Dynamic Spectrum Resource Allocation in Internet of Vehicles Based on SAC Reinforcement Learning

To address the scarcity of spectrum resources in Internet of Vehicles(IoV), a novel multi-agent dynamic spectrum allocation solution based on Soft Actor-Critic(SAC) reinforcement learning is proposed.The solution aims to maximize the total channel capacity and the success rate of payload delivery.To achieve this goal, a spectrum resource allocation model consisting of Vehicle-to-Vehicle(V2V) links is constructed.Each V2V link is regarded as an agent to model this problem as a Markov decision process.Then the SAC reinforcement learning algorithm is used to design a neural network.The agents are trained by maximum entropy and cumulative reward, so the V2V links can optimize the allocation of spectrum resources through rounds of learning.Simulation results show that compared with spectrum resource allocation scheme based on Deep Q-Network(DQN) and Deep Deterministic Policy Gradient(DDPG), the proposed scheme can more efficiently implement spectrum sharing between V2V links, and improves the channel transmission rate and the success rate of payload delivery.