Edge computing and artificial intelligence promise to turn future mobile networks into service- and radio-aware infrastructures, able to address the requirements of upcoming latency-sensitive applications. For instance, they can be used to dynamically and optimally manage the Radio Access Network Slicing. However, this is a challenging goal, due to the mostly unpredictably nature of the wireless channel. This paper presents a novel architecture using Deep Reinforcement Learning at the network edge for addressing Radio Access Network Slicing and Radio Resource Management. By considering the autonomous-driving use-case, computer simulations demonstrate the effectiveness of our proposal against baseline methodologies.
Martiradonna, S., Abrardo, A., Moretti, M., Piro, G., Boggia, G. (2021). Deep reinforcement learning-aided RAN slicing enforcement supporting latency sensitive services in B5G networks. INTERNET TECHNOLOGY LETTERS, 4(6) [10.1002/itl2.328].
Deep reinforcement learning-aided RAN slicing enforcement supporting latency sensitive services in B5G networks
Abrardo, A;
2021-01-01
Abstract
Edge computing and artificial intelligence promise to turn future mobile networks into service- and radio-aware infrastructures, able to address the requirements of upcoming latency-sensitive applications. For instance, they can be used to dynamically and optimally manage the Radio Access Network Slicing. However, this is a challenging goal, due to the mostly unpredictably nature of the wireless channel. This paper presents a novel architecture using Deep Reinforcement Learning at the network edge for addressing Radio Access Network Slicing and Radio Resource Management. By considering the autonomous-driving use-case, computer simulations demonstrate the effectiveness of our proposal against baseline methodologies.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1175885