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Optimal decision boundaries for M-QAM signal formats using neural classifiers
1INFOCOM Department, Università Degli Studi Di Roma La Sapienza, 00184 Rome, Italy.
IEEE Transactions on Neural Networks
|February 7, 2008
Summary
Neural classifiers optimize decision boundaries for nonlinear M-QAM constellations. Performance is assessed via carrier-to-noise ratio degradation and pattern recognition metrics, showing effectiveness in mild nonlinearity.
Area of Science:
- Electrical Engineering
- Machine Learning
- Signal Processing
Background:
- Nonlinear distortions in M-QAM constellations degrade signal quality.
- Neural classifiers offer potential for adaptive signal equalization.
Purpose of the Study:
- To analyze neural classifiers for optimal decision boundaries in nonlinear M-QAM systems.
- To evaluate classifier performance using carrier-to-noise ratio degradation and pattern recognition metrics.
Main Methods:
- Application of neural classifiers to warped and clustered M-QAM constellations.
- Evaluation using carrier to noise ratio (CNR) degradation (DeltaC/N) for a target error rate.
- Assessment of classification confidence and generalization capability.
- Investigation of training data distribution and activation function sharpness (net temperature).
Main Results:
- Neural classifiers achieve optimal matching with theoretical upper bounds under mild nonlinearity.
- Classifier behavior is evaluated through CNR degradation and pattern recognition figures of merit.
- The influence of training data distribution and activation function sharpness was investigated.
Conclusions:
- Neural classifiers can effectively define decision boundaries for nonlinear M-QAM constellations.
- The method's effectiveness is suitable for mild nonlinearity conditions.
- Performance analysis provides insights into classifier behavior and optimization parameters.
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