Multi-Agent Power and Resource Allocation for D2D Communications: A Deep Reinforcement Learning Approach

Honglin Xiang*, Jingyi Peng, Zhen Gao, Lingjie Li, Yang Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The explosion in the number of smartphones and wearable devices brings the challenge of high achievable rate (AR) requirement, and D2D communications become the critical technology to solve this challenge. However, the co-channel interference caused by spectrum reusing and low delay requirement restrict D2D communications performance improvements. In this paper, we consider the cases of no time delay constraint and time delay constraint respectively, and design a joint power control and resource allocation scheme based on deep reinforcement learning (DRL) to maximize the AR of cellular users (CUEs) and D2D users (DUEs). Specifically, D2D pairs are considered multiple agents for reusing CUE spectrum, each agent can independently select spectrum resources and power without any prior information to ease interference. Furthermore, a double deep Q-network with priority sampling (Pr-DDQN) distributed algorithm is proposed, which helps agents to learn more dominant features during experience replay. Simulation results indicate that Pr-DDQN algorithm can obtain a higher AR than the present DRL algorithms. In particular, the probability of selecting low power of agents enlarges as the increase of the remaining transmission time, which demonstrates that the agents can successfully learn and perceive the implicit relationship of time delay constraint.

Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454681
DOIs
Publication statusPublished - 2022
Event96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom
Duration: 26 Sept 202229 Sept 2022

Publication series

NameIEEE Vehicular Technology Conference
Volume2022-September
ISSN (Print)1550-2252

Conference

Conference96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/TerritoryUnited Kingdom
CityLondon
Period26/09/2229/09/22

Keywords

  • Device-to-device communications
  • deep reinforcement learning
  • power control
  • resource allocation

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