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 MC-AE over the conventional OFDM, we map the MC-AE's input-output 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.
Orthogonal frequency-division multiplexing, auto-encoder, binary convolutional coding, deep learning, deep neural network, low density parity check, mutual information, turbo detection
600-614
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
1 June 2022
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, 8 (2), .
(doi:10.1109/TCCN.2022.3168725).
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 MC-AE over the conventional OFDM, we map the MC-AE's input-output 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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Turbo_MC_AE_Accept_two_col
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Accepted/In Press date: 13 April 2022
Published date: 1 June 2022
Additional Information:
Funding Information:
We would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/P034284/1 and EP/P003990/1 (COALESCE) as well as of the European Research Council-s Advanced Fellow Grant QuantCom (Grant No. 789028) The work of Luping Xiang is supported by International Postdoctoral Exchange Fellowship Program (YJ20210244). The work of S. Sugiura was supported in part by the Japan Science and Technology Agency (JST) Precursory Research for Embryonic Science and Technology (PRESTO) under Grant JPMJPR1933.
Publisher Copyright:
© 2015 IEEE.
Keywords:
Orthogonal frequency-division multiplexing, 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
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Date deposited: 13 May 2022 16:41
Last modified: 12 Nov 2024 02:46
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