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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) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe20230907121212 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2023
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| Publish Date: | 2023-09-07 |
| Description: |
AbstractEnergy 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. see all
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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 |
| OADOI: | https://oadoi.org/10.1109/lcomm.2023.3275989 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
213 Electronic, automation and communications engineering, electronics |
| Subjects: | |
| 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.) |
| Copyright information: |
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