Resource Allocation Scheme Based on Deep Reinforcement Learning for Device-to-Device Communications

Yu, S.; Jeong, Y.J.; Lee, J.W.

doi:10.1109/ICOIN50884.2021.9333953

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Resource Allocation Scheme Based on Deep Reinforcement Learning for Device-to-Device Communications

Yu, S.;
Jeong, Y.J.;
Lee, J.W.

Citations

WEB OF SCIENCE

10

Citations

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12

초록

In this paper, we propose a decentralized resource allocation scheme based on deep reinforcement learning designed for device-to-device communications underlay cellular networks. The proposed scheme allocates appropriate channel resource and transmit power to each D2D pairs iteratively to maximize the overall effective throughput by utilizing observation consisting of location information of mobile devices and resource allocation of the other devices.

키워드

D2D; deep reinforcement learning; effective throughput; outage probability; resource allocation; Reinforcement learning; Resource allocation; Cellular network; Channel resource; Decentralized resource allocation; Device-to-Device communications; Effective throughput; Location information; Resource allocation schemes; Transmit power; Deep learning

제목: Resource Allocation Scheme Based on Deep Reinforcement Learning for Device-to-Device Communications

저자: Yu, S.; Jeong, Y.J.; Lee, J.W.

DOI: 10.1109/ICOIN50884.2021.9333953

발행일: 2021-01

유형: Proceedings Paper

저널명: International Conference on Information Networking

권: 2021-January

페이지: 712 ~ 714

언어: ENG

출판사: IEEE Computer Society

분량: 3 페이지

ISSN: P 1976-7684