Learning-Based Multi-Channel Access in 5G and beyond Networks with Fast Time-Varying Channels

Wang, S; Lv, T; Zhang, X; Lin, Z; Huang, P

Learning-Based Multi-Channel Access in 5G and beyond Networks with Fast Time-Varying Channels

Wang, S Lv, T Zhang, X Lin, Z

Huang, P

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2020, 69, (5), pp. 5203-5218
Issue Date:: 2020-05-01

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Lv, T
dc.contributor.author	Zhang, X
dc.contributor.author	Lin, Z https://orcid.org/0000-0001-5941-2163
dc.contributor.author	Huang, P
dc.date.accessioned	2021-02-25T20:39:53Z
dc.date.available	2021-02-25T20:39:53Z
dc.date.issued	2020-05-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2020, 69, (5), pp. 5203-5218
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/146472
dc.description.abstract	© 1967-2012 IEEE. We propose a learning-based scheme to investigate the dynamic multi-channel access (DMCA) problem in the fifth generation (5G) and beyond networks with fast time-varying channels wherein the channel parameters are unknown. The proposed learning-based scheme can maintain near-optimal performance for a long time, even in the sharp changing channels. This scheme greatly reduces processing delay, and effectively alleviates the error due to decision lag, which is cased by the non-immediacy of the information acquisition and processing. We first propose a psychology-based personalized quality of service model after introducing the network model with unknown channel parameters and the streaming model. Then, two access criteria are presented for the living streaming model and the buffered streaming model. Their corresponding optimization problems are also formulated. The optimization problems are solved by learning-based DMCA scheme, which combines the recurrent neural network with deep reinforcement learning. In the learning-based DMCA scheme, the agent mainly invokes the proposed prediction-based deep deterministic policy gradient algorithm as the learning algorithm. As a novel technical paradigm, our scheme has strong universality, since it can be easily extended to solve other problems in wireless communications. The real channel data-based simulation results validate that the performance of the learning-based scheme approaches that derived from the exhaustive search when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision at every few time-slots.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2020.2980861
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	Learning-Based Multi-Channel Access in 5G and beyond Networks with Fast Time-Varying Channels
dc.type	Journal Article
utslib.citation.volume	69
utslib.for	0805 Distributed Computing
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-25T20:39:50Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	69
utslib.citation.issue	5

Abstract:

© 1967-2012 IEEE. We propose a learning-based scheme to investigate the dynamic multi-channel access (DMCA) problem in the fifth generation (5G) and beyond networks with fast time-varying channels wherein the channel parameters are unknown. The proposed learning-based scheme can maintain near-optimal performance for a long time, even in the sharp changing channels. This scheme greatly reduces processing delay, and effectively alleviates the error due to decision lag, which is cased by the non-immediacy of the information acquisition and processing. We first propose a psychology-based personalized quality of service model after introducing the network model with unknown channel parameters and the streaming model. Then, two access criteria are presented for the living streaming model and the buffered streaming model. Their corresponding optimization problems are also formulated. The optimization problems are solved by learning-based DMCA scheme, which combines the recurrent neural network with deep reinforcement learning. In the learning-based DMCA scheme, the agent mainly invokes the proposed prediction-based deep deterministic policy gradient algorithm as the learning algorithm. As a novel technical paradigm, our scheme has strong universality, since it can be easily extended to solve other problems in wireless communications. The real channel data-based simulation results validate that the performance of the learning-based scheme approaches that derived from the exhaustive search when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision at every few time-slots.

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http://hdl.handle.net/10453/146472