• 06/11/2024: PEPR PC10 Meeting at the INSA around CorteXlab
• 27/08/2024: Presence at GNU Radio European Days
• CorteXlab is part of the PEPR Reseaux futurs Project
• CorteXlab is also part of the EU INSTINCT Project

The FIT/CorteXlab testbed is one of 9 testbeds of the Future Internet of Things (FIT) Initiative. All the testbeds are dedicated to radio communication research but FIT/CorteXlab is the only one specifically designed for Software Defined and Cognitive Radio research.

With its 180m² EM shielded room that ensures experiment reproducibility and its 40 high-end SDR-capable radio nodes, FIT/CorteXlab provides a unique setup to experiment on new state-of-the-art radio techniques.

More info on the official FIT/CorteXlab website: www.cortexlab.fr.

A recent video presentation of FIT/CorteXlab (and the S3-CAP framework) can be found here.

• Because it goes down to the physical layer:
  It is one of the only platforms in the world enabling you to work down to the physical layer by deploying your own physical layer implementations.
• Because it allows for large scale experiments:
  With up to a total of 40 nodes, the possibilities of experimentation scenarios are widened to a level never seen before.
• Because it ensures reproducibility:
  As the room is isolated from the outer world, you can experiment on any band from 300MHz to 4GHz with no interference, thus running the exact same experiment twice will give you the exact same (statistical) results.
• Because you can do everything from home:
  All systems in FIT/CorteXlab are fully automated and the testbed is accessible from the Internet for everyone so once you get an account you just need your PC and an Internet connection to use it!
• Because it's free to use!

To get started with CorteXlab, follow these short introductory steps:

• Create an account
• Access the testbed
• Book the testbed
• Follow some CorteXlab tutorials

• Slotted, synchronised and modular framework for the evaluation of channel access policies in dense IoT network using FIT/CorteXlab, A Paris et al., 2022
• Deep Learning-based Transmitter identification on the physical layer, C Morin et al., 2020
• Distributed beamforming for wireless power transfer, F Hutu et al., 2020
• Study of Distributed Beamforming for Wireless Power Transfer in Presence of RF Impairments, F Hutu et al., 2020
• Simultaneous information and energy transmission: towards feasible systems, N Vinyes, 2019
• Framework for PHY-MAC layers Prototyping in Dense IoT Networks using FIT/CorteXlab Testbed, O Oubejja et al., 2019
• Transmitter classification with supervised deep learning, C Morin et al., 2019
• Beyond LoRa and NB-IoT: Proposals for Future LPWA Systems, G Vivier et al., 2019
• Radio Datasets for radioelectric spectrum analysis in Colombian Scenarios using GNU radio into the FIT/CorteXlab, EP Guillen et al., 2018.
• Learning to fingerprint: physical layer identification, C Morin et al., 2018
• Ultra Narrow Band based IoT networks, Y Mo, 2018
• Reliable and Reproducible Radio Experiments in FIT/CorteXlab SDR Testbed: Initial Findings, L Cardoso et al., 2017
• Coding for Caching in 5G Networks, Y Fadlallah et al., 2017

More here

Mentioning CorteXlab in your research publications allow us to measure the impact of the usage of the testbed, which in turn keeps the testbed working.

If you have used CorteXlab in your research publications there are two ways to mention it:

Please use this phrase in your acknowledgements line:

Experiments presented in this work were carried out using the FIT/CorteXlab testbed. (see http://wiki.cortexlab.fr). 

Cite the following paper: http://ieeexplore.ieee.org/abstract/document/6849176/

With bibtex:

@inproceedings{massouri2014cortexlab,
  title={CorteXlab: An open FPGA-based facility for testing SDR \& cognitive radio networks in a reproducible environment},
  author={Massouri, Abdelbassat and Cardoso, Leonardo and Guillon, Benjamin and Hutu, Florin and Villemaud, Guillaume and Risset, Tanguy and Gorce, Jean-Marie},
  booktitle={Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on},
  pages={103--104},
  year={2014},
  organization={IEEE}
}