DRL-driven RAN Slicing Management: A V2X-oriented Approach In Multi-service Scenarios

2026-10-01

AI Summary

This study addresses the challenges of resource contention and stringent low-latency, high-reliability requirements in scenarios where V2X and eMBB services coexist. To tackle these issues, this work proposes an intelligent RAN slicing management framework tailored for V2X communications, incorporating a multi-service, dynamic data-driven slice coordination mechanism. Furthermore, a deep reinforcement learning (DRL) algorithm is employed to achieve real-time resource scheduling and adaptive balancing across heterogeneous traffic flows. Experimental validation within a 5G standalone network environment demonstrates that the proposed approach significantly reduces SLA violation rates compared to conventional static strategies while substantially enhancing eMBB resource utilization. Ultimately, this framework effectively realizes the synergistic optimization of safety-critical V2X applications and high-bandwidth eMBB demands.

Abstract

The integration of Vehicle-to-Everything (V2X) communications is driving a profound transformation in vehicular connectivity, expected to significantly enhance traffic efficiency and safety. However, the stringent requirements of V2X services, particularly ultra-low latency and high reliability, present significant technical challenges. 5G's Network Slicing emerges as a key enabler by providing tailored virtual networks that ensure isolation and adaptability for heterogeneous services. This work proposes an intelligent Radio Access Network (RAN) slicing management framework specifically designed for scenarios where safety-critical V2X and high-capacity eMBB slices coexist. In such complex environments, harmonizing conflicting traffic requirements demands continuous, data-driven optimization. To achieve this, the proposed framework leverages an advanced Deep Reinforcement Learning (DRL) approach which dynamically optimizes resource allocation in real time. The framework is empirically validated on a real 5G Standalone (SA) network, where experimental results demonstrate that the DRL-driven approach successfully balances both objectives, outperforming traditional static and proportional allocation strategies by minimizing SLA violations while ensuring high resource utilization for eMBB slices.

Problem

• V2X
• Network Slicing
• RAN
• Multi-service
• Resource Allocation

Innovation

• Deep Reinforcement Learning
• RAN Slicing
• V2X
• Network Slicing
• 5G SA