Applying Neural Networks in Optical Communication Systems: Possible Pitfalls

Abstract

We investigate the risk of overestimating the performance gain when applying neural network based receivers in systems with pseudo random bit sequences or with limited memory depths, resulting in repeated short patterns. We show that with such sequences, a large artificial gain can be obtained which comes from pattern prediction rather than predicting or compensating the studied channel/phenomena.

References in corpus (1)

• Sequence to Sequence Learning with Neural Networks

Cited by in corpus (2)

• End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
• Fiber-induced nonlinearity compensation in coherent optical systems by affinity propagation soft-clustering