Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations

TL;DR

This paper introduces four new blind source separation algorithms using hyperbolic and Givens rotations tailored for high-order QAM signals, demonstrating improved performance over existing methods in various metrics.

Contribution

The paper presents novel iterative BSS algorithms employing hyperbolic and Givens rotations specifically designed for moderate and high-order QAM constellations, with enhanced performance for small sample sizes.

Findings

Algorithms outperform existing batch BSS methods in SNR, SER, and convergence.

Hyperbolic and Givens rotations improve separation quality, especially with limited data.

Proposed methods are effective for both moderate and high-order QAM signals.

Abstract

This paper addresses the problem of blind demixing of instantaneous mixtures in a multiple-input multiple-output communication system. The main objective is to present efficient blind source separation (BSS) algorithms dedicated to moderate or high-order QAM constellations. Four new iterative batch BSS algorithms are presented dealing with the multimodulus (MM) and alphabet matched (AM) criteria. For the optimization of these cost functions, iterative methods of Givens and hyperbolic rotations are used. A pre-whitening operation is also utilized to reduce the complexity of design problem. It is noticed that the designed algorithms using Givens rotations gives satisfactory performance only for large number of samples. However, for small number of samples, the algorithms designed by combining both Givens and hyperbolic rotations compensate for the ill-whitening that occurs in this case and thus improves the performance. Two algorithms dealing with the MM criterion are presented for moderate order QAM signals such as 16-QAM. The other two dealing with the AM criterion are presented for high-order QAM signals. These methods are finally compared with the state of art batch BSS algorithms in terms of signal-to-interference and noise ratio, symbol error rate and convergence rate. Simulation results show that the proposed methods outperform the contemporary batch BSS algorithms.