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arXiv · 2408.15767

Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity

Daniel Plabst·Tobias Prinz·Francesca Diedolo·Thomas Wiegart·Georg Böcherer·Norbert Hanik·Gerhard Kramer
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Abstract

Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are combined with successive interference cancellation (SIC) to approach the information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains.

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Daniel Plabst, Tobias Prinz, Francesca Diedolo, Thomas Wiegart, Georg Böcherer, Norbert Hanik, Gerhard Kramer. 2024-08-28. Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity. https://arxiv.org/abs/2408.15767

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