Mattia Merluzzi

Mattia Merluzzi
Cea Leti · DSYS

Doctor of Philosophy

About

38
Publications
3,099
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212
Citations
Introduction
I currently work on dyamic resource allocation strategies for edge machine learning in the context of edge computing aided 5G and beyond networks. The goal is to explore new trade-offs between network energy consumption, service delay, and learning accuracy. I use tools from Lyapunov stochastic optimization, convex optimization, and machine learning.

Publications

Publications (38)
Preprint
Full-text available
The advent of Reconfigurable Intelligent Surfaces (RISs) in wireless communication networks unlocks the way to support high frequency radio access (e.g. in millimeter wave) while overcoming their sensitivity to the presence of deep fading and blockages. In support of this vision, this work exhibits the forward-looking perception of using RIS to enh...
Preprint
Full-text available
We present a dynamic resource allocation strategy for energy-efficient and Electromagnetic Field (EMF) exposure aware computation offloading at the wireless network edge. The goal is to maximize the overall system sum-rate of offloaded data, under stability (i.e. finite end-to-end delay), EMF exposure and system power constraints. The latter compri...
Preprint
Full-text available
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), and finally processed remotely to output the result of training and/or inference phases. This involves heterogeneous resources, such as radio, computing and learning relate...
Preprint
Full-text available
Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial Intelligence (AI) functions instantiated in the network to facilitate...
Conference Paper
Full-text available
We propose a dynamic resource allocation algorithm in the context of future wireless networks endowed with edge computing, to enable accurate energy efficient classification with end-to-end delay guarantees. In our scenario, sensor devices continuously upload data to an Edge Server (ES) for classification purposes. Merging Lyapunov stochastic optim...
Preprint
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 Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Buil...
Article
Full-text available
Private networks will play a key role in 5G and beyond to enable smart factories with the required better deployment, operation and flexible usage of available resource and infrastructure. 5G private networks will offer a lean and agile solution to effectively deploy and operate services with stringent and heterogeneous constraints in terms of reli...
Article
Full-text available
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 consumption of the overall system, comprising multiple User Equipment (UE), an access point (AP), and an edge server (ES), under constraints on the end-to-end service delay an...
Preprint
The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-bas...
Preprint
The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-bas...
Preprint
In this work, we study the problem of energy-efficient computation offloading enabled by edge computing. In the considered scenario, multiple users simultaneously compete for limited radio and edge computing resources to get offloaded tasks processed under a delay constraint, with the possibility of exploiting low power sleep modes at all network n...
Preprint
In this work, we study the problem of energy-efficient computation offloading enabled by edge computing. In the considered scenario, multiple users simultaneously compete for limited radio and edge computing resources to get offloaded tasks processed under a delay constraint, with the possibility of exploiting low power sleep modes at all network n...
Article
Full-text available
The aim of this paper is to propose a resource allocation strategy for dynamic training and inference of machine learning tasks at the edge of the wireless network, with the goal of exploring the trade-off between energy, delay and learning accuracy. The scenario of interest is composed of a set of devices sending a continuous flow of data to an ed...
Preprint
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided 5G (and beyond) networks. The goal is to minimize the energy consumption of the overall system, comprising users and network elements, under constraints on the end-to-end service delay and the packet error rate performance over th...
Article
Full-text available
The goal of this work is to propose an energy-efficient algorithm for dynamic computation offloading, in a multi-access edge computing scenario, where multiple mobile users compete for a common pool of radio and computational resources. We focus on delay-critical applications, incorporating an upper bound on the probability that the overall time re...
Conference Paper
Full-text available
In this paper, we address the problem of dynamic computation offloading with Multi-Access Edge Computing (MEC), considering an Internet of Things (IoT) environment where computation requests are continuously generated locally at each device, and are handled through dynamic queue systems. In such context, we consider simple devices (e.g., sensors) w...
Conference Paper
Full-text available
In this paper, we address the problem of dynamic computation offloading with Multi-Access Edge Computing (MEC), where new requests for computations are continuously generated at each user equipment (UE), and are handled through dynamic queue systems. Building on stochastic optimization tools, we provide a dynamic algorithm that jointly optimize rad...
Conference Paper
Full-text available
Multi-Access Edge Computing (MEC) is one of the key technology enablers of the 5G ecosystem, in combination with the high speed access provided by mmWave communications. In this paper, among all services enabled by MEC, we focus on computation offloading, devising an algorithm to optimize computation and communication resources jointly with the ass...
Preprint
Two enablers of the 5th Generation (5G) of mobile communication systems are the high data rates achievable with millimeter-wave radio signals and the cloudification of the network's mobile edge, made possible also by Multi-access Edge Computing (MEC). In 5G networks, user devices may exploit the high capacity of their mobile connection and the comp...
Article
Full-text available
The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) c...

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Projects

Projects (2)
Project
5G CONNI is a joint EU-Taiwan project within the European Union Horizon 2020 program that brings together major players in ICT and Industry 4.0 from Europe and Taiwan with the joint vision of paving the way for industrial 5G applications and accelerating deployments https://5g-conni.eu/
Archived project
The goal of the project is to merge mmWave communications and the edge cloud to enable 5G services