Adaptive Equalisation using Neural Networks

  • Sheng Chen


Adaptive signal processing plays a crucial role in many modern communication systems. A particular example of adaptive signal processing is known as adaptive equalisation, which is an important technique for combatting distortion and interference in communication links. The conventional approach to communication channel equalisation is based on adaptive linear system theory. In the past few years, artificial neural networks have been applied to this important area of signal processing. The results obtained have indicated that the neural network approach offers superior performance over the conventional equalisation approach. This chapter continues this theme and investigates the application of neural networks to adaptive channel equalisation with the emphasis on what a neural network attempts to achieve and why it outperforms a conventional equaliser. This allows us to choose the most appropriate neural network structure for the task of equalisation. Issues of adaptive implementation and computational complexity are also addressed.


Less Mean Square Radial Basis Function Network Volterra Series Bayesian Decision Less Mean Square Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Sheng Chen
    • 1
  1. 1.Department of Electrical EngineeringUniversity of EdinburghEdinburghUK

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