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

Seoyoung Yu; Yun Jae Jeong; Jeong Woo Lee

doi:10.1109/ICOIN50884.2021.9333953

2021 International Conference on Information Networking (ICOIN)

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

Year: 2021, Pages: 712-714

DOI Bookmark: 10.1109/ICOIN50884.2021.9333953

Authors

Seoyoung Yu, Chung-Ang University,School of Electrical and Electronics Engineering,Seoul,Republic of Korea
Yun Jae Jeong, Chung-Ang University,School of Electrical and Electronics Engineering,Seoul,Republic of Korea
Jeong Woo Lee, Chung-Ang University,School of Electrical and Electronics Engineering,Seoul,Republic of Korea

Abstract

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.

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

Authors

Abstract

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