Paolo Banelli
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View article: Conformal Lyapunov Optimization: Optimal Resource Allocation under Deterministic Reliability Constraints
Conformal Lyapunov Optimization: Optimal Resource Allocation under Deterministic Reliability Constraints Open
This paper introduces conformal Lyapunov optimization (CLO), a novel resource allocation framework for networked systems that optimizes average long-term objectives, while satisfying deterministic long-term reliability constraints. Unlike …
View article: Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication Open
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features are maximally relevant to a specific learning or inference t…
View article: Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing
Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing Open
This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion c…
View article: AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work
AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work Open
The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a co…
View article: Opportunistic Information-Bottleneck for Goal-Oriented Feature Extraction and Communication
Opportunistic Information-Bottleneck for Goal-Oriented Feature Extraction and Communication Open
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features are maximally relevant to a specific learning or inference t…
View article: Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting
Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting Open
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 computational burden to nearby Edge Cloud Servers (ECSs). This opens ne…
View article: Goal-Oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Goal-Oriented Communications for the IoT: System Design and Adaptive Resource Optimization Open
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiven…
View article: Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization Open
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiven…
View article: Multi-User Goal-Oriented Communications With Energy-Efficient Edge Resource Management
Multi-User Goal-Oriented Communications With Energy-Efficient Edge Resource Management Open
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 common challenge in running inference tasks from remote is to…
View article: Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management
Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management Open
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 common challenge in running inference tasks from remote is to…
View article: Low-Complexity PAPR Reduction by Coded Data Insertion on DVB-T2 Reserved Carriers
Low-Complexity PAPR Reduction by Coded Data Insertion on DVB-T2 Reserved Carriers Open
Digital terrestrial video broadcasting systems based on orthogonal frequency division multiplexing (OFDM), such as the second generation of terrestrial digital video broadcasting standard (DVB-T2), often include carriers reserved for peak-…
View article: Dynamic Resource Allocation for Multi-User Goal-oriented Communications at the Wireless Edge
Dynamic Resource Allocation for Multi-User Goal-oriented Communications at the Wireless Edge Open
This paper proposes a wireless, goal-oriented, multi-user communication system assisted by edge-computing, within the general framework of Edge Machine Learning (EML). Specifically, we consider a set of mobile devices that, exploiting conv…
View article: Single- and Multi-Carrier Systems Affected by Impulsive Noise: Covid-19 View
Single- and Multi-Carrier Systems Affected by Impulsive Noise: Covid-19 View Open
In this work we present a fully stochastic model of performance analysis of single- and multi-carrier modulations (SCM and MCM) in communication systems affected by impulsive noise. The key performance of the model is the symbol error rate…
View article: Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks
Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks Open
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 strategies to select radio parameters, sampling set, and har…
View article: GMM-based Symbol Error Rate Prediction for Multicarrier Systems with Impulsive Noise Suppression
GMM-based Symbol Error Rate Prediction for Multicarrier Systems with Impulsive Noise Suppression Open
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 communication systems. Indeed, although an exact closed-form ex…
View article: Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks
Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks Open
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 strategies to select radio parameters, sampling set, and har…
View article: K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions Open
This paper presents a feasibility study for a very high data rate receiver operating in the K/Ka-band suitable to future Moon exploration missions. The receiver specifications are outlined starting from the mission scenario and from a care…
View article: Real-Time Generation of Standard-Compliant DVB-T Signals
Real-Time Generation of Standard-Compliant DVB-T Signals Open
This paper proposes and discusses two software implementations of the DVB-T modulator, using C++ and MATLAB, respectively. All the key features of the DVB-T standard are included. The C++ DVB-T modulator, incorporated into the Iris framewo…
View article: Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies Open
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 recast two classical adaptive algorithms in the graph sign…
View article: Distributed Recursive Least Squares Strategies For Adaptive Reconstruction Of Graph Signals
Distributed Recursive Least Squares Strategies For Adaptive Reconstruction Of Graph Signals Open
Publication in the conference proceedings of EUSIPCO, Kos island, Greece, 2017
View article: Optimal Sampling Strategies For Adaptive Learning Of Graph Signals
Optimal Sampling Strategies For Adaptive Learning Of Graph Signals Open
Publication in the conference proceedings of EUSIPCO, Kos island, Greece, 2017
View article: Observing and Tracking Bandlimited Graph Processes
Observing and Tracking Bandlimited Graph Processes Open
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 far, the prior art is mostly focused on (sub)sampling sing…
View article: Distributed Adaptive Learning of Graph Signals
Distributed Adaptive Learning of Graph Signals Open
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 distributed reconstruction, with guaranteed performance in ter…
View article: 2-Dimensional finite impulse response graph-temporal filters
2-Dimensional finite impulse response graph-temporal filters Open
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 FIR graph filters do not capture the time variations of the i…
View article: On the Equivalence of Maximum SNR and MMSE Estimation: Applications to Additive Non-Gaussian Channels and Quantized Observations
On the Equivalence of Maximum SNR and MMSE Estimation: Applications to Additive Non-Gaussian Channels and Quantized Observations Open
The minimum mean-squared error (MMSE) is one of the most popular criteria for\nBayesian estimation. Conversely, the signal-to-noise ratio (SNR) is a typical\nperformance criterion in communications, radar, and generally detection theory.\n…
View article: Least Mean Squares Estimation of Graph Signals.
Least Mean Squares Estimation of Graph Signals. Open
In many applications spanning from sensor to social networks, transportation systems, gene regulatory networks or big data, the signals of interest are defined over the vertices of a graph. The aim of this paper is to propose a least mean …
View article: IEEE Transactions on Signal Processing publication information
IEEE Transactions on Signal Processing publication information Open
The Signal Processing Society is an organization, within the framework of the IEEE, of members with principal professional interest in the technology of transmission, recording, reproduction, processing, and measurement of speech; other au…