Our lab is equipped with hardware and software for emulation of cellular networks. The emulation is based on a software platform OpenAirInterface (OAI), developed by the OpenAirInterface Software Alliance, where CTU in Prague is a member. Furthermore, we exploit open source software for development and validation of our theoretical solutions. The open source software enables us to modify all communication parameters and the code so we can do research beyond the standardized communication protocols. To validate the bleeding edge solutions we are also implementing our ideas from a scratch.

Our lab is equipped with:

  • USRPs B205 mini (SDR on flying base station)
  • USRPs B210 (SDR for fixed base stations and user equipment)
  • USRPs N310 (SDR for fixed base stations and user equipment for 5G)
  • PCs and laptops for emulation of radio and core network
  • Unmanned Aerial Vehicle – hexa-copter (flying base station)
  • Intel NUC for emulation of cellular network on flying base station
  • Two MEC servers (Intel Xeon Gold 6154 @3GHz, 36 cores, 256 GB RAM)
  • Smartphones, tablets and USB dongles
  • Universal Radio Communication Tester & shielded box (provided by CETIN)


  • OAI RAN and Core Network both for 4G and 5G emulation.
  • GNU Radio for emulation of vehicular communication networks
  • NS-3 for Cloud Radio Access Network development.

Research topics carried out in our lab

In our lab we work on development and implementation of the bleeding edge solutions based on our theoretical work and industry related projects. Some of these projects and topics are shown below.

Development of Flying base station (FlyBS)
We have developed a FlyBS that exploits a hexacopter as its base to which we have attached Intel NUC and USRP B205 mini to create a 4G network. The FlyBS is then connected via a 4G dongle to an emulated 4G ground network. More details can be found in documentation and the FlyBS in action can be seen on this link.
Prediction of the channel quality via a Deep Neural Network
We have implemented a solution for prediction channel quality between a mobile device and its serving base station. This work is based on M. Najla, Z. Becvar, P. Mach, D. Gesbert, “Predicting Device-to-Device Channels from Cellular Channel Measurements: A Learning Approach“, IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7124 – 7138, 2020.
Multi-access Edge Computing
Development of the Multi-access Edge testbed based on the OAI with Open vSwitch based local breakpoint. For the evaluation we exploit an offloadable Android application Percipio that has been developed within our lab. The developed testbed with the application, enables not only measuring communication latency but also energy consumption of the mobile devices.
Self-optimizing mobile networks
Implementation of the mobile device transmission power control to prolong the device battery lifetime via Mobile Edge Computing. The details are in bachelor thesis of Jakub Novy “Mobile network optimization exploiting Multi-Access Edge Computing”.
Design and implementation of scheduling (resource allocation)
Implementation of the centralized version of the Game theoretic scheduler proposed in I. Bistritz and A. Leshem. “Game theoretic dynamic channel allocation for frequency-selective interference channels.“, IEEE Transactions on Information Theory 65, no. 1 (2018): 330-353.
Development and validation of communication solutions for autonomous railways
We are working on developing of communication technologies to enable autonomous railways in collaboration with our industrial partners.

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