Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems

TL;DR

This paper introduces a multi-agent reinforcement learning approach for joint channel assignment and power allocation in vehicular communication systems, enabling distributed decision-making amid high mobility and large user numbers.

Contribution

It proposes a novel distributed RL-based resource allocation algorithm for C-V2X systems that operates with local information and outperforms traditional methods.

Findings

RL algorithm achieves near-optimal performance

Distributed approach reduces reliance on global channel info

Improves sum-rate and latency in vehicular networks

Abstract

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) 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 high user mobility 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 multi-agent 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, acting 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 sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.