Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Xianfu Chen, Celimuge Wu, Tao Chen, Honggang Zhang, Zhi Liu (Corresponding Author), Yan Zhang, Mehdi Bennis

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.

Original languageEnglish
Article number8954939
Pages (from-to)2268-2281
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume19
Issue number4
DOIs
Publication statusPublished - Apr 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • deep reinforcement learning
  • long short-term memory
  • Markov decision process
  • multi-user resource scheduling
  • Q-function decomposition
  • Vehicular communications

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