Deep reinforcement learning optimizes 5G resources for vehicle communication
A DRL-Driven Optimization of RAN Slice Resource Partitioning for V2X SLA Compliance in 5G Networks
Networking and Internet Architecture
Summary
Vehicles need super-fast and reliable connections to communicate with everything around them, like other cars and traffic lights. But in 5G networks, many types of services share limited radio resources, making it hard to give vehicles what they need. The authors used a kind of artificial intelligence called reinforcement learning to split these resources smartly so vehicle communications get the speed and reliability they require while other services still work well. They tested their approach in different traffic situations and showed it can adapt to changes and keep the network running smoothly.
What this means in practice
- •For network schedulers: Dynamically adjust radio resource allocation to meet stringent vehicle communication requirements and maintain other service quality in 5G networks.
- •For urban traffic management teams: Improve communication reliability for connected vehicles by enabling flexible network resource partitioning under high traffic demand conditions.
Authors
M. Martínez, I. de-la-Bandera, D. E. García, P. Vera, S. Fortes, M. L. Luque, A. Mendo, J. Ramiro, R. Barco
Abstract
Vehicle-to-Everything (V2X) communications impose very demanding requirements in terms of latency and reliability, which must be met in scenarios where multiple services with diverse performance targets coexist. In such scenarios, traffic-intensive services compete for limited radio resources, complicating the fulfillment of V2X service demands. Within this context, Network Slicing (NS) emerges as a key factor that enables the creation of multiple slices and the allocation of resources among them to satisfy heterogeneous service requirements. In particular, this work addresses the Radio Access Network (RAN) slicing problem from the perspective of Physical Resource Block (PRB) partitioning under high traffic demand conditions. To this end, a reinforcement learning approach based on Proximal Policy Optimization (PPO) is proposed to determine PRB allocations that satisfy the strict latency and reliability requirements of V2X services, while improving resource utilization efficiency and minimizing performance degradation of enhanced Mobile BroadBand (eMBB) services. The proposed solution is evaluated through simulation-based experiments under various traffic loads and different V2X service requirements, demonstrating its ability to adapt resource partitioning to network conditions and service demands.