Prof. Emil Björnson
Emil Björnson received the M.S. degree in engineering mathematics from Lund University, Sweden, in 2007, and the Ph.D. degree in telecommunications from the KTH Royal Institute of Technology, Sweden, in 2011. From 2012 to 2014, he was a post-doc at the Alcatel-Lucent Chair on Flexible Radio, SUPELEC, France. From 2014 to 2021, he held different professor positions at Linköping University, Sweden. In 2020-2021, he was a part-time Visiting Full Professor at the KTH. From 2022, he is a tenured Full Professor of Wireless Communication at KTH.
He has authored the textbooks Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013), Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency (2017), and Foundations of User-Centric Cell-Free Massive MIMO (2021). He is dedicated to reproducible research and has made a large amount of simulation code publicly available. He performs research on MIMO communications, radio resource allocation, machine learning for communications, and energy efficiency. He has been on the Editorial Board of the IEEE Transactions on Communications since 2017. He has been a member of the Online Editorial Team of the IEEE Transactions on Wireless Communications since 2020. He has been an Area Editor in IEEE Signal Processing Magazine since 2021. He has also been a guest editor of multiple special issues.
He has performed MIMO research for 15 years, his papers have received more than 16000 citations, and he has filed more than twenty patent applications. He is a host of the podcast Wireless Future and has a popular YouTube channel with the same name. He is an IEEE Fellow, a Wallenberg Academy Fellow, a Digital Futures Fellow, and an SSF Future Research Leader. He has received the 2014 Outstanding Young Researcher Award from IEEE ComSoc EMEA, the 2015 Ingvar Carlsson Award, the 2016 Best Ph.D. Award from EURASIP, the 2018 IEEE Marconi Prize Paper Award in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society 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, and the 2021 IEEE ComSoc RCC Early Achievement Award. He also co-authored papers that received Best Paper Awards at the conferences, including WCSP 2009, the IEEE CAMSAP 2011, the IEEE SAM 2014, the IEEE WCNC 2014, the IEEE ICC 2015, and WCSP 2017.
Title: Wireless Networks With Uniform Performance: Distributed MIMO and Sequential Fronthaul
Abstract: The peak performance in wireless networks has grown over the last decades. In current 5G deployments, the spectral efficiency improvements are mainly thanks to the Massive MIMO technology. This means that the base station consists of arrays with around 64 antenna-integrated radios, which are utilized to focus each signal at its desired receiving user and transmit it to multiple users simultaneously. This is just the beginning of the MIMO (multiple-input, multiple-output) story. As the data traffic increases, we can continue adding more antennas to the base station arrays to support more users, but the users will always observe large data rate variations depending on their location in the cell due to distance-based pathloss and inter-cell interference.
In this keynote, we will consider an alternative approach for future networks: serve the users by antennas that are distributed over the coverage area. By shifting from a world where base stations are surrounded by users, to a world where each user is surrounded by antennas, we can deliver almost uniformly good spectral efficiency wherever the user is. This concept has recently been called Cell-Free Massive MIMO but has its roots in earlier concepts for base station cooperation. A core practical challenge is to deploy a massively distributed MIMO array affordably. One potential approach is to use radio stripes, which are cables with integrated antennas. We will take a close look at how these can be implemented and recent results on how communications algorithms can be designed to exploit their special characteristics.