338 citations · 1.3k across the 45 of their papers we have counts for
16 papers · 1 filter
Structured Sheaf Learning of Consistent Connection Graphs
Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa +1
Connection graphs (CGs) extend classical graphs by associating vector-valued signals to nodes and orthogonal transport maps across edges, making them a natural model for synchroniz…
Sheaf-theoretic Signal Processing on Graphs: Spectral Theory, Filtering, and Sampling
Gabriele D'Acunto, Leonardo Di Nino, Paolo Di Lorenzo +1
Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure. Processing su…
Learning Dirac Spectral Transforms for Topological Signals
Leonardo Di Nino, Tiziana Cattai, Sergio Barbarossa +2
The Dirac operator provides a unified framework for processing signals defined over different order topological domains, such as node and edge signals. Its eigenmodes define a spec…
Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
Francesco Binucci, Paolo Banelli, Paolo Di Lorenzo +1
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features a…
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…
Control Aspects for Using RIS in Latency-Constrained Mobile Edge Computing
Fabio Saggese, Victor Croisfelt, Francesca Costanzo +4
This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which mult…
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…
Semantic Communications Based on Adaptive Generative Models and Information Bottleneck
S. Barbarossa, D. Comminiello, E. Grassucci +3
Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequ…
Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management
Francesco Binucci, Paolo Banelli, Paolo Di Lorenzo +1
Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A co…
Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back
Claudio Battiloro, Zhiyang Wang, Hans Riess +2
In this work we introduce a convolution operation over the tangent bundle of Riemann manifolds in terms of exponentials of the Connection Laplacian operator. We define tangent bund…
Topological Signal Processing over Weighted Simplicial Complexes
Claudio Battiloro, Stefania Sardellitti, Sergio Barbarossa +1
Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extracti…
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck
Francesco Pezone, Sergio Barbarossa, Paolo Di Lorenzo
Whenever communication takes place to fulfil a goal, an effective way to encode the source data to be transmitted is to use an encoding rule that allows the receiver to meet the re…
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…
Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks
C. Battiloro, P. Di Lorenzo, P. Banelli +1
We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic s…
Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing
Mattia Merluzzi, Nicola di Pietro, Paolo Di Lorenzo +2
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks. The goal is to minimize the energy consu…
Observing and Tracking Bandlimited Graph Processes
Elvin Isufi, Paolo Banelli, Paolo Di Lorenzo +1
One of the most crucial challenges in graph signal processing is the sampling of bandlimited graph signals, i.e., signals that are sparse in a well-defined graph Fourier domain. So…