Hiển thị ở chế độ nhân viên: 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 chan...

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Tác giả chính:	Chao, Xu, Thien, Van Luong, Luping, Xiang, Shinya, Sugiura, Robert, G. Maunder, Lie-Liang, Yang, Lajos, Hanzo
Định dạng:	Bài trích
Ngôn ngữ:	English
Nhà xuất bản:	IEEE 2022
Chủ đề:	Decoding Channel coding
Truy cập trực tuyến:	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
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score	8.889492

Turbo Detection Aided Autoencoder for Multi-Carrier Wireless Systems: Integrating Deep Learning into Channel Coded Systems

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