Carlos Natalino

TL;DR: This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities and shows how the policy is able to adapt to different conditions in terms of slice degradation penalty versus slice revenue factors, and proportion of high versus low priority services.

TL;DR: Simulation results show that the proposed schemes can effectively improve one-hop access probability for real-time vehicular services implying low delay performance, even when the fog resource is under heavy load.

TL;DR: The architecture extends the media independent handover/ IEEE 802.21 proposal with QoE-aware seamless mobility, video quality estimator, dynamic class of service mapping, and a set of content adaptation schemes to ensure that the end user is always best connected.

TL;DR: An experimental investigation of a machine learning framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field deployed testbed with coherent receivers, indicates that artificial neural networks achieve 99.9% accuracy in attack type and intensity classification, and are capable of processing 1 million samples in less than 10 seconds.

TL;DR: This work presents a slice admission policy based on reinforcement learning able to maximize the profit of an infrastructure provider and shows that when tenants request slices with different latency requirements, the proposed policy outperforms benchmark heuristics.