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We consider the problem of dynamic platoon leader selection, userassociation, channel assignment, and power allocation on a cellularvehicle-to-everything (C-V2X) based highway, where multiple vehicle-to-vehicle(V2V) and vehicle-to-infrastructure (V2I) links share the frequency resources.There are multiple roadside units (RSUs) on a highway, and vehicles can formplatoons, which has been identified as an advanced use case to increase roadefficiency. The traditional optimization methods, requiring global channelinformation at a central controller, are not viable for high-mobility vehicularnetworks. To deal with this challenge, we propose a distributed multi-agentreinforcement learning (MARL) for resource allocation (RA). Each platoonleader, acting as an agent, can collaborate with other agents for jointsub-band selection and power allocation for its V2V links, and joint userassociation and power control for its V2I links. Moreover, each platoon candynamically select the vehicle most suitable to be the platoon leader. We aimto maximize the V2V and V2I packet delivery probability in the desired latencyusing the deep Q-learning algorithm. Simulation results indicate that ourproposed MARL outperforms the centralized hill-climbing algorithm, and platoonleader selection helps to improve both V2V and V2I performance.

Platoon Leader Selection, User Association and Resource Allocation on a C-V2X based highway: A Reinforcement Learning Approach