118 citations · 405 across the 18 of their papers we have counts for
19 papers
Information Bottleneck Meets Quantization: Finite Rate Analysis and Optimal Designs
Francesco Binucci, Paolo Banelli
The Information Bottleneck (IB) is a well established framework that looks for a latent compact representation of a data source, by trading rate and data-size representation, for i…
Radio-Coverage-Aware Path Planning for Cooperative Autonomous Vehicles
Giuseppe Baruffa, Luca Rugini, Francesco Binucci +3
Fleets of autonomous vehicles (AV) often are at the core of intelligent transportation scenarios for smart cities, and may require a wireless Internet connection to offload compute…
Conformal Lyapunov Optimization: Optimal Resource Allocation under Deterministic Reliability Constraints
Francesco Binucci, Osvaldo Simeone, Paolo Banelli
This paper introduces conformal Lyapunov optimization (CLO), a novel resource allocation framework for networked systems that optimizes average long-term objectives, while satisfyi…
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…
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…
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…
GMM-based Symbol Error Rate Prediction for Multicarrier Systems with Impulsive Noise Suppression
Nikola Rozic, Paolo Banelli, Dinko Begusic +1
Theoretical analysis of orthogonal frequency division multiplexing (OFDM) systems equipped at the receiver by a non-linear impulsive noise suppressor is a challenging topic in comm…
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…
Sampling and Recovery of Graph Signals
P. Di Lorenzo, S. Barbarossa, P. Banelli
The aim of this chapter is to give an overview of the recent advances related to sampling and recovery of signals defined over graphs. First, we illustrate the conditions for perfe…
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…
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Paolo Di Lorenzo, Paolo Banelli, Elvin Isufi +2
The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices. We…
Distributed Adaptive Learning of Graph Signals
P. Di Lorenzo, P. Banelli, S. Barbarossa +1
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables dis…
On the Equivalence of Maximum SNR and MMSE Estimation: Applications to Additive Non-Gaussian Channels and Quantized Observations
Luca Rugini, Paolo Banelli
The minimum mean-squared error (MMSE) is one of the most popular criteria for Bayesian estimation. Conversely, the signal-to-noise ratio (SNR) is a typical performance criterion in…
2-Dimensional Finite Impulse Response Graph-Temporal Filters
Elvin Isufi, Geert Leus, Paolo Banelli
Finite impulse response (FIR) graph filters play a crucial role in the field of signal processing on graphs. However, when the graph signal is time-varying, the state of the art FI…
Adaptive Least Mean Squares Estimation of Graph Signals
Paolo Di Lorenzo, Sergio Barbarossa, Paolo Banelli +1
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a…
Modulation Formats and Waveforms for the Physical Layer of 5G Wireless Networks: Who Will be the Heir of OFDM?
Paolo Banelli, Stefano Buzzi, Giulio Colavolpe +3
5G cellular communications promise to deliver the gigabit experience to mobile users, with a capacity increase of up to three orders of magnitude with respect to current LTE system…
Bayesian Estimation of a Gaussian source in Middleton's Class-A Impulsive Noise
Paolo Banelli
The paper focuses on minimum mean square error (MMSE) Bayesian estimation for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. In addition to the optimal…
Non-Linear Transformations of Gaussians and Gaussian-Mixtures with implications on Estimation and Information Theory
Paolo Banelli
This paper investigates the statistical properties of non-linear transformations (NLT) of random variables, in order to establish useful tools for estimation and information theory…