Abstract
The fast fixed-point independent component analysis (ICA) algorithm has been widely used in various applications because of its fast convergence and superior performance. However, in a highly dynamic environment, real-time adaptation is necessary to track the variations of the mixing matrix. In this scenario, the gradient-based online learning algorithm performs better, but its convergence is slow, and depends on a proper choice of convergence factor. This paper develops a gradient-based optimum block adaptive ICA algorithm (OBA/ICA) that combines the advantages of the two algorithms. Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions, which is particularly useful in mobile communications.
Original language | American English |
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Journal | EURASIP Journal on Applied Signal Processing |
Volume | 2006 |
State | Published - Feb 18 2006 |
Keywords
- telecommunication
- mobile communication
- independent component analysis (ICA) algorithm
- online learning algorithm
- optimum block adaptive ICA algorithm
Disciplines
- Digital Communications and Networking
- Electrical and Computer Engineering