A comparative study of complex gradient and fixed-point ICA algorithms for interference suppression in static and dynamic channels
Abbreviated Journal Title
independent component analysis; kurtosis; adaptive signal processing; INDEPENDENT COMPONENT ANALYSIS; Engineering, Electrical & Electronic
Separation of complex signals using independent component analysis (ICA) is an area of extensive research. Several gradient and fixed-point complex ICA algorithms have been proposed in this regard. In this contribution, the performance of the recently developed complex ICA with individual adaptation. (C-IA-ICA) is compared to the most recent gradient optimization KM algorithm (KM-G) and fixed-point complex fast-ICA (CF-ICA) algorithm. The algorithms are tested in interference suppression for QPSK based receivers, in both static and dynamic channel conditions. In addition, two simulation scenarios are presented. In the first case, the interferer is another QPSK signal, while in the second the interferer is a 16-QAM signal. In static conditions, the CF-ICA has the fastest convergence with high interference suppression. However, in dynamic scenarios frequently encountered in practice, its convergence speed is greatly affected. The complex IA-ICA achieves good interference suppression in both static and dynamic channels without a significant effect on its convergence speed. The KM-G, while not diverging, in both static and dynamic channel situations, is less effective in interference suppression, in contrast to the CF-ICA and C-IA-ICA which achieve acceptable interference suppression in both cases. (c) 2007 Elsevier B.V. All rights reserved.
"A comparative study of complex gradient and fixed-point ICA algorithms for interference suppression in static and dynamic channels" (2008). Faculty Bibliography 2000s. 868.