Projects

The ns-3 quic module is available on Github and on the ns-3 App Store. The development effort is lead by Alvise De Biasio, Federico Chiariotti and me, at the SIGNET Lab, University of Padova. Check out this paper for more details.

The ns-3 mmWave module is available on Github and on the ns-3 App Store. The development effort is lead by NYU Wireless and University of Padova. I developed the DC framework described in this and this paper, available in the new-handover branch of the NYU repo, the IAB framework (available here). Check out also the implementation of the 3GPP channel model for the 6-100 GHz band.

I created a fork of Dissertate, a modern LaTeX template for a thesis or disseration, adapting it to the UNIPD style. This is not an official template, check if your Department has specific requirements for the Thesis template. Here’s a sample.

LENA-PLUS is a version of the LTE-EPC Network simulAtor (LENA) extended with a realistic LTE RACH model. It is based on the ns-3 LTE module and backward compatible with it. The code can be found on Github. Some results on RACH congestion obtained with this tools are available on IEEExplore or arXiv.

Wireless Systems and Networks class final project, by Michele Polese and Stefano Olivotto. Network coding techniques can be used to efficiently transmit data over wireless links and networks. In this project we will describe an implementation of a sender/receiver pair that by using network coding and ARQ mechanisms transmits a file over different kind of connections. We will show that the choice of different parameters outlines trade-offs in terms of different performance metrics, and that the system reaches high efficiency and goodput when used over a real connection from Padova to Lausanne.

Channel Coding class final project, for which I implemented and evaluated the performance of the ITU-T G.975.1 binary LDPC code. This code is used with DWDM submarine systems in order to perform Forward Error Correction. See the presentation for more details and the results of the performance evaluation.

Image and Video Analysis class final project, by Michele Polese, Davide Magrin and Andrea Dittadi. In this project we introduced two descriptors that can be used for hand gesture recognition by means of machine learning techniques, and we provided some performance analysis of such descriptors. We used a support vector machine (SVM) as classifier, training it with pairs consisting of some image descriptor and the corresponding desired output class. After the training phase, the SVM was used to classify new hand gestures.

Telecommunication Networks class final project, by Michele Polese, Davide Magrin and Paolo Scaramuzza. In this project we introduced the Homework Inherent Protocol Such That Efficiency Rises (HIPSTER), a file transfer protocol designed to work on top of the UDP transport layer. The aim of the protocol is to minimize transfer time over an unreliable transport channel, which introduces delay and drops packets. HIPSTER is able to reach low transfer times despite the unreliable channel: read the results of the performance evaluation in this report.