Lecturer Section: Emil Björnson

Emil Björnson

Dr. Emil Björnson

Department of Signal Processing,

KTH Royal Institute of Technology



Signal Processing for Optimal Radio Resource Management: Fundamentals and Recent Multi-Cell Advances



Future cellular networks are expected to improve greatly in terms of both spectral efficiency and energy efficiency. The key to fulfilling these ambitious and conflicting goals is the resource management; that is, how the time, frequency, power, and spatial resources are divided among the users. The classical approach has been to allocate orthogonal time and frequency resources to the users, which guarantees low inter-user interference but is wasteful from the perspective of area spectral efficiency. Modern base stations are equipped with multiple antennas which enable adaptive interference suppression by precoding; thus, spatially separated users can be served in parallel by directing each signal towards its intended receiver. Precoding brings huge new possibilities since the throughput ideally increases linearly with the number of transmit antennas. Unfortunately, the practical performance is limited by a variety of nonidealities, such as high computational complexity, cumbersome inter-cell coordination, unreliable channel knowledge, and heterogeneous user conditions.

This tutorial presents a general framework for joint modeling, analysis, and optimization of different cellular scenarios, including clustered joint transmission, coordinated beamforming, heterogeneous soft-cell deployments, cognitive radio, and spectrum sharing between operators. Based on this framework, the first part of the tutorial provides a thorough foundation to resource management from a multi-objective optimization perspective. We categorize different types of resource management problems in terms of computational complexity and describe the signal processing algorithms that solve them. We explain by simple examples what makes resource management difficult: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between optimizing different conflicting objectives such as aggregate system throughput, user fairness, and energy efficiency. Building on these insights, we present a pragmatic approach to optimize the resource management with low computational complexity.

The second part of the tutorial describes recent breakthroughs and applications in the area of resource management for cellular networks. In particular, we describe how to measure and optimize the energy efficiency, how to solve optimization problems distributively, and how practical nonidealities such has imperfect channel knowledge and hardware impairments can be taken into account in the system modeling and optimization. The tutorial is partially based on our book “Optimal Resource Allocation in Coordinated Multi-Cell Systems” written by Emil Björnson and Eduard Jorswieck. This book can be downloaded for free from the authors’ home pages.



Emil Björnson was born in Malmö, Sweden, in 1983. He received the M.S. degree in Engineering Mathematics from Lund University, Lund, Sweden, in 2007. He received the Ph.D. degree in Telecommunications from the Department of Signal Processing at KTH Royal Institute of Technology, Stockholm, Sweden, in 2011. He was then one of the first recipients of the International Postdoc Grant from the Swedish Research Council. From Sept. 2012 – Sept. 2014, this grant is funding a joint postdoctoral research position at the Alcatel-Lucent Chair on Flexible Radio, Supélec, Paris, France, and the Department of Signal Processing at KTH Royal Institute of Technology, Stockholm, Sweden. Starting January 2014, he is also employed as a Research Fellow at Linköping University, Linköping, Sweden.

Dr. Björnson is the first author of the monograph “Optimal Resource Allocation in Coordinated Multi-Cell Systems” published in Foundations and Trends in Communications and Information Theory, January 2013. His research interests include multi-antenna cellular communications, radio resource management, random matrix theory, estimation theory, stochastic signal processing, and mathematical optimization.

For his work on optimization of multi-cell MIMO communications, he received a Best Paper Award at the 2009 International Conference on Wireless Communications & Signal Processing (WCSP’09) and a Best Student Paper Award at the 2011 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP’11).