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See the future in our lab

Our lab is equipped with hardware and software for emulation of mobile networks. The emulation is based on the open source software platform OpenAirInterface (OAI), developed by the OpenAirInterface Software Alliance, where we are a member. We exploit the software for development and validation of our theoretical solutions. The open source software enables us to modify arbitrary communication parameters and implemet our own solutions to go beyond the standardized communication protocols. To validate the cutting edge solutions, we also implement our ideas from a scratch.

Demos and Experiments developed in our lab

Flying base station (FlyBS)

Our developed FlyBS is a hexacopter with Radio Access Network based on Open Air Interface. The mobile network runs on the attached Intel NUC and USRP B205 mini. The connectivity to the core of a mobile network is provided via LTE dongle. More details can be found in documentation.

Prediction of the channel quality via Deep Neural Network

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In this demo the quality of channel between two devices is predicted based on the idea proposed in 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. As D2D communication is not yet fully available in OAI, we demonstrate prediction between a user equipment and a base station with knowledge of channels between the user equipment and few neighboring base stations.

Multi-access Edge Computing

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Our Multi-access Edge testbed is based on the OAI with Open vSwitch local breakpoint. For the evaluation we exploit an Android application for augmented reality Percipio developed within our lab that supports computation offloading . The MEC testbed with the application enables not only to demonstrate MEC capabilities, but we can also optimize and measure communication latency and/or energy consumption of the mobile devices.

Self-optimized RAN

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The self-optimization of RAN is done via developed API providing communication interface to MEC, where optimization of RAN is performed. We demonstrate the self-optimization capabilities via control of UE transmission power. The power control reduces the energy consumed by the UE and prolongs its battery lifetime. The details are in bachelor thesis of Jakub Novy “Mobile network optimization exploiting Multi-Access Edge Computing”.

Radio resource scheduler

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We demonstrate efficiency of a centralized version of scheduler based on game theory, as 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. This scheduler maximizes the data rate of each user by achieving close to optimal pure Nash equilibria.

Communication for autonomous railways

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We develop communication technologies to support communication of autonomous trains in collaboration with Ixperta (Czech company).

Our equipment

Hardware:

  • 2x USRPs B205 mini (SDR on flying base station)
  • 6x USRPs B210 (SDR for fixed base stations and user equipment)
  • 8x USRPs N310 (SDR for fixed base stations and user equipment for 5G)
  • PCs and laptops for emulation of radio and core network
  • Flying base station (drone – hexa-copter and HW+SW for emulated mobile connectivity)
  • Two MEC servers (Intel Xeon Gold 6154 @3GHz, 36 cores, 256 GB RAM)
  • Smartphones, tablets and USB dongles

Software:

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