5 research outputs found
Multi-Agent Reinforcement Learning for Joint Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems
We consider the problem of joint channel assignment and power allocation in
underlaid cellular vehicular-to-everything (C-V2X) systems where multiple
vehicle-to-infrastructure (V2I) uplinks share the time-frequency resources with
multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and
autonomous vehicles to travel closely together. Due to the nature of fast
channel variant in vehicular environment, traditional centralized optimization
approach relying on global channel information might not be viable in C-V2X
systems with large number of users. Utilizing a reinforcement learning (RL)
approach, we propose a distributed resource allocation (RA) algorithm to
overcome this challenge. Specifically, we model the RA problem as a multi-agent
system. Based solely on the local channel information, each platoon leader, who
acts as an agent, collectively interacts with each other and accordingly
selects the optimal combination of sub-band and power level to transmit its
signals. Toward this end, we utilize the double deep Q-learning algorithm to
jointly train the agents under the objectives of simultaneously maximizing the
V2I sum-rate and satisfying the packet delivery probability of each V2V link in
a desired latency limitation. Simulation results show that our proposed
RL-based algorithm achieves a close performance compared to that of the
well-known exhaustive search algorithm.Comment: 6 pages, 4 figure