Blind Separation for Wireless Communication Convolutive Mixtures Based on Denoising IVA

Abstract

In wireless communication systems, signal transmission through a channel can not avoid the influence of noise. When it reaches the receiver, it is accompanied by time delay and attenuation. Therefore, the observed mixture signals at the receiver are convolutional mixed signals with noise contamination. To solve the problem of traditional frequency-domain based convolutive blind signal separation methods have poor separation performance for convolutional mixed signals with noise, this paper propose a denoise-FastIVA method to separate convolutional mixed signals with noise. The basic principle is to use a wavelet transform to denoise the observation signal, reduce the effect of noise on the separation effect of the algorithm, and enhance the robustness of the fast fixed-point independent vector analysis (FastIVA) separation algorithm to noise. Simulation experiments show the effectiveness of a denoise-FastIVA, under the condition that the baseband signal of binary phase shift keying (BPSK) and binary frequency shift keying (2FSK) signal modulation signal is 10 bits respectively,the separation accuracy of linear frequency modulization (LFM) has increased from 87% to more than 94%; BPSK has risen from 83% to over 97%; 2FSK has improved from 81% to over 95%. When the SNR is greater than 10 dB, the separation similarity of denoise-FastIVA for the communication mixed signals is more than 90%, and the demixing signal can demodulate the baseband signal completely and correctly. Baseband signals of experimental BPSK and 2FSK signal modulation signals are 100 bits respectively. When the signal-to-noise ratio is greater than 5dB, the signal separated by denoise-FastIVA method has the highest similarity coefficient with the source signal, and the bit error rate (BER) of the separated BPSK signal and the separated 2FSK signal are the lowest, compared with the traditional frequency domain demixing method and FastIVA algorithm.