Optimizing Energy-Efficient Resource Allocation in 5G Autonomous Vehicle Networks Through Deep Reinforcement Learning

AVs are also bound to capitalize on 5G networks, which creates crucial challenges in the adaptable management of resources because they need very low latency, a high-speed connection, and energy-efficient functionality. Older approaches to optimizing resource allocation in the high-frequency changing vehicle environment fail to deliver as mobility trends and network status constantly adapt and change. To overcome these problems, we suggest a new Deep Reinforcement Learning (DRL)-based algorithm, which is aimed at optimizing the allocation of resources to AVs. This model combines a Spatiotemporal Graph Convolution Network (ST-GCN), Gated Recurrent Units (GRU), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to create a unified model. The ST-GCN is successful at both capturing the dynamic space relationship between vehicles and between vehicles and roadside infrastructure, and also gives a complete picture of network topology. GRU uses traffic and communication information to forecast future mobility patterns and bandwidth demand of each agent and therefore allocate resources proactively. The MADDPG algorithm is used to enable decentralized but coordinated decision-making among AVs, which enables the realization of dynamic policies of bandwidth allocation in real-time. Simulations using such aspects as a realistic Rayleigh fading channel model, a node density of 100 vehicles/km2, and 100 MHz of bandwidth prove the effectiveness of the framework extensively. We find that the end-to-end latency increase is reduced by up to 30%, and the system throughput is increased by up to 28, and the energy efficiency is increased by an average of 40 percent in comparison with the baseline techniques. Such results confirm our framework to be a plausible solution to building effective and sustainable communication systems to enable AVs to cooperate in the information exchange of important data.