Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2017-11-24Online Publication Date
2017-11-24Print Publication Date
2018-04-01Permanent link to this record
http://hdl.handle.net/10754/626225
Metadata
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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.Citation
Shah SAW, Abed-Meraim K, Al-Naffouri TY (2017) Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations. IEEE Transactions on Signal Processing: 1–1. Available: http://dx.doi.org/10.1109/TSP.2017.2777392.Sponsors
King Abdullah University of Science and Technology[OSR-2016-KKI-2899]arXiv
1506.06650Additional Links
http://ieeexplore.ieee.org/document/8119869/ae974a485f413a2113503eed53cd6c53
10.1109/TSP.2017.2777392