AIDRC Seminar Series - Prof. Emil Björnson

Title:

How to Use Reconfigurable Intelligent Surfaces in Practical Scenarios?

Abstract:

There are many ways in which a reconfigurable intelligent surface (RIS) can improve the communication channel between a transmitter and a receiver. The larger the surface is, the higher the potential communication performance gains. The gains come from adapting the RIS configuration to the channels from the transmitter and to the receiver, which must be estimated in practice. The estimation challenge grows with the number of RIS elements, both in terms of computational complexity and signal resources that must be dedicated to pilot signaling. The problem is much different from conventional multi-antenna communications since the RIS is essentially blind, so its configurations must be designed elsewhere. The relative cost of channel estimation is particularly large in practical scenarios with user mobility and/or transmission of short data packets.

In this talk, we will first consider how the length of the data packet determines how much resources are worth spending on channel estimation, and how to operate the RIS when the estimation resources are scarce. Next, we will consider how physical geometry can be utilized to simplify channel estimation, with the particular aim of minimizing the estimation overhead. Two types of geometric characteristics can be utilized: the array geometry of the RIS and the distribution of multipath components in the environment. We will demonstrate analytically how the necessary pilot resources can be vastly reduced depending on how much prior geometrical information is available.

Bio:

Emil Björnson is a Professor of Wireless Communication at the KTH Royal Institute of Technology, Stockholm, Sweden. He is an IEEE Fellow, Digital Futures Fellow, and Wallenberg Academy Fellow. He has a podcast and YouTube channel called Wireless Future. His research focuses on multi-antenna communications and radio resource management, using methods from communication theory, signal processing, and machine learning. He has authored three textbooks and has published a large amount of simulation code.

He has received the 2018 and 2022 IEEE Marconi Prize Paper Awards 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 received six Best Paper Awards at conferences.