Profiling Vehicles for Improved Small Cell Beam-Vehicle Pairing Using Multi-Armed Bandit

Abstract

The 5G technology has tapped into millimeter wave (mmWave) spectrum to create additional bandwidth for improved network capacity. The use of mmWave for specific applications including vehicular networks has widely discussed. However, applying mmWave to vehicular networks faces challenges of high mobility nodes and narrow coverage along the mmWave beams. In this paper, we focus on a mmWave small cell base station deployed in a city area to support vehicular network application. We propose profiling vehicle mobility for a machine learning agent to learn the performance of serving vehicles with different mobility profiles and utilize the past experiences to select appropriate mmWave beam to service a vehicle. Our machine learning agent is based on multi-armed bandit learning model, where classical multi-armed bandit and contextual multi-armed bandit are used. Particularly for the contextual multi-armed bandit, the contexts are vehicle mobility information. We show that the local street layout has naturally constrained vehicle movement creating distinct mobility information for vehicles, and the vehicle mobility information is highly related to communication performance. By using vehicle mobility information, the machine learning agent is able to identify vehicles that can remain within a beam for longer time period to avoid frequent handovers.