Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services
This addresses the challenge of optimizing network resources for latency-sensitive services like autonomous driving in B5G, representing an incremental improvement in applying AI to network management.
The paper tackles the problem of dynamically managing Radio Access Network Slicing and Radio Resource Management for latency-sensitive applications in B5G networks by proposing a novel architecture that uses Deep Reinforcement Learning at the edge, and it demonstrates effectiveness through computer simulations in an autonomous-driving use-case.
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.