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20182026
most citedLyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces

20 citations · 37 across the 25 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024

Goal-oriented Communications based on Recursive Early Exit Neural Networks

Jary Pomponi, Mattia Merluzzi, Alessio Devoto +3

This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First,…

eess.SP20241 cited

Optimizing RIS Impairments through Semantic Communication

Nour Hello, Mattia Merluzzi, Emilio Calvanese Strinati +1

This paper investigates how semantic communication can effectively influence and potentially redefine the limitations imposed by physical layer settings. Reconfigurable Intelligent…

eess.SY2024

Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits

Hiba Dakdouk, Mohamed Sana, Mattia Merluzzi

In recent years, the integration of communication and control systems has gained significant traction in various domains, ranging from autonomous vehicles to industrial automation…

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…