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

20 citations · 36 across the 24 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

eess.SP2022

Blue Communications for Edge Computing: the Reconfigurable Intelligent Surfaces Opportunity

Fatima Ezzahra Airod, Mattia Merluzzi, Antonio Clemente +1

Wireless traffic is exploding, due to the myriad of new connections and the exchange of capillary data at the edge of the networks to operate real-time processing and decision maki…

cs.IT2022

Reconfigurable Intelligent Surface Aided Mobile Edge Computing over Intermittent mmWave Links

Fatima Ezzahra Airod, Mattia Merluzzi, Paolo Di Lorenzo +1

The advent of Reconfigurable Intelligent Surfaces (RISs) in wireless communication networks unlocks the way to support high frequency radio access (e.g. in millimeter wave) while o…

eess.SP2022

Energy-Efficient Dynamic Edge Computing with Electromagnetic Field Exposure Constraints

Mattia Merluzzi, Serge Bories, Emilio Calvanese Strinati

We present a dynamic resource allocation strategy for energy-efficient and Electromagnetic Field (EMF) exposure aware computation offloading at the wireless network edge. The goal…

eess.SP2022

Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees

Mattia Merluzzi, Claudio Battiloro, Paolo Di Lorenzo +1

Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), an…

eess.SP2022

Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case

Mattia Merluzzi, Miltiadis C. Filippou, Leonardo Gomes Baltar +1

Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capabil…