Open RAN-Enabled Deep Learning-Assisted Mobility Management for Connected Vehicles

TL;DR

This paper introduces an open RAN-based deep learning framework for mobility management in connected vehicles, aiming to reduce handover issues and improve service continuity in high-mobility scenarios.

Contribution

It develops an open-source, deep learning-enabled decision-making system integrated with O-RAN to enhance handover performance for connected vehicles.

Findings

Reduced handover latency compared to standard procedures

Improved QoS in urban vehicle scenarios

Effective integration of deep learning with open RAN platforms

Abstract

Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent changes of serving base station, also known as handovers (HOs). This paper proposes the adoption of Open Radio Access Network (Open RAN/O-RAN) and deep learning models for decision-making to prevent Quality of Service (QoS) degradation due to HOs and to ensure the timely connectivity needed for CV services. The solution utilizes the O-RAN Software Community (OSC), an open-source O-RAN platform developed by the collaboration between the O-RAN Alliance and Linux Foundation, to develop xApps that are executed in the near-Real-Time RIC of OSC. To demonstrate the proposal's effectiveness, an integrated framework combining the OMNeT++ simulator and OSC was created. Evaluations used real-world datasets in urban application scenarios, such as video streaming transmission and over-the-air (OTA) updates. Results indicate that the proposal achieved superior performance and reduced latency compared to the standard 3GPP HO procedure.