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Hybrid Beamforming Design and Resource Allocation for UAV-aided Wireless-Powered Mobile Edge Computing Networks with NOMA

Feng, W; Tang, J; Zhao, N; Zhang, X; Wang, X; Wong, KK; Chambers, J; (2021) Hybrid Beamforming Design and Resource Allocation for UAV-aided Wireless-Powered Mobile Edge Computing Networks with NOMA. IEEE Journal on Selected Areas in Communications 10.1109/JSAC.2021.3091158. (In press). Green open access

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Abstract

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

Type: Article
Title: Hybrid Beamforming Design and Resource Allocation for UAV-aided Wireless-Powered Mobile Edge Computing Networks with NOMA
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSAC.2021.3091158
Publisher version: https://dx.doi/10.1109/JSAC.2021.3091158
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10133332
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