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Turbo detection aided autoencoder for multi-carrier wireless systems: Integrating deep learning into channel coded systems

Turbo detection aided autoencoder for multi-carrier wireless systems: Integrating deep learning into channel coded systems
Turbo detection aided autoencoder for multi-carrier wireless systems: Integrating deep learning into channel coded systems
A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations
concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems,
which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the channel coding arrangement and of the
channel model, where the output layer of the MC-AE decoder is designed for both accepting and producing reliable soft-bit decisions. Owing to the fact that bit-dependency is imposed by the MC-AE mapping, our bespoke MC-AE decoder becomes
capable of achieving a beneficial iteration gain, when the extrinsic information is exchanged between the soft-decision MC-AE decoder and the soft-decision channel decoder. Secondly, in order to be able to interpret the performance advantages of our MCAE over the conventional OFDM, we map the MC-AE’s inputoutput relationship to an equivalent model-based representation. The associated theoretical analysis verifies the fact that during the process of data-driven signal reconstruction across OFDM’s
subcarriers, a beneficial frequency diversity gain is achieved by the proposed MC-AE design. Finally, our simulation results demonstrate that the MC-AE is capable of achieving substantial performance advantages over both conventional OFDM and
OFDM based index modulation (OFDM-IM) in channel coded systems.
Channel coding, Decoding, Deep learning, Mutual information, OFDM, Orthogonal frequency-division multiplexing, Training, Wireless communication, auto-encoder, binary convolutional coding, deep learning, deep neural network, low density parity check, mutual information., turbo detection
2332-7731
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Luong, Thien V
a15fc6c2-8387-4e11-aae6-64553eb9770c
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Sugiura, Shinya
4c8665dd-1ad8-4dc0-9298-bf04eded3579
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Luong, Thien V
a15fc6c2-8387-4e11-aae6-64553eb9770c
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Sugiura, Shinya
4c8665dd-1ad8-4dc0-9298-bf04eded3579
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Xu, Chao, Luong, Thien V, Xiang, Luping, Sugiura, Shinya, Maunder, Robert, Yang, Lie-Liang and Hanzo, Lajos (2022) Turbo detection aided autoencoder for multi-carrier wireless systems: Integrating deep learning into channel coded systems. IEEE Transactions on Cognitive Communications and Networking. (doi:10.1109/TCCN.2022.3168725).

Record type: Article

Abstract

A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations
concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems,
which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the channel coding arrangement and of the
channel model, where the output layer of the MC-AE decoder is designed for both accepting and producing reliable soft-bit decisions. Owing to the fact that bit-dependency is imposed by the MC-AE mapping, our bespoke MC-AE decoder becomes
capable of achieving a beneficial iteration gain, when the extrinsic information is exchanged between the soft-decision MC-AE decoder and the soft-decision channel decoder. Secondly, in order to be able to interpret the performance advantages of our MCAE over the conventional OFDM, we map the MC-AE’s inputoutput relationship to an equivalent model-based representation. The associated theoretical analysis verifies the fact that during the process of data-driven signal reconstruction across OFDM’s
subcarriers, a beneficial frequency diversity gain is achieved by the proposed MC-AE design. Finally, our simulation results demonstrate that the MC-AE is capable of achieving substantial performance advantages over both conventional OFDM and
OFDM based index modulation (OFDM-IM) in channel coded systems.

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Accepted/In Press date: 13 April 2022
Published date: 19 April 2022
Keywords: Channel coding, Decoding, Deep learning, Mutual information, OFDM, Orthogonal frequency-division multiplexing, Training, Wireless communication, auto-encoder, binary convolutional coding, deep learning, deep neural network, low density parity check, mutual information., turbo detection

Identifiers

Local EPrints ID: 456870
URI: http://eprints.soton.ac.uk/id/eprint/456870
ISSN: 2332-7731
PURE UUID: 77248a44-067c-41f7-9d59-678c1f644d8a
ORCID for Chao Xu: ORCID iD orcid.org/0000-0002-8423-0342
ORCID for Luping Xiang: ORCID iD orcid.org/0000-0003-1465-6708
ORCID for Robert Maunder: ORCID iD orcid.org/0000-0002-7944-2615
ORCID for Lie-Liang Yang: ORCID iD orcid.org/0000-0002-2032-9327
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 13 May 2022 16:41
Last modified: 14 May 2022 01:42

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Contributors

Author: Chao Xu ORCID iD
Author: Thien V Luong
Author: Luping Xiang ORCID iD
Author: Shinya Sugiura
Author: Robert Maunder ORCID iD
Author: Lie-Liang Yang ORCID iD
Author: Lajos Hanzo ORCID iD

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