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

Ankit Gupta; Mathini Sellathurai

doi:10.1109/ICASSP40776.2020.9054512

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 proceeding › Conference contribution

6 Citations (Scopus)

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 language	English
Title of host publication	ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages	5245-5249
Number of pages	5
ISBN (Electronic)	978-1-5090-6631-5
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9054512
Publication status	Published - 14 May 2020
Event	45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020 - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020 https://2020.ieeeicassp.org/

Publication series

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

Conference

Conference	45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020
Abbreviated title	ICASSP 2020
Country/Territory	Spain
City	Barcelona
Period	4/05/20 → 8/05/20
Internet address	https://2020.ieeeicassp.org/

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP40776.2020.9054512

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

@inproceedings{6243446a0ead4f49bed4629c968ca4b8,

title = "A Stacked-Autoencoder Based End-to-End Learning Framework for Decode-and-Forward Relay Networks",

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.",

author = "Ankit Gupta and Mathini Sellathurai",

year = "2020",

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doi = "10.1109/ICASSP40776.2020.9054512",

language = "English",

series = "IEEE International Conference on Acoustics, Speech and Signal Processing",

pages = "5245--5249",

booktitle = "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

note = "45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",

url = "https://2020.ieeeicassp.org/",

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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). IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5245-5249, 45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020, Barcelona, Spain, 4/05/20. https://doi.org/10.1109/ICASSP40776.2020.9054512

A Stacked-Autoencoder Based End-to-End Learning Framework for Decode-and-Forward Relay Networks. / Gupta, Ankit; Sellathurai, Mathini.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020. p. 5245-5249 (IEEE International Conference on Acoustics, Speech and Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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

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