Emil björnson github
Hem / Teknik & Digitalt / Emil björnson github
That’s why we warmly invite you to take part in our 2025 user survey. He is dedicated to reproducible research and has published much simulation code. If you in any way use this code for research that results in publications, please cite our textbook as described above. degree in telecommunications from the KTH Royal Institute of Technology, Sweden, in 2011.
Award from EURASIP, the 2018 and 2022 IEEE Marconi Prize Paper Awards in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE ComSoc Fred W. Ellersick Prize, the 2019 IEEE Signal Processing Magazine Best Column Award, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award, the 2020 CTTC Early Achievement Award, the 2021 IEEE ComSoc RCC Early Achievement Award, the 2023 IEEE ComSoc Outstanding Paper Award, and the 2024 IEEE ComSoc Stephen O.
Rice Prize. He has been a Full Professor of Wireless Communication at KTH since 2020 and the Head of the Communication Systems division since 2024.
He has authored the textbooksOptimal Resource Allocation in Coordinated Multi-Cell Systems (2013),Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency (2017),Foundations of User-Centric Cell-Free Massive MIMO(2021), andIntroduction to Multiple Antenna Communications and Reconfigurable Surfaces (2024).
He is a Wallenberg Academy Fellow, a Digital Futures Fellow, and an SSF Future Research Leader. The code has been tested with QuaDRiGa version 1.4.8-571.
Since the running example in this monograph considers a setup with 16 cells, 100 antennas per BS, and 10 UEs per cell, some of the simulations require a lot of RAM to store the channel correlation matrices and channel realizations.
DOI: 10.1561/2000000093.
For further information about the book, please visit: https://www.massivemimobook.com
Simulation code: The repository also contains the code package that is distributed along with the textbook.
The simulation code is licensed under the GPLv2 license.
We encourage you to also perform reproducible research!
Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for the next generation of wireless communication networks because it has the potential to provide game-changing improvements in spectral efficiency (SE) and energy efficiency (EE).
It may not be redistributed without permission and may not be sold. From 2012 to 2014, he was a Post-Doctoral Researcher with the Alcatel-Lucent Chair on Flexible Radio, SUPELEC, France.
The code has been tested successfully on a MacBook Pro with 8 GB 1600 MHz DDR3 RAM and a 2.6 GHz Intel Core i5 processor, which should be viewed as a minimum requirement for using this code. We discourage the use of the solvers SDPT3 and SeDuMi since these crashed during the test. Björnson has performed MIMO research since 2006.
He co-hosts the podcast Wireless Future and has a popular YouTube channel with the same name. However, its net budget is shrinking. We are grateful for the feedback provided by our proof-readers Alessio Zappone (University of Cassino and Southern Lazio), Maximilian Arnold (University of Stuttgart), Andrea Pizzo (University of Pisa), Daniel Verenzuela, Hei Victor Cheng, Giovanni Interdonato, Marcus Karlsson, Antzela Kosta, Özgecan Özdogan (Linköping University), and Zahid Aslam (Siradel).
Emil Björnson has been supported by ELLIIT, CENIIT, and the Swedish Foundation for Strategic Research.
Luca Sanguinetti has been supported by the ERC Starting Grant 305123 MORE.
The authors' version of the textbook, which is found in this repository, is delivered for free personal use.
The scripts are named using the convention sectionX_figureY, which is interpreted as the script that reproduces Figure X.Y. A few scripts are instead named as sectionX_figureY_Z and will then generate both Figure X.Y and Figure X.Z.
The functions are used by the scripts to carry out certain tasks, such as initiating a simulation setup, generating channel correlation matrices, generating channel realizations, computing channel estimates, computing SEs, computing the power consumption, etc.
The Matlab data files are of the type .mat and contain measurement results or particular precomputed simulation results.
See each script and function for further documentation.
To generate Figures 7.2, 7.41, and 7.42, you need to solve convex optimization problems using CVX from CVX Research, Inc. (http://cvxr.com/cvx/).