Joint Channel Selection using FedDRL in V2X

TL;DR

This paper introduces FedDRL, a federated deep reinforcement learning approach for joint channel selection in V2X networks, improving communication reliability and efficiency through collaborative learning.

Contribution

It applies federated Proximal Policy Optimization to V2X channel selection, enabling vehicles to learn optimal strategies collaboratively without sharing raw data.

Findings

Enhanced communication reliability in simulations

Reduced transmission costs and channel switches

Demonstrated effectiveness of FedDRL in V2X scenarios

Abstract

Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity enhances road safety, transportation efficiency, and driver assistance systems. V2X benefits from Machine Learning, enabling real-time data analysis, better decision-making, and improved traffic predictions, making transportation safer and more efficient. In this paper, we study the problem of joint channel selection, where vehicles with different technologies choose one or more Access Points (APs) to transmit messages in a network. In this problem, vehicles must learn a strategy for channel selection, based on observations that incorporate vehicles' information (position and speed), network and communication data (Signal-to-Interference-plus-Noise Ratio from past communications), and environmental data (road type). We propose an approach based on Federated Deep Reinforcement Learning (FedDRL), which enables each vehicle to benefit from other vehicles' experiences. Specifically, we apply the federated Proximal Policy Optimization (FedPPO) algorithm to this task. We show that this method improves communication reliability while minimizing transmission costs and channel switches. The efficiency of the proposed solution is assessed via realistic simulations, highlighting the potential of FedDRL to advance V2X technology.