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20172026
most citedLyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces

20 citations · 35 across the 31 of their papers we have counts for

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cs.IT2026

Federated Latent Space Alignment for Multi-user Semantic Communications

Giuseppe Di Poce, Mario Edoardo Pandolfo, Emilio Calvanese Strinati +1

Semantic communication aims to convey meaning for effective task execution, but differing latent representations in AI-native devices can cause semantic mismatches that hinder mutu…

cs.IT20252 cited

Topological Neural Networks over the Air

Simone Fiorellino, Claudio Battiloro, Paolo Di Lorenzo

Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes)…

cs.IT2024

Adaptive Semantic Token Selection for AI-native Goal-oriented Communications

Alessio Devoto, Simone Petruzzi, Jary Pomponi +2

In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and com…

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…

cs.IT20216 cited

Wireless Environment as a Service Enabled by Reconfigurable Intelligent Surfaces: The RISE-6G Perspective

Emilio Calvanese Strinati, George C. Alexandropoulos, Vincenzo Sciancalepore +15

The design of 6th Generation (6G) wireless networks points towards flexible connect-and-compute technologies capable to support innovative services and use cases. Targeting the 203…