On Recurrent Neural Networks for Sequence-based Processing in Communications

24 May 2019Daniel TandlerSebastian DörnerSebastian CammererStephan ten Brink

In this work, we analyze the capabilities and practical limitations of neural networks (NNs) for sequence-based signal processing which can be seen as an omnipresent property in almost any modern communication systems. In particular, we train multiple state-of-the-art recurrent neural network (RNN) structures to learn how to decode convolutional codes allowing a clear benchmarking with the corresponding maximum likelihood (ML) Viterbi decoder... (read more)

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