AoI-Aware Joint Scheduling and Power Allocation in Intelligent Transportation System: A Deep Reinforcement Learning Approach

In Intelligent Transportation System (ITS), information freshness is a crucial indicator for monitoring road traffic, which is measured by Age of Information (AoI). This paper studies the problem of vehicle data packet scheduling and power allocation for AoI minimization in a Manhattan grid Vehicle to Infrastructure (V2I) network. The challenge of the problem originates from the dynamic wireless environment and different AoI requirements of vehicles. To solve the above problems, a single-agent Markov Decision Process (MDP) is modeled. And we propose a Dueling Double Deep Q-Network (D3QN)-based Scheduling and Power Allocation Method (SPAM). The D3QN agent is devoted to minimizing the AoI of each vehicle. In addition, Priority Experience Replay (PER) technology is developed to solve the difficulty of obtaining high-value learning experiences. The simulation results show that the proposed method outperforms baseline D3QN approaches in average AoI by 22.6%.