Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services

TL;DR

This paper proposes a deep reinforcement learning-based architecture at the network edge to optimize RAN slicing and resource management for latency-sensitive B5G services, demonstrated through autonomous-driving simulations.

Contribution

It introduces a novel edge intelligence architecture utilizing deep reinforcement learning for dynamic RAN slicing and resource management in latency-sensitive B5G networks.

Findings

Outperforms baseline methods in simulation

Effective for autonomous-driving latency requirements

Demonstrates potential for real-time network optimization

Abstract

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive applications. In this context, a challenging research goal is to exploit edge intelligence to dynamically and optimally manage the Radio Access Network Slicing (that is a less mature and more complex technology than fifth-generation Network Slicing) and Radio Resource Management, which is a very complex task due to the mostly unpredictably nature of the wireless channel. This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management optimization supporting latency-sensitive applications. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.