Toward Intelligent Vehicular Networks: A Machine Learning Framework

TL;DR

This paper explores how machine learning, especially reinforcement learning, can address the unique challenges of high mobility vehicular networks by optimizing network performance and managing resources.

Contribution

It introduces a machine learning framework tailored for intelligent vehicular networks, emphasizing reinforcement learning for resource management in high mobility environments.

Findings

Reinforcement learning effectively manages network resources in vehicular networks.

Machine learning can predict network dynamics for better decision-making.

The paper highlights open challenges for future research.

Abstract

As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this article, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.