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

Neural Networks (NNs) for Direct Detection

Beschreibung

In [1] we consider a short-reach fiber-optic link with a single photodiode at the receiver, which is a so-called direct detector (DD). The DD outputs a signal, propotional to the squared magnitude of its input. At first glance, this makes phase modulation challgenging. In [1] we showed that inter-symbol intereference (ISI) can be used to retrieve the phase. A suboptimal symbol-wise MAP detector was then proposed for phase retrieval. However, the detector exhibits a large complexity, which grows exponentially in the amount of ISI.

The task of the student is to efficiently approximate the MAP detector using a NN.  An appropriate NN type/structure needs to be selected. Finally, lower bounds on the achievable rates are computed to evaluate the performance of the NN and compare it to the MAP detector [1].

[1] D. Plabst et al., "Achievable Rates for Short-Reach Fiber-Optic Channels With Direct Detection," in Journal of Lightwave Technology, vol. 40, no. 12, pp. 3602-3613, 15 June15, 2022, doi: 10.1109/JLT.2022.3149574.

 

 

Voraussetzungen

Machine Learning

Statistical Signal Processing

Betreuer:

Laufende Abschlussarbeiten

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)

Geometric and Probabilistic Constellation Shaping via end-to-end Learning

Beschreibung

Betreuer:

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

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 )