超宽带信道建模中基于压缩感知的解卷积算法
doi: 10.3724/SP.J.1146.2011.00567
A Deconvolution Algorithm for Ultra Wideband Channel Modeling Based on Compressive Sensing
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摘要: 针对频域测量方式下的超宽带(UWB)信道测量数据后处理，该文提出了用具有高斯滚降特性过渡带的类高斯窗，提取符合中国UWB频谱规范的信道测量数据，并将类高斯窗对应的时域脉冲作为先验信息，使用基于压缩感知(CS)的算法对时域信道测量信号解卷积，使得解卷积后的信道冲激响应具有高分辨率特性。利用频域加窗补零，以及改变解卷积算法中参数化波形字典原子的步长，可以得到不同分辨率的解卷积结果。采用匹配追踪(Matching Pursuit, MP)算法作为CS的重构算法。针对一间办公室的视距(LOS)与非视距(NLOS)信道测量数据处理结果表明，基于压缩感知的解卷积算法可以用较少的观测值获得和CLEAN算法相近的解卷积性能。Abstract: A deconvolution algorithm based on Compressive Sensing (CS) is proposed for the post-processing of Ultra WideBand (UWB) channel modeling using frequency-domain measurements. A window with Gaussian transition band is used to extract the measurements according to the UWB frequency regulation policy of China. The time-domain waveform of the quasi-Gaussian window is used as the apriori information of the CS based deconvolution algorithm. The deconvolution results are with high-resolution characteristic. Furthermore, flexible zero-padding of windowing and the design of parameterized waveform dictionary lead to different resolutions of the deconvolution results. Matching Pursuit (MP) algorithm is used as the reconstruction algorithm of CS. Both LOS and NLOS measurements of offices are exploited to demonstrate that the proposed CS based deconvolution algorithm can achieve comparable performance with CLEAN algorithm using fewer samples.
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