Title
Separation Of Complex Signals With Known Source Distributions In Time-Varying Channels Using Optimum Complex Block Adaptive Ica
Abstract
This paper presents a novel realization of the Complex Block Adaptive Independent Component Analysis algorithm. The algorithm optimally updates the real and imaginary components of the weight vector independently. The new implementation is employed for the separation of complex signals with known source distributions, a scenario frequently encountered in practice. Under time-varying channel conditions, the performance of the proposed method is compared with the widely known Complex Fast-ICA. Simulation results show that this new technique exhibits superior performance in time varying channel conditions in terms of convergence speed. In addition, the performance of the proposed method is independent of the processing block length and is achieved without any additional cost in computational complexity. © 2007 IEEE.
Publication Date
12-1-2007
Publication Title
Midwest Symposium on Circuits and Systems
Number of Pages
361-364
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/MWSCAS.2007.4488606
Copyright Status
Unknown
Socpus ID
51449120488 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/51449120488
STARS Citation
Ranganathan, Raghuram; Yang, Thomas T.; and Mikhael, Wasfy B., "Separation Of Complex Signals With Known Source Distributions In Time-Varying Channels Using Optimum Complex Block Adaptive Ica" (2007). Scopus Export 2000s. 6077.
https://stars.library.ucf.edu/scopus2000/6077