We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:


 Artificial Intelligence

 Nonlinear Time Series Analysis

 Web Research 


 Music Technology

 Interactive  Technologies

 Barcelona MedTech

 Natural Language  Processing

 Nonlinear Time Series  Analysis


Wireless Networking

Educational Technologies




Back Barrachina-Muñoz S, Wilhelmi Roca FJ, Bellalta B. Dynamic Channel Bonding in Spatially Distributed High-Density WLANs. IEEE Transactions on Mobile Computing

Barrachina-Munoz S, Wilhelmi F, Bellalta B. Performance Analysis of Dynamic Channel Bonding in Spatially Distributed High Density WLANs. arXiv preprint.


In this paper we discuss the effects on throughput and fairness of dynamic channel bonding (DCB) in spatially distributed high density (HD) wireless local area networks (WLANs). First, we present an analytical framework based on continuous time Markov networks (CTMNs) for depicting the phenomena given when applying different DCB policies in spatially distributed scenarios, where nodes are not required to be within the carrier sense of each other. Then, we assess the performance of DCB in HD IEEE 802.11ax WLANs by means of simulations. Regarding spatial distribution, we show that there may be critical interrelations among nodes – even if they are located outside the carrier sense range of each other – in a chain reaction manner. Results also show that, while always selecting the widest available channel normally maximizes the individual long-term throughput, it often generates unfair scenarios where other WLANs starve. Moreover, we show that there are scenarios where DCB with stochastic channel width selection improves the latter approach both in terms of individual throughput and fairness. It follows that there is not a unique DCB policy that is optimal for every case. Instead, smarter bandwidth adaptation is required in the challenging scenarios of next-generation WLANs.

DOI: 10.1109/TMC.2019.2899835

Additional material:

  • Spatial-Flexible Continuous Time Markov Network( SFCTMN), analytical framework based on Continuous Time Markov Networks (CTMNs). https://github.com/ sergiobarra/SFCTMN
  • Komondor, a wireless networks simulator built on top of COST library https://github.com/wn-upf/ Komondor
  • arXiv pre-print: https://arxiv.org/abs/1801.00594