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.
2021
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1175885