  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.
