Minimum BER Criterion and Adaptive Moment Estimation Based Enhanced ICA for Wireless Communications

Zhongqiang Luo,Yan Chen,Xingzhong Xiong,Lidong Zhu,Chengjie Li

doi:10.1109/access.2020.3016989

Abstract

This paper concentrates on investigating an enhanced independent component analysis (ICA) method for blind separation of signals corrupted by noise in wireless communications. Because of the traditional classical ICA methods that always have an inadequate capacity of anti-noise or insufficient separable ability in noise circumstance without satisfying practical application requirements. For this reason, two mechanisms are conducted to establish the modified cost function and fulfill the optimization assignment in the process of constituting an enhanced ICA algorithm. This proposed algorithm can benefit tremendously from the derived minimum bit error rate (BER) criterion and the novel adaptive moment estimation (Adam) optimization approach. In this work, firstly, the novel cost function is obtained according to minimum BER fused into maximum likelihood (ML) principle-based ICA cost function. Furthermore, by utilizing Adam processing, the task of blind separation of mixed signals is implemented via optimizing this modified cost function. Lastly, theoretical analysis and experiment results corroborate the better effectiveness and robustness of the proposed enhanced ICA algorithm compared with a series of popular representative ICA methods.

Highlights

B LIND source separation (BSS) has received attractable and remarkable attention from the academic and industrial community in recent decades
Some investigators hold the view of adding the number of received sensors through noise estimation operation [1], [3], or constructing overdetermined model using principal component analysis (PCA) [7] for suppressing the influence of noise
A minimum bit error rate (BER) is derived for merging into maximum likelihood (ML) or MML independent principle for constructing a new cost function

Summary

INTRODUCTION

B LIND source separation (BSS) has received attractable and remarkable attention from the academic and industrial community in recent decades. The expected signals can be separated or extracted from the received mixed signals by the independent component analysis (ICA) algorithm based BSS theory. Some investigators hold the view of adding the number of received sensors through noise estimation operation [1], [3], or constructing overdetermined model using principal component analysis (PCA) [7] for suppressing the influence of noise. A novel algorithm called adaptive moment estimation (Adam) [19], [22] for gradient-based optimization will be exploited for improving its computational efficiency and convergence speed This algorithm can reach a better separation performance thanks to using the mechanism of the adaptive moments estimation of the gradient.

SYSTEM MODEL

MODIFIED COST FUNCTION BASED ON MINIMUM BER CRITERION

DERIVED MINIMUM BER CRITERION MERGING INTO TRADITIONAL ML BASED COST FUNCTION

Result

EXPERIMENT ANALYSIS AND DISCUSSIONS

CONCLUSIONS