University of Oulu

M. A. Ouamri, G. Barb, D. Singh, A. B. M. Adam, M. S. A. Muthanna and X. Li, "Nonlinear Energy-Harvesting for D2D Networks Underlaying UAV With SWIPT Using MADQN," in IEEE Communications Letters, vol. 27, no. 7, pp. 1804-1808, July 2023, doi: 10.1109/LCOMM.2023.3275989.

Nonlinear energy-harvesting for D2D networks underlaying UAV with SWIPT using MADQN

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Author: Ouamri, Mohamed Amine1; Barb, Gordana2; Singh, Daljeet3;
Organizations: 1CNRS, LIG, INP Grenoble, Université Grenoble Alpes, Grenoble, France
2Faculty of Electronics, Telecommunication and Information Technologies, Politehnica University Timisoara, Timişoara, Romania
3Faculty of Medicine, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
4School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
5Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog, Russia
6School of Physics and Electronics Information Engineering, Henan Polytechnic University, Jiaozuo, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-07


Energy Efficiency (EE) has become an essential metric in Device-to-Device (D2D) communication underlaying Unmanned Aerial Vehicles (UAVs) Among the several technologies that provide significant energy, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve EE. However, it is a challenging task to study the EE under nonlinear energy harvesting (EH) due to the limited sensitivity and the composition of the nonlinear circuit. Moreover, when D2D users transmit information using the EH from UAVs, interferences to cellular users occur and deteriorate the throughput. To tackle these problems, we leverage concepts from artificial intelligence (AI) to optimize EE of UAV-assisted D2D communication. Specifically, multi-agent deep reinforcement learning was proposed to jointly maximize throughput and EE, where the reward function is defined in terms of the introduced goal. Simulation results verify the supremacy of proposed approach over traditional algorithms.

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Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 27
Issue: 7
Pages: 1804 - 1808
DOI: 10.1109/lcomm.2023.3275989
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This paper was financially supported by the Project ?Network of excellence in applied research and innovation for doctoral and postdoctoral programs? / InoHubDoc, project co-funded by the European Social Fund financing agreement no. (Grant Number: POCU/993/6/13/153437.)
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