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

arXiv: https://arxiv.org/abs/2103.10277 · HTML: https://mlsanity.com/paper/2103.10277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks](https://mlsanity.com/paper/1712.08083/) (2017)
- [From Network Sharing to Multi-tenancy: The 5G Network Slice Broker](https://mlsanity.com/paper/1605.01201/) (2016)
- [Toward Operator-to-Waveform 5G Radio Access Network Slicing](https://mlsanity.com/paper/1905.08130/) (2019)

## Abstract (arXiv)

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.