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

124 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
JournalIEEE Transactions on Wireless Communications
Volume19
Issue number4
DOIs
Publication statusPublished - Apr 2020
MoE publication typeA1 Journal article-refereed

Funding

This work was supported in part by the Academy of Finland under Grant 319759, Grant 319758, and Grant 289611, in part by the National Key Research and Development Program of China under Grant 2017YFB1301003, in part by the National Natural Science Foundation of China under Grant 61701439 and Grant 61731002, in part by the Zhejiang Key Research and Development Plan under Grant 2019C01002, in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant 18KK0279, Grant 18K18036, and Grant 19H04092, and in part by the Telecommunications Advanced Foundation.

Keywords

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

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