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Two algorithms for neural-network design and training with application to channel equalization
C Z Sweatman1, B Mulgrew, G J Gibson
1Department of Electrical Engineering, University of Edinburgh, Edinburgh EH9 3JL, UK.
IEEE Transactions on Neural Networks
|February 7, 2008
Summary
Two novel algorithms, the linear programming slab algorithm (LPSA) and perceptron learning slab algorithm (PLSA), efficiently train neural-network classifiers for signal reconstruction and adaptive equalization, showing strong performance in simulations.
Area of Science:
- Machine Learning
- Signal Processing
- Telecommunications
Background:
- Digital signal reconstruction is challenging due to channel dispersion and noise.
- Adaptive equalization is crucial for reliable data transmission over complex channels.
Purpose of the Study:
- To introduce two new algorithms for designing and training neural-network classifiers.
- To apply these algorithms for adaptive equalization of a 4-quadrature amplitude modulation (QAM) channel.
Main Methods:
- Developed the linear programming slab algorithm (LPSA) using linear programming for multilayer perceptron (MLP) parameter identification.
- Developed the perceptron learning slab algorithm (PLSA) using an error-correction approach to reduce computational costs.
- Exploited constrained parameter spaces and classification problem symmetry in both algorithms.
Main Results:
- Both LPSA and PLSA demonstrated effectiveness in adaptive equalization procedures.
- Simulations compared algorithm performance on stationary and time-varying channels (COST 207 GSM model).
Conclusions:
- The developed algorithms offer efficient methods for neural-network classifier training.
- These algorithms are suitable for adaptive equalization tasks in digital communication systems.
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