Multi-Agent Reinforcement Learning for Slicing Resource Allocation in Vehicular Networks

Abstract

To support diverse Internet of vehicles (IoV) services with different quality of service (QoS) requirements, network slicing is applied in vehicular networks to establish multiple logically isolated networks on common physical network infrastructure. However, dynamic and efficient radio access network (RAN) slicing adapting to the dynamics of vehicular networks remains challenging. The diverse applications make multi-dimensional resource requirements, which will result in the resource allocation more complicated. In addition, the system needs to frequently adjust the resources of slices, which will cause additional slicing overhead. Thus, to solve the above problems, we propose a resource allocation strategy by using multi-agent reinforcement learning to allocate resources in vehicular networks. Firstly, the cost composition of RAN slicing is analyzed, and the optimization problem is formulated to minimize the long-term system cost. Then, we transform the resource allocation problem into a partially observable Markov decision process. Finally, we propose a multi-agent deep deterministic policy gradient based resource allocation algorithm to solve it. All base stations are treated as independent agents, and they cooperatively allocate spectrum and computing resources. Simulation results show that the proposed strategy reduces the system cost effectively compared to the benchmarks, and the average QoS satisfaction rate achieves 96.5%.