Picture of Francesca Diedolo

M.Sc. Francesca Diedolo

Technical University of Munich

Chair of Communications Engineering (Prof. Kramer)

Postal address

Postal:
Theresienstr. 90
80333 München

Biography

  • Doctoral researcher at the Chair of Communications Engineering at TUM since February 2021
  • M.Sc. in Communications Engineering at TUM, 2018-2020
  • B.Sc. in Electrical Engineering and Information Technology at Università degli Studi di Padova, 2015-2018

Teaching

Machine Learning for Communications (WS 21/22, WS 22/23, WS 23/24)

Wireless Communications Laboratory (SS22)

Available Theses

Theses in Progress

Probabilistic Principal Component Analysis

Description

Principal component analysis (PCA) [1] is a famous technique for feature extraction and dimensionality reduction. Despite its widespread use and effectiveness, PCA is constrained by its linear nature.

Probabilistic PCA [2] improves upon classical PCA by linking it with maximum likelihood estimation based on a probability density model of the observed data.

The task of the student is to read about probabilistic PCA. The  student should understand the underlying theoretical concepts of the algorithm, its application spectrum, as well as its limitations.

 

[1] https://arxiv.org/pdf/1404.1100.pdf

[2] https://www.cs.columbia.edu/~blei/seminar/2020-representation/readings/TippingBishop1999.pdf

Prerequisites

Information Theory, Linear Algebra

Supervisor:

Publications

2024

  • Pabst, D.; Prinz, T.; Diedolo, F.; Wiegart, T.; Böcherer, G.; Hanik, N.; Kramer, G.: Neural network equalizers and successive interference cancellation for bandlimited channels with a nonlinearity. IEEE International Symposium on Information Theory, 2024 more… BibTeX
  • Plabst, D.; Prinz, T.; Diedolo, F.; Wiegart, T.; Boecherer, G.; Hanik, N.; Kramer, G.: Neural network equalizers and successive interference cancellation for bandlimited channels with a nonlinearity. IEEE Trans. Commun., 2024 more… BibTeX

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 more… 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 more… 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 more… BibTeX Full text ( DOI )