activity
20212024
most citedLyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces

20 citations · 21 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SP20241 cited

Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach

Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore +17

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requir…

eess.SP2024

Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting

Francesco Binucci, Mattia Merluzzi, Paolo Banelli +2

Deep Neural Network (DNN) splitting is one of the key enablers of edge Artificial Intelligence (AI), as it allows end users to pre-process data and offload part of the computationa…

eess.SP2023

Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization

Paolo Di Lorenzo, Mattia Merluzzi, Francesco Binucci +4

Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and pr…

eess.SP2023

6G goal-oriented communications: How to coexist with legacy systems?

Mattia Merluzzi, Miltiadis C. Filippou, Leonardo Gomes Baltar +2

6G will connect heterogeneous intelligent agents to make them operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how AI…

cs.IT202320 cited

Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces

Kyriakos Stylianopoulos, Mattia Merluzzi, Paolo Di Lorenzo +1

In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable in…

eess.SP2021

Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces

Paolo Di Lorenzo, Mattia Merluzzi, Emilio Calvanese Strinati +1

In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable…