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Nonlinear channel equalization for QAM signal constellation using artificial neural networks
J C Patra1, R N Pal, R Baliarsingh
1Dept. of Appl. Electron., Regional Eng. Coll., Rourkela.
Summary
A novel functional link artificial neural network (FLANN) effectively performs adaptive channel equalization for digital communication systems. This computationally efficient method surpasses traditional equalizers in handling nonlinear channels.
Area of Science:
- Digital Communications
- Artificial Intelligence
- Signal Processing
Background:
- Adaptive channel equalization is crucial for reliable digital communication.
- Artificial neural networks (ANNs) offer potential for complex equalization tasks.
- Existing methods like LMS may struggle with nonlinear channel distortions.
Purpose of the Study:
- To propose and evaluate a novel, computationally efficient single-layer functional link artificial neural network (FLANN) for adaptive channel equalization.
- To investigate the FLANN's capability in handling nonlinear channel equalization for 4-QAM systems.
- To compare the FLANN's performance against other ANNs and conventional equalizers.
Main Methods:
- A single-layer functional link artificial neural network (FLANN) was designed, introducing nonlinearity via trigonometric polynomial expansion.
- The FLANN was applied to channel equalization, treating it as a nonlinear classification problem.
- Performance was evaluated against multilayer perceptron (MLP), polynomial perceptron network (PPN), and linear LMS equalizers under various channel models.
Main Results:
- The FLANN demonstrated effective nonlinear channel equalization capabilities.
- Performance was compared across different ANN structures and channel models.
- The impact of the eigenvalue ratio (EVR) on equalizer performance was analyzed.
- Computational complexity of the ANN structures was assessed.
Conclusions:
- The proposed FLANN provides an efficient and effective solution for adaptive channel equalization, particularly in nonlinear scenarios.
- FLANN's ability to form nonlinear decision boundaries makes it suitable for complex communication channels.
- The study offers insights into ANN-based equalization strategies and their comparative performance.
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