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Advancing O-RAN to Facilitate Intelligence in V2X

Eugenio Moro, Francesco Linsalata, Maurizio Magarini, Umberto Spagnolini, Antonio Capone

TL;DR

This work tackles the rigidity of traditional RANs in meeting V2X demands by proposing a tightly integrated O-RAN–V2X architecture that introduces an enabling framework for network intelligence in vehicular networks. It defines an architecture with extended interfaces and O-RAN–based control loops (xApps and rApps) to manage beam selection, radio resources, V2V connectivity, and a programmable digital twin, demonstrated through a dedicated simulation framework. The results indicate substantial gains in throughput, resilience, and control overhead over standard approaches, validating the viability of O-RAN as a platform for intelligent V2X. The study highlights the practical impact of programmable wireless networks in enabling autonomous driving, cooperative sensing, and reliable high-rate vehicular communications in future 6G systems.

Abstract

Vehicular communications integrated with the Radio Access Network (RAN) are envisioned as a breakthrough application for the 6th generation (6G) cellular systems. However, traditional RANs lack the flexibility to enable sophisticated control mechanisms that are demanded by the strict performance requirements of the vehicle-to-everything (V2X) environment. In contrast, the features of Open RAN (O-RAN) can be exploited to support advanced use cases, as its core paradigms represent an ideal framework for orchestrating vehicular communication. Although the high potential stemming from their integration can be easily seen and recognized, the effective combination of the two ecosystems is an open issue. Conceptual and architectural advances are required for O-RAN to be capable of facilitating network intelligence in V2X. This article pioneers the integration of the two strategies for seamlessly incorporating V2X control within O-RAN ecosystem. First, an enabling architecture that tightly integrates V2X and O-RAN is proposed and discussed. Then, a set of key V2X challenges is identified, and O-RAN-based solutions are proposed, paired with extensive numerical analysis to support their effectiveness. Results showcase the superior performance of such an approach in terms of raw throughput, network resilience, and control overhead. Finally, these results validate the proposed enabling architecture and confirm the potential of O-RAN in support of V2X communications.

Advancing O-RAN to Facilitate Intelligence in V2X

TL;DR

This work tackles the rigidity of traditional RANs in meeting V2X demands by proposing a tightly integrated O-RAN–V2X architecture that introduces an enabling framework for network intelligence in vehicular networks. It defines an architecture with extended interfaces and O-RAN–based control loops (xApps and rApps) to manage beam selection, radio resources, V2V connectivity, and a programmable digital twin, demonstrated through a dedicated simulation framework. The results indicate substantial gains in throughput, resilience, and control overhead over standard approaches, validating the viability of O-RAN as a platform for intelligent V2X. The study highlights the practical impact of programmable wireless networks in enabling autonomous driving, cooperative sensing, and reliable high-rate vehicular communications in future 6G systems.

Abstract

Vehicular communications integrated with the Radio Access Network (RAN) are envisioned as a breakthrough application for the 6th generation (6G) cellular systems. However, traditional RANs lack the flexibility to enable sophisticated control mechanisms that are demanded by the strict performance requirements of the vehicle-to-everything (V2X) environment. In contrast, the features of Open RAN (O-RAN) can be exploited to support advanced use cases, as its core paradigms represent an ideal framework for orchestrating vehicular communication. Although the high potential stemming from their integration can be easily seen and recognized, the effective combination of the two ecosystems is an open issue. Conceptual and architectural advances are required for O-RAN to be capable of facilitating network intelligence in V2X. This article pioneers the integration of the two strategies for seamlessly incorporating V2X control within O-RAN ecosystem. First, an enabling architecture that tightly integrates V2X and O-RAN is proposed and discussed. Then, a set of key V2X challenges is identified, and O-RAN-based solutions are proposed, paired with extensive numerical analysis to support their effectiveness. Results showcase the superior performance of such an approach in terms of raw throughput, network resilience, and control overhead. Finally, these results validate the proposed enabling architecture and confirm the potential of O-RAN in support of V2X communications.
Paper Structure (10 sections, 6 figures, 1 table)

This paper contains 10 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. A next-gen architecture for
  3. -based control plane for
  4. Technological feasibility
  5. An O-RAN approach to the V2X challenges
  6. Beam selection and management
  7. Radio resource management.
  8. Enhanced Vehicle-to-vehicle connectivity.
  9. Programmable and up-to-date V2X digital twin.
  10. Concluding Remarks

Figures (6)

  • Figure 1: Next-generation architecture details.
  • Figure 2: Beam Management training time overhead percentage (bars) and overall system spectral efficiency (lines) for different beam codebook cardinalities
  • Figure 3: Average normalized throughput (bars) and the average collision ratio (line) versus the number of simultaneously transmitting
  • Figure 4: Network connectivity (bars) and average number of hops (line) versus required SNR
  • Figure 5: Control plane traffic required by the xApp
  • ...and 1 more figures