In the near future, 6G networks will be able to provide its users with heterogeneous tailored end-to-end network services for edge AI-powered applications. However, in many 6G use case scenarios there may not be structured network infrastructures. One solution to meet this problem is to bring computing, networking and storage facilities on site by means of UAVs. The target of this paper is to define a management framework for Flying Ad-Hoc Networks (FANET) aimed at bringing edge computing to remote areas, guaranteeing low latency to ground devices with heterogeneous requirements in terms of mean latency. The proposed framework allows zero-touch management of different network slices realized by partitioning the Computing Elements (CE) installed on each UAV of the FANET and wireless links connecting them to each other. An Inter-Slice Orchestrator is in charge of deciding this resource partition, while an Intra-Slice Orchestrator manages horizontal offload among UAVs for each slice via Reinforcement Learning, in order to minimize the end-to-end latency.

Tailoring FANET-Based 6G Network Slices in Remote Areas for Low-Latency Applications

Grasso C.;Raftopoulos R.;Schembra G.
2022-01-01

Abstract

In the near future, 6G networks will be able to provide its users with heterogeneous tailored end-to-end network services for edge AI-powered applications. However, in many 6G use case scenarios there may not be structured network infrastructures. One solution to meet this problem is to bring computing, networking and storage facilities on site by means of UAVs. The target of this paper is to define a management framework for Flying Ad-Hoc Networks (FANET) aimed at bringing edge computing to remote areas, guaranteeing low latency to ground devices with heterogeneous requirements in terms of mean latency. The proposed framework allows zero-touch management of different network slices realized by partitioning the Computing Elements (CE) installed on each UAV of the FANET and wireless links connecting them to each other. An Inter-Slice Orchestrator is in charge of deciding this resource partition, while an Intra-Slice Orchestrator manages horizontal offload among UAVs for each slice via Reinforcement Learning, in order to minimize the end-to-end latency.
2022
978-3-903176-48-5
6G
Deep Reinforcement Learning
Low-latency
Network Slicing
UAVs
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/551989
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