ADSA: A Multi-path Transmission Scheduling Algorithm based on Deep Reinforcement Learning in Vehicle Networks

Abstract

Cognitive Radio (CR) enabled vehicles in Vehicle Networks can use multiple Radio Access Networks (RANs) for data transmission. The simultaneous use of multiple RANs for transmission requires the design of a specific multi-path transmission protocol. Many scholars have studied the scheduling algorithm to improve the quality of multi-path transmission. However, most of the existing scheduling algorithms are difficult to deal with the challenges brought by the diversity and heterogeneity of the vehicle network. To deal with these challenges, this paper proposes an IP layer Deep Reinforcement Learning (DRL) multi-path transmission scheduling algorithm named Adaptive Dynamic Scheduling Algorithm (ADSA), which can dynamically generate the optimal scheduling policy through the interaction between agent and network environment. This paper first models the data packet scheduling strategy of multi-path transmission into an optimization problem of multi-path transmission efficiency. Then this paper transforms the optimization problem into a DRL problem and finds the optimal scheduling strategy through DRL model training. This paper evaluates the network performance of ADSA in different network scenarios compared with traditional scheduling algorithms. Simulation results show that ADSA increases the throughput by 8.9 Mbps compared with the three traditional scheduling algorithms and reduces the transmission delay by 4.3 ms.