method for Estimating Doubly-Selective Sparse Channels in OFDM Systems

This paper proposed a new sparse channel estimation method based on compressive sensing（CS） in the OFDM system.In order to solve the problem that the conventional two-dimensional interpolation methods fail to make an accurate estimation of doubly-selective sparse channels,the new method fully exploits the sparsity of physical channels in the time-frequency domain and transforms the OFDM channel model into the BPIC model,which can be solved by the compressive sensing algorithms.Finally,the exact estimation of sparse channel is fulfilled by using the BP algorithm.Simulation results show that the new method can significantly reduce the number of pilots and thus improve spectrum efficiency.It is also shown that the BP algorithm can still make an exact estimation of sparse channel when the conventional FFT-Linear and FFT-FFT two-dimensional joint interpolation methods fail to work.