A Reinforcement Learning Method for Joint Mode Selection and Power Adaptation in the V2V Communication Network in 5G

Di Zhao, Hao Qin, Bin Song, Yanli Zhang, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V) communication technology, and V2V communication in 5G has recently attracted great interest. In the V2V communication network, users can choose different transmission modes and power levels for communication, to guarantee their quality-of-service (QoS), high capacity of vehicle-to-infrastructure (V2I) links and ultra-reliability of V2Vlinks. Aiming atV2V communication mode selection and power adaptation in 5G communication networks, a reinforcement learning (RL) framework based on slow fading parameters and statistical information is proposed. In this paper, our objective is to maximize the total capacity of V2I links while guaranteeing the strict transmission delay and reliability constraints of V2V links. Considering the fast channel variations and the continuous-valued state in a high mobility vehicular environment, we use a multi-agent double deep Q-learning (DDQN) algorithm. Each V2V link is considered as an agent, learning the optimal policy with the updated Q-network by interacting with the environment. Experiments verify the convergence of our algorithm. The simulation results show that the proposed scheme can significantly optimize the total capacity of the V2I links and ensure the latency and reliability requirements of the V2V links.

Original languageEnglish
Article number9046279
Pages (from-to)452-463
Number of pages12
JournalIEEE Transactions on Cognitive Communications and Networking
Volume6
Issue number2
DOIs
StatePublished - Jun 2020

Keywords

  • 5G
  • V2V
  • mode selection
  • power adaptation
  • reinforcement learning

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