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Actor-critic learning-based energy optimization for UAV access and backhaul networks
In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both b...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550566/ https://www.ncbi.nlm.nih.gov/pubmed/34777489 http://dx.doi.org/10.1186/s13638-021-01960-0 |
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author | Yuan, Yaxiong Lei, Lei Vu, Thang X. Chatzinotas, Symeon Sun, Sumei Ottersten, Björn |
author_facet | Yuan, Yaxiong Lei, Lei Vu, Thang X. Chatzinotas, Symeon Sun, Sumei Ottersten, Björn |
author_sort | Yuan, Yaxiong |
collection | PubMed |
description | In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL. |
format | Online Article Text |
id | pubmed-8550566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-85505662021-11-10 Actor-critic learning-based energy optimization for UAV access and backhaul networks Yuan, Yaxiong Lei, Lei Vu, Thang X. Chatzinotas, Symeon Sun, Sumei Ottersten, Björn EURASIP J Wirel Commun Netw Research In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL. Springer International Publishing 2021-04-07 2021 /pmc/articles/PMC8550566/ /pubmed/34777489 http://dx.doi.org/10.1186/s13638-021-01960-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Yuan, Yaxiong Lei, Lei Vu, Thang X. Chatzinotas, Symeon Sun, Sumei Ottersten, Björn Actor-critic learning-based energy optimization for UAV access and backhaul networks |
title | Actor-critic learning-based energy optimization for UAV access and backhaul networks |
title_full | Actor-critic learning-based energy optimization for UAV access and backhaul networks |
title_fullStr | Actor-critic learning-based energy optimization for UAV access and backhaul networks |
title_full_unstemmed | Actor-critic learning-based energy optimization for UAV access and backhaul networks |
title_short | Actor-critic learning-based energy optimization for UAV access and backhaul networks |
title_sort | actor-critic learning-based energy optimization for uav access and backhaul networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550566/ https://www.ncbi.nlm.nih.gov/pubmed/34777489 http://dx.doi.org/10.1186/s13638-021-01960-0 |
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