Abstract
Improving bit error rates in optical communication systems is a difficult and important problem. Error detection and correction must take place at high speed, and be extremely accurate. Also, different communication channels have different characteristics, and those characteristics may change over time. We show the feasibility of using simple artificial neural networks to address these problems, and examine the effect of using different representations of signal waveforms on the accuracy of error correction. The results we have obtained lead us to the conclusion that a machine learning system based on these principles can improve on the performance of existing error correction hardware at the speed required, whilst being able to adapt to suit the characteristics of different communication channels.KeywordsError correctionclassificationoptical communicationadaptive signal processing
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