Blind separation of mutually correlated sources using precoders

IEEE Transactions on Neural Networks
Yong XiangVan Khanh Nguyen

Abstract

This paper studies the problem of blind source separation (BSS) from instantaneous mixtures with the assumption that the source signals are mutually correlated. We propose a novel approach to BSS by using precoders in transmitters. We show that if the precoders are properly designed, some cross-correlation coefficients of the coded signals can be forced to be zero at certain time lags. Then, the unique correlation properties of the coded signals can be exploited in receiver to achieve source separation. Based on the proposed precoders, a subspace-based algorithm is derived for the blind separation of mutually correlated sources. The effectiveness of the algorithm is illustrated by simulation examples.

Citations

Sep 10, 2014·IEEE Transactions on Neural Networks and Learning Systems·Zuyuan YangKan Xie
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·N TengtrairatS S Dlay
Jan 1, 2013·IEEE Transactions on Neural Networks and Learning Systems·Yong XiangSong Guo
Jan 1, 2013·IEEE Transactions on Neural Networks and Learning Systems·Zuyuan YangShengli Xie
Oct 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Zuyuan YangYue Rong
Mar 23, 2011·IEEE Transactions on Neural Networks·Jian YangHongsheng Xi
Mar 9, 2011·IEEE Transactions on Neural Networks·Guoxu ZhouJun-Mei Yang
Feb 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Shengli XieYong Xiang
Mar 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Wei Bian, Xiaojun Chen
Jul 28, 2011·IEEE Transactions on Neural Networks·Miguel AlmeidaRicardo Vigário
Jul 30, 2010·IEEE Transactions on Neural Networks·Yoshikazu WashizawaAndrzej Cichocki
Nov 26, 2010·IEEE Transactions on Neural Networks·Guoxu ZhouJun-Mei Yang

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