RL-Loop: Reinforcement Learning-Driven Real-Time 5G Slice Control for Connected and Autonomous Mobility Services

TL;DR

RL-Loop is a reinforcement learning framework that dynamically manages CPU resources in 5G network slices for connected mobility, significantly reducing resource usage while maintaining service quality.

Contribution

It introduces a real-time, RL-based CPU control mechanism for 5G slices supporting mobility services, demonstrating adaptive resource management on a real testbed.

Findings

RL-Loop reduces CPU allocation by over 55%.

It maintains comparable quality-of-service levels.

The framework operates at one-second granularity.

Abstract

Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing traffic conditions. In this paper, we propose RL-Loop, a closed-loop reinforcement learning framework for real-time CPU resource control in 5G network slicing environments supporting connected mobility services. RL-Loop employs a Proximal Policy Optimization (PPO) agent that continuously observes slice-level key performance indicators and adjusts edge CPU allocations at one-second granularity on a real testbed. The framework leverages real-time observability and feedback to enable adaptive, software-defined edge intelligence. Experimental results suggest that RL-Loop can reduce average CPU allocation by over 55% relative to the reference operating point while reaching a comparable quality-of-service degradation region. These results indicate that lightweight reinforcement learning--based feedback control can provide efficient and responsive resource management for 5G-enabled smart mobility and connected vehicle services.