Authors: Maria Barbosa, Kelvin Dias
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.
Source: http://arxiv.org/abs/2412.21161v1
