The sparsity of multipaths in the wideband channel has motivated the use of compressed sensing for channel estimation. In this letter, we propose a different approach to sparse channel estimation. We exploit the fact that $L$ taps of channel impulse response in time domain constitute a non-orthogonal superposition of $L$ geometric sequences in frequency domain. This converts the channel estimation problem into the extraction of the parameters of geometric sequences. Numerical results show that the proposed scheme is superior to existing algorithms in high signal-to-noise ratio (SNR) and large bandwidth conditions.
翻译:宽带频道多路径的宽度促使使用压缩传感器进行频道估计。 在本信中,我们提出一种不同的方法来估计频道的稀疏度。我们利用以下事实:在时间域中,频道脉冲反应的用量为美元,这在频率域中构成非垂直的超值,即美元几何序列。这把频道估计问题转换成几何序列参数的提取。数字结果显示,拟议的办法优于信号对噪音比率高和带宽条件大的现有算法。