An LSTM-based hybrid proximal policy optimization spectrum access algorithm in vehicular network

Abstract

Purpose

In order to meet the different quality of service (QoS) requirements of vehicle-to-infrastructure (V2I) and multiple vehicle-to-vehicle (V2V) links in vehicle networks, an efficient V2V spectrum access mechanism is proposed in this paper.

Design/methodology/approach

A long-short-term-memory-based multi-agent hybrid proximal policy optimization (LSTM-H-PPO) algorithm is proposed, through which the distributed spectrum access and continuous power control of V2V link are realized.

Findings

Simulation results show that compared with the baseline algorithm, the proposed algorithm has significant advantages in terms of total system capacity, payload delivery success rate of V2V link and convergence speed.

Originality/value

The LSTM layer uses the time sequence information to estimate the accurate system state, which ensures the choice of V2V spectrum access based on local observation effective. The hybrid PPO framework shares training parameters among agents which speeds up the entire training process. The proposed algorithm adopts the mode of centralized training and distributed execution, so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.

Citation

Kang, L., Chen, J., Wang, J. and Wei, Y. (2024), "An LSTM-based hybrid proximal policy optimization spectrum access algorithm in vehicular network", International Journal of Intelligent Computing and Cybernetics, Vol. 17 No. 3, pp. 486-502. https://doi.org/10.1108/IJICC-01-2024-0027