Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state...
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Autor*in:	Wenhao Sun [verfasserIn] Yuchen He [verfasserIn] Tianfeng Yan [verfasserIn] Zhongdong Wu [verfasserIn] Yide Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Deep JTROCS deep neural network neural network training deep learning

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 13(2024), 5, p 831
Übergeordnetes Werk:	volume:13 ; year:2024 ; number:5, p 831

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics13050831

Katalog-ID:	DOAJ091269067

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520			\|a Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
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allfieldsGer	10.3390/electronics13050831 doi (DE-627)DOAJ091269067 (DE-599)DOAJ0982a34e46bc44da849de4761680f3fd DE-627 ger DE-627 rakwb eng TK7800-8360 Wenhao Sun verfasserin aut Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method. Deep JTROCS deep neural network neural network training deep learning Electronics Yuchen He verfasserin aut Tianfeng Yan verfasserin aut Zhongdong Wu verfasserin aut Yide Ma verfasserin aut In Electronics MDPI AG, 2013 13(2024), 5, p 831 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:13 year:2024 number:5, p 831 https://doi.org/10.3390/electronics13050831 kostenfrei https://doaj.org/article/0982a34e46bc44da849de4761680f3fd kostenfrei https://www.mdpi.com/2079-9292/13/5/831 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2024 5, p 831
allfieldsSound	10.3390/electronics13050831 doi (DE-627)DOAJ091269067 (DE-599)DOAJ0982a34e46bc44da849de4761680f3fd DE-627 ger DE-627 rakwb eng TK7800-8360 Wenhao Sun verfasserin aut Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method. Deep JTROCS deep neural network neural network training deep learning Electronics Yuchen He verfasserin aut Tianfeng Yan verfasserin aut Zhongdong Wu verfasserin aut Yide Ma verfasserin aut In Electronics MDPI AG, 2013 13(2024), 5, p 831 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:13 year:2024 number:5, p 831 https://doi.org/10.3390/electronics13050831 kostenfrei https://doaj.org/article/0982a34e46bc44da849de4761680f3fd kostenfrei https://www.mdpi.com/2079-9292/13/5/831 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2024 5, p 831
language	English
source	In Electronics 13(2024), 5, p 831 volume:13 year:2024 number:5, p 831
sourceStr	In Electronics 13(2024), 5, p 831 volume:13 year:2024 number:5, p 831
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deep JTROCS deep neural network neural network training deep learning Electronics
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authorswithroles_txt_mv	Wenhao Sun @@aut@@ Yuchen He @@aut@@ Tianfeng Yan @@aut@@ Zhongdong Wu @@aut@@ Yide Ma @@aut@@
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callnumber-first	T - Technology
author	Wenhao Sun
spellingShingle	Wenhao Sun misc TK7800-8360 misc Deep JTROCS misc deep neural network misc neural network training misc deep learning misc Electronics Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools
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topic_title	TK7800-8360 Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools Deep JTROCS deep neural network neural network training deep learning
topic	misc TK7800-8360 misc Deep JTROCS misc deep neural network misc neural network training misc deep learning misc Electronics
topic_unstemmed	misc TK7800-8360 misc Deep JTROCS misc deep neural network misc neural network training misc deep learning misc Electronics
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title	Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools
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title_full	Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools
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title_sort	training of deep joint transmitter-receiver optimized communication system without auxiliary tools
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title_auth	Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools
abstract	Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
abstractGer	Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
abstract_unstemmed	Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
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title_short	Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools
url	https://doi.org/10.3390/electronics13050831 https://doaj.org/article/0982a34e46bc44da849de4761680f3fd https://www.mdpi.com/2079-9292/13/5/831 https://doaj.org/toc/2079-9292
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Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

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