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 chan...

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Main Authors: Chao, Xu, Thien, Van Luong, Luping, Xiang, Shinya, Sugiura, Robert, G. Maunder, Lie-Liang, Yang, Lajos, Hanzo
Format: Bài trích
Language:English
Published: IEEE 2022
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Online Access:https://ieeexplore.ieee.org/document/9759489/keywords#keywords
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5769
https://doi.org/10.1109/tccn.2022.3168725
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spelling oai:localhost:PNK-57692022-08-17T05:54:54Z Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems Chao, Xu Thien, Van Luong Luping, Xiang Shinya, Sugiura Robert, G. Maunder Lie-Liang, Yang Lajos, Hanzo Decoding Channel coding 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. 2022-05-05T07:26:22Z 2022-05-05T07:26:22Z 2022 Bài trích https://ieeexplore.ieee.org/document/9759489/keywords#keywords https://dlib.phenikaa-uni.edu.vn/handle/PNK/5769 https://doi.org/10.1109/tccn.2022.3168725 en IEEE
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Decoding
Channel coding
spellingShingle Decoding
Channel coding
Chao, Xu
Thien, Van Luong
Luping, Xiang
Shinya, Sugiura
Robert, G. Maunder
Lie-Liang, Yang
Lajos, Hanzo
Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
description 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.
format Bài trích
author Chao, Xu
Thien, Van Luong
Luping, Xiang
Shinya, Sugiura
Robert, G. Maunder
Lie-Liang, Yang
Lajos, Hanzo
author_facet Chao, Xu
Thien, Van Luong
Luping, Xiang
Shinya, Sugiura
Robert, G. Maunder
Lie-Liang, Yang
Lajos, Hanzo
author_sort Chao, Xu
title Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
title_short Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
title_full Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
title_fullStr Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
title_full_unstemmed Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems
title_sort turbo detection aided autoencoder for multi-carrier wireless systems: integrating deep learning into channel coded systems
publisher IEEE
publishDate 2022
url https://ieeexplore.ieee.org/document/9759489/keywords#keywords
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5769
https://doi.org/10.1109/tccn.2022.3168725
_version_ 1751856282168459264
score 8.887836