A Stacked-Autoencoder Based End-to-End Learning Framework for Decode-and-Forward Relay Networks

Ankit Gupta, Mathini Sellathurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we study an end-to-end deep learning (DL)based constellation design for decode-and-forward (DF) relay network. Firstly, we study both the one-way (OW) and two-way (TW) relaying by interpreting DF relay networks as stacked autoencoders, under Rayleigh fading channels, leading to a performance improvement of 0.5 dB for TWDF networks. Secondly by introducing redundant bits in transmission and reception, we design end-to-end DL-based framework similar to the differential coded modulation for OWDF and coded modulation for TWDF relay networks, under block fading Rayleigh channels and achieve performance gain of 2 dB and 1 dB over conventional method, without using the channel state information knowledge in OWDF networks.
Original languageEnglish
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages5245-5249
ISBN (Electronic)978-1-5090-6631-5
DOIs
Publication statusE-pub ahead of print - 14 May 2020
Event45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020
https://2020.ieeeicassp.org/

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (Electronic)2379-190X

Conference

Conference45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020
Abbreviated titleICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20
Internet address

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  • Cite this

    Gupta, A., & Sellathurai, M. (2020). A Stacked-Autoencoder Based End-to-End Learning Framework for Decode-and-Forward Relay Networks. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5245-5249). (IEEE International Conference on Acoustics, Speech and Signal Processing). https://doi.org/10.1109/ICASSP40776.2020.9054512