This page contains a short list of topics for Laurea Theses

The list is not necessarily exaushive and may be also not frequently updated.

It is possible to contact me to evaluate other topics of reciprocal interest.

(Previous Topics)

Digital and Wireless Communications  

Topics are mostly focused on physical layer issues of wireless and wired communication systems (OFDM, LTE, 5G, WiFi, etc.).

In the past we mostly focused on performance analysis of multicarrier communication systems (OFDM, MC-CDMA, MC-DS-CDMA, etc.) both in linear and non-linear (PA, A/D, etc) environments, subject to frequency selective fading, frequency synchronization errors, impulsive noise, etc..

Another widely investigated topic has been time-varying channel estimation and equalization in wireless communication systems subject to high Doppler spread, also in the presence of multiple antennas (MIMO)., with an active scientific collaboration with Prof. G Leus, from TU-Delft University, also within Erasmus +  students exchanges.

Below are highligted some of the physical layer issues for future 5G cellular systems, which are expected to be deployed  worldwide by 2020, that may be objcet of a MS or BS Thesis.


Signal Processing on Graphs and Distributed Signal Processing


Signal processing on graphs (SPG) is a rather new theoretical subject, which is rapidly emerging in the technical literature and where signal are defined over a non-metric topological space such as a graph, rather than a metric space such as the real/integer fields of classical signal processing. This general tool, by capturing and exploiting the structural relations among the nodes of the networks (graph), can effectively model a wide class of phenomena such as the data or parameters in a communication network, or the information associated to nodes of a social network, or the measurements gathered by sensors in a power-grid or a vehicular network, as well as the concentration of proteins in a gene regulatory network.

Such a kind of data, described as a signal on graph, are typically characterized by high volumes (Big-data) in several application scenarios (communication network, cloud, social networks, etc. SPG can be useful also in this view because it is possible to extended to this new paradigm the classical Shannon-Nyquist Sampling Theorem that, under mild conditions, lets to reconstruct the overall data on all the network from samples taken by a limited and opportunely selected number of nodes, with controllable reconstruction error.

This fact actually suggests that also the extraction of valuable, possibly statistical, information from huge data-sets (big data analytics) may be possible by processing only the data on a reduced number of nodes, by exploiting the topological structure of the graph.
The SPG framework naturally merges with adaptive and distributed signal processing, by modelling each node (or part of them) not only as a source of information (data) but also as a potential computational unit that can communicate with neighborhoods nodes over a communication network. Thus, classical SP algorithms, such as the LMS, RLS, Kalman filtering, and statistical inference (e.g., estimation, detection, hypothesis testing, etc.)  can be extended to SPG to realize a decentralized computing and communication network that is capable to execute complex tasks in a distributed fashion, such as tracking the evolution of a dynamic field or a moving target in a sensor network, infer the status and possibly detect faults of a power-grid by a limited number of intelligent power meters, as well as traffic analysis and control in a vehicular network, or empower the functionality of an automated recommendation system, such those used by Netflix for movies or by Amazon or Google for electronic merchandising.

Our group is investigating this subject in collaboration wih Prof. S. Barbarossa, from University of Rome, "La Sapienza" .

Currently we are looking for motivated students that are willing to test and customize the general purpose SPG algorithms produced by the research community, on real applications such those highlighted before (enviromental sensor networks, power-grid networks, social networks, fault-detection in communication networks, fraud detection, vehicular networks, etc.).

The Thesis could be either signal and graph model characterization for a specific applications, data collection or access from public or private databases, developing of new algorithms for specific applications, as well as implementation of SPG algorithms on distributed, parallel, or cloud-based platforms.