Collaborative Multi-Agent Resource Allocation in C-V2X Mode 4

Abstract

Intelligent Transport System (ITS) provides an efficient solution to road safety traffic. To support safety applications, cellular vehicle-to-everything (C-V2X) is developed by third generation partnership project (3GPP). C-V2X support two modes of communication as mode 3 and mode 4. In mode 4, vehicles reserve the resources based on their local observations using semi-persistent scheduling (SPS). If two vehicles, simultaneously select the same resources, it will lead to resource contention. This arises the consensus problem. To overcome this, in this paper we proposed the multi agent collaborative deep reinforcement learning based scheme. A single deep Q network (DQN) is trained for each zone. Each zone is preconfigured with resources which constitute a resource pool. A reward function is shared between the vehicles that belong to the same pool. This approach makes the vehicles to collaborate rather than compete in selecting the resources for their transmission. The proposed scheme is compared with the random resource allocation in C-V2X. The results show that the proposed scheme outperforms even in dense vehicular environment.