Underdetermined Blind Source Separation for linear instantaneous mixing system in the non-cooperative wireless communication

Under the condition of non-cooperative wireless communication, many signals always overlap in time-frequency field, therefore, the signal separation and reconstruction of the received mixed signals is of great significance for the subsequent information processing. A new blind separation strategy is proposed to solve the blind separation problem in non-cooperative communication under general underdetermined conditions. Firstly, based on a new double-constrained single source points (SSP) detection criterion, a fuzzy mean clustering underdetermined blind identification (UBI) algorithm is proposed which got the high precision estimation of the mixing matrix. Then a singular value membership matching underdetermined source recovery (SVMMUSR) algorithm with dynamic k sparse component analysis (kSCA) assumption is present. The singular value decomposition (SVD) method is applied to detect the membership of every sample data point with the subspace so as to obtain the optimal k-dimensional subspace matching with each data point. Subspace projection method is then used to achieve the accurate recovery of the signal for unknown k sparse conditions. Compared with other conventional methods, the simulation results indicate that the estimation performance and blind separation performance of the proposed method is better. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A new blind separation strategy is proposed to solve the problem of blind separation in non-cooperative communication under general underdetermined conditions. The strategy involves fuzzy mean clustering algorithm and singular value membership matching algorithm to achieve accurate separation and reconstruction of mixed signals.