A cognitive internet of things resource allocation method based on multi-agent reinforcement learning algorithm

This paper addresses the challenges of inter-vehicle communication, taking into consideration the stochastic nature of primary user spectrum occupancy, the highly dynamic fluctuation of channel states, and the timeliness requirements for communication among vehicles. The study investigates the joint channel selection and power control resource allocation problem in cognitive Internet of Things (CIoT) under high-speed mobility, with the aim of minimizing the system's Age of Information (AoI). The presented problem is modeled as a Markov Decision Process (MDP) and incorporates a meticulously designed reward function. Furthermore, to meet the timeliness demands, a multi-agent reinforcement learning approach is employed, with vehicles serving as intelligent agents that gather localized observational information and directly determine their transmission strategies. An improved Multi-agent Proximal Policy Optimization (IMAPPO) algorithm is proposed, which is based on a centralized training and distributed execution framework. Enhancements to the Actor network within the algorithm enable it to address the challenges presented by the discrete-continuous hybrid action space. Finally, the feasibility and effectiveness of the enhanced multi-agent proximal policy optimization algorithm are verified through simulations. The results demonstrate that compared to alternative approaches, the CIoT resource allocation scheme based on the improved multi-agent proximal policy optimization algorithm significantly reduces the AoI for vehicle users.