Deep learning for communication over dispersive nonlinear channels: performance and comparison with classical digital signal processing

Boris Karanov, Gabriele Liga, Vahid Aref, Domanic Lavery, Polina Bayvel, Laurent Schmalen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)
13 Downloads (Pure)

Abstract

In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IMDD) links. We consider an autoencoder based on the recently proposed sliding window bidirectional recurrent neural network (SBRNN) design to realize the transceiver for optical IMDD communication. We show that its performance can be improved by introducing a weighted sequence estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping optimization to reduce the bit-error rate (BER) of the system. Furthermore, we carry out a detailed comparison with classical schemes based on pulse-amplitude modulation and maximum likelihood sequence detection (MLSD). Our investigation shows that for a reference 42 Gb/s transmission, the SBRNN autoencoder achieves a BER performance comparable to MLSD, when both systems account for the same amount of memory. In contrast to MLSD, the SBRNN performance is achieved without incurring a computational complexity exponentially growing with the processed memory.

Original languageEnglish
Title of host publication2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages192-199
Number of pages8
ISBN (Electronic)9781728131511
DOIs
Publication statusPublished - Sep 2019
Event57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019 - Monticello, United States
Duration: 24 Sep 201927 Sep 2019

Conference

Conference57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
CountryUnited States
CityMonticello
Period24/09/1927/09/19

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