Deep reinforcement learning improves 5G network slices for vehicle safety and high data use
DRL-driven RAN Slicing Management: A V2X-oriented Approach In Multi-service Scenarios
Networking and Internet Architecture
Summary
Vehicles communicating with everything around them need very fast and reliable connections, which is hard to manage because different services have conflicting needs. The authors designed a smart system using deep reinforcement learning to dynamically share 5G network resources between urgent vehicle safety messages and high-data services. They tested their system on a real 5G network and found it reduces service problems while keeping data service speeds high. This helps balance the needs of safety and entertainment in connected cars.
What this means in practice
- •For mobile network operators: Optimize 5G network slices in real time to better support vehicle safety and high-data services simultaneously.
- •For telecom equipment vendors: Integrate intelligent RAN slicing management tools into 5G base stations for improved service quality in multi-service traffic.$Commercial implications: Enables new software products that enhance 5G base station capability to dynamically allocate resources based on deep learning.
Authors
Daniel E. Garcia-Fernandez, Pablo Vera-Soto, Sergio Fortes, M. Martinez, I. de-la-Bandera, M. L. Luque, A. Mendo, J. Ramiro, Raquel Barco
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.