Foto von Francesca Diedolo

M.Sc. Francesca Diedolo

Technische Universität München

Lehrstuhl für Nachrichtentechnik (Prof. Kramer)

Postadresse

Postal:
Theresienstr. 90
80333 München

Biografie

  • Seit Februar 2021 wissenschaftliche Mitarbeiterin am Lehrstuhl für Nachrichtentechnik der TUM
  • M.Sc. in  Communications Engineering an der TUM, 2018-2020
  • B.Sc. in Elektro- und Informationstechnik an der Università degli Studi di Padova, 2015-2018

Abschlussarbeiten

Angebotene Abschlussarbeiten

Factor Graphs and the Sum-Product Algorithm

Beschreibung

 

This paper [1] introduces factor graphs and describes the sum-product algorithm, which is a generic message-passing algorithm operating on factor graphs. The algorithm computes various marginal functions associated with the global function.  

This algorithm is very powerful, in fact, a surprisingly wide variety of algorithms developed in the artificial intelligence, signal processing, and digital communications communities can be seen as specific instances of the sum-product algorithm, operating in an appropriately chosen factor graph. Some examples are the forward/backward algorithm, the Viterbi algorithm, Pearl’s belief propagation algorithm, the iterative turbo decoding algorithm, the Kalman filter, and even certain FFT algorithms.

The task of the student is to learn and understand factor graphs and the sum-product algorithm. The student can then relate and analyze other known algorithm under the framework of the sum-product algorithm.

[1] https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=910572

Voraussetzungen

Information Theory

Betreuer:

Laufende Abschlussarbeiten

Spiking Neural Networks for IM/DD systems

Beschreibung

During the internship the student investigates spiking neural networks to perform equalization and demapping at the receiver side of an intensity-modulation / direct-detection communication system.

Betreuer:

Francesca Diedolo - Georg Böcherer (Huawei)

Radio Traffic Classification with Deep Neural Networks

Beschreibung

The goal of this internship is to classify raw radio signal data to the respective application layer traffic type using machine learning.

 

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7905847

 

Betreuer:

Francesca Diedolo - Dr. Alberto Viseras (Motius GmbH)

Publikationen

2022

  • Diedolo, F.; Böcherer, G.; Schädler M.; Calabrò S.: Nonlinear Equalization for Optical Communications Based on Entropy-Regularized Mean Square Error. European Conference on Optical Communication (ECOC), 2022 mehr… BibTeX
  • Schädler, M.; Böcherer, G.; Diedolo, F.; Calabrò, S.: Nonlinear Component Equalization: A Comparison of Deep Neural Networks and Volterra Series. European Conference on Optical Communication (ECOC), 2022 mehr… BibTeX

2021

  • Böcherer, G.; Diedolo, F.; Pittala, F.: Label Extension for 32QAM: The Extra Bit for a Better FEC Performance-Complexity Tradeoff. 2020 European Conference on Optical Communications (ECOC), IEEE, 2021 mehr… BibTeX Volltext ( DOI )