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

Maria Barbosa, Kelvin Dias

TL;DR

This work tackles QoS degradation in high-mobility connected vehicles caused by frequent handovers by integrating Open RAN with deep learning. It presents an open, integrated framework combining OSC and the OMNeT++/Simu5G simulator to train and test xApps in a near-real-time RIC environment, focusing on HO optimization. The authors implement supervised models (GRU and LSTM) for proactive handover prediction and evaluate the approach on MEC-enabled video streaming and OTA updates using real urban traces, demonstrating improvements in CQI, delay, and throughput over the 3GPP Default HO baseline. The results show that the LSTM model often yields the best performance across use cases, highlighting the practical viability of Open RAN–driven, DL-assisted mobility management for ITS and autonomous-vehicle scenarios.

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.

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

TL;DR

This work tackles QoS degradation in high-mobility connected vehicles caused by frequent handovers by integrating Open RAN with deep learning. It presents an open, integrated framework combining OSC and the OMNeT++/Simu5G simulator to train and test xApps in a near-real-time RIC environment, focusing on HO optimization. The authors implement supervised models (GRU and LSTM) for proactive handover prediction and evaluate the approach on MEC-enabled video streaming and OTA updates using real urban traces, demonstrating improvements in CQI, delay, and throughput over the 3GPP Default HO baseline. The results show that the LSTM model often yields the best performance across use cases, highlighting the practical viability of Open RAN–driven, DL-assisted mobility management for ITS and autonomous-vehicle scenarios.

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.
Paper Structure (14 sections, 8 figures, 4 tables)

This paper contains 14 sections, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Integrated OSC and OMNeT++ Simulator Evaluation Framework
  3. O-RAN Software Community and its components
  4. OMNeT++ Simulator
  5. O-RAN-Based Handover Optimization
  6. Handover Mechanism
  7. Implementation of Supervised prediction models
  8. Use-Cases Performance Evaluation
  9. Baseline
  10. Vehicular Network
  11. Results
  12. Video Streaming
  13. Over-the-Air (OTA) Software Updates
  14. Conclusion

Figures (8)

  • Figure 1: Integrated O-RAN Software Community and Simu5G Framework.
  • Figure 2: xApps and its Interactions in the proposed HO solution.
  • Figure 3: Signaling Flow of the Proposed Solution.
  • Figure 4: Channel Quality Indicator by Model.
  • Figure 5: Delay by Model.
  • ...and 3 more figures