Reference : Actor‑critic learning‑based energy optimization for UAV access and backhaul networks
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/47378
Actor‑critic learning‑based energy optimization for UAV access and backhaul networks
English
Yuan, Yaxiong mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Lei, Lei mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Vu, Thang Xuan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Sun, Sumei mailto [Institute for Infocomm Research, Agency for Science, Technology, and Research, Singapore]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
7-Apr-2021
EURASIP Journal on Wireless Communications and Networking
Springer
Yes
International
1687-1472
1687-1499
Germany
[en] UAV ; Deep reinforcement learning ; User scheduling ; Backhaul power allocation ; Energy optimization ; Actor-critic
[en] 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 difficul- ties for solving such a non-convex and combinatorial problem lie at the high compu- tational 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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/47378
10.1186/s13638-021-01960-0
https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-021-01960-0
FnR ; FNR12173206 > Srikanth Bommaraveni > LARGOS > Learning-assisted Optimization For Resource And Security Management In Slicing-based 5g Networks > 15/03/2018 > 14/03/2022 > 2017

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