338 citations · 1.3k across the 45 of their papers we have counts for
46 papers
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…
Game-Theoretic Latent Space Alignment for Multi-user Semantic MIMO Communications
Giuseppe Di Poce, Mattia Merluzzi, Emilio Calvanese Strinati +1
Semantic communications enable AI-native wireless systems by mapping raw data into compressed task-oriented latent representations. However, independently trained agents often rely…
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…
SPARQ: An Optimization Framework for the Distribution of AI-Intensive Applications under Non-Linear Delay Constraints
Pietro Spadaccino, Paolo Di Lorenzo, Sergio Barbarossa +2
Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-…
Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati +1
In future AI-native wireless networks, the presence of mismatches between the latent spaces of independently designed and trained deep neural network (DNN) encoders may impede mutu…
Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks
Kyriakos Stylianopoulos, Paolo Di Lorenzo, George C. Alexandropoulos
In the Edge Inference (EI) paradigm, where a Deep Neural Network (DNN) is split across the transceivers to wirelessly communicate goal-defined features in solving a computational t…
Semantic Communication Enhanced by Knowledge Graph Representation Learning
Nour Hello, Paolo Di Lorenzo, Emilio Calvanese Strinati
This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic communications. The proposed…
Multi-RIS-Empowered Multiple Access: A Distributed Sum-Rate Maximization Approach
Konstantinos D. Katsanos, Paolo Di Lorenzo, George C. Alexandropoulos
The plethora of wirelessly connected devices, whose deployment density is expected to largely increase in the upcoming sixth Generation (6G) of wireless networks, will naturally ne…
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…
Dynamic Relative Representations for Goal-Oriented Semantic Communications
Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati +1
In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However,…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
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…
Power Minimizing MEC Offloading with QoS Constraints over RIS-Empowered Communications
Mattia Merluzzi, Francesca Costanzo, Konstantinos D. Katsanos +2
This work lies at the intersection of two cutting edge technologies envisioned to proliferate in future 6G wireless systems: Multi-access Edge Computing (MEC) and Reconfigurable In…
Learning Multi-Frequency Partial Correlation Graphs
Gabriele D'Acunto, Paolo Di Lorenzo, Francesco Bonchi +2
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlat…
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…
Generalized Simplicial Attention Neural Networks
Claudio Battiloro, Lucia Testa, Lorenzo Giusti +3
Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many c…
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…
RIS-Enabled Smart Wireless Environments: Deployment Scenarios, Network Architecture, Bandwidth and Area of Influence
George C. Alexandropoulos, Dinh-Thuy Phan-Huy, Kostantinos D. Katsanos +19
Reconfigurable Intelligent Surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments, and are lately being considered as a candidate phys…
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…
Cell Attention Networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa +3
Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships…