WSDM中卷积混合模型下的一种时域快速分离算法

摘要

无线信道统计复用(Wireless Statistic Division Multiplexing,以下简称WSDM)是一种允许在无线信道上同时同频传输多路信号的复用技术.针对卷积混合模型下的WSDM技术运用,提出了一种时域快速分离算法.算法首先利用时域盲解卷积算法,将带噪声的卷积混合模型转化为无噪声的线性瞬时混合模型,然后根据得到的线性瞬时混合模型,采用一种基于峭度差值变化的变步长等变自适应分解(EASI)算法分离混合信号.仿真结果表明,与传统的固定步长算法和变步长算法相比,所提算法在卷积混合模型下拥有更快的收敛速度和更准确的信号分离效果.%Abstrac: WSDM (Wireless statistic division multiplexing) is a multiplexing technology that can transmit multiple signals simultaneously in the same frequency band over wireless channels. Aiming at the convolutive mixture model in WSDM, a time-domian fast separation algorithm is proposed. This algorithm firstly transforms noisy convolutive mixture into a noiseless instantaneous linear mixture by employing a time-domian blind deconvolution method, then based on the obtained linear instantaneous mixture, the sources are recovered by a modified variable step-size EASI(Equivariant Adaptive Separation via Independence) algorithm using the difference value of kurtosis to adjust step-size. Simulation results show that the proposed separation algorithm has faster convergence rate and better separation performance than the conventional fixed step-size and the classical variable step-size algorithm under the convolutive mixture model.