Cover Image

Turbo Detection Aided Autoencoder for Multicarrier 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...

Full description

Saved in:
Bibliographic Details
Main Authors: Chao, Xu, Thien Van, Luong, Luping, Xiang, Shinya, Sugiura
Format: Bài trích
Language:English
Published: IEEE Transactions on Magnetics 2022
Subjects:
Orthogonal frequency-division multiplexing
Deep learning
Online Access:https://ieeexplore.ieee.org/document/9759489
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5911
https://doi.org/10.1109/tccn.2022.3168725
Tags: Add Tag
No Tags, Be the first to tag this record!
id oai:localhost:PNK-5911
record_format dspace
spelling oai:localhost:PNK-59112022-08-17T05:54:55Z Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems Chao, Xu Thien Van, Luong Luping, Xiang Shinya, Sugiura Orthogonal frequency-division multiplexing Deep learning 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. 2022-07-13T01:59:53Z 2022-07-13T01:59:53Z 2022 Bài trích https://ieeexplore.ieee.org/document/9759489 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5911 https://doi.org/10.1109/tccn.2022.3168725 en IEEE Transactions on Magnetics
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Orthogonal frequency-division multiplexing
Deep learning
spellingShingle Orthogonal frequency-division multiplexing
Deep learning
Chao, Xu
Thien Van, Luong
Luping, Xiang
Shinya, Sugiura
Turbo Detection Aided Autoencoder for Multicarrier 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
author_facet Chao, Xu
Thien Van, Luong
Luping, Xiang
Shinya, Sugiura
author_sort Chao, Xu
title Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems
title_short Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems
title_full Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems
title_fullStr Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems
title_full_unstemmed Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems
title_sort turbo detection aided autoencoder for multicarrier wireless systems: integrating deep learning into channel coded systems
publisher IEEE Transactions on Magnetics
publishDate 2022
url https://ieeexplore.ieee.org/document/9759489
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5911
https://doi.org/10.1109/tccn.2022.3168725
_version_ 1751856288113885184
score 8.887836

Similar Items