Preben Mogensen

Bio: Preben Mogensen is an academic researcher from Aalborg University. The author has contributed to research in topics: Telecommunications link & Scheduling (computing). The author has an hindex of 64, co-authored 512 publications receiving 16042 citations. Previous affiliations of Preben Mogensen include Nokia & Bell Labs.

5G New Radio Mobility Performance in LEO-based Non-Terrestrial Networks

Abstract: As part of 3GPP standardization work for Release 17, non-terrestrial networks (NTNs) aim to bring 5G New Radio (NR) communications to unserved and isolated areas. Constellations of low Earth orbit (LEO) satellites have emerged as a promising asset for NTNs and a key enabler technology to provide truly seamless and ubiquitous connectivity through 5G. Varying and longer propagation delays compared to terrestrial networks, limited radio link budget and the inherent high-speed movement of LEO satellites introduce new challenges in the mobility management procedures. To guarantee robust service continuity and satisfactory user experience, the handover (HO) procedure in LEO satellite systems is critical. Motivated by this fact, this paper presents a first performance analysis of the conventional 5G NR HO algorithm in a LEO-based NTN deployment. We provide system-level simulations obtained for different values of HO margin and time-to-trigger. Furthermore, we compare the HO performance in NTN with two 3GPP terrestrial scenarios - urban macro and high-speed train. The simulation results show HO failures and radio link failures are a factor 10 higher for the NTN scenario, while the corresponding time in outage is 5 times longer. Finally, we analyze the key issues and suggest potential mobility enhancements

A distributed interference-aware rank adaptation algorithm for local area MIMO systems with MMSE receivers

Abstract: Typically, rank adaptation (RA) algorithms are aimed at balancing the trade-off between increasing the spatial gain and improving the interference resilience property. In this paper, we consider a small-cell/local area cellular system and propose a simple and distributed interference-aware rank adaptation algorithm aimed at maximizing the system sum throughput. The performance of the proposed algorithm is numerically evaluated in terms of the system sum throughput. Simulation results show that the proposed algorithm results in close to optimum throughput performance, and can provide up to 40% throughput gain over interference-unaware RA schemes.

An Agile Multi-Node Multi-Antenna Wireless Channel Sounding System

Abstract: The upcoming fifth-generation wireless technology application areas bring new communication performance requirements, mainly in terms of reliability and latency, but also in terms of radio planning, where the further detailed characterization of the wireless channel is needed. To address these demands, we developed an agile multi-node multi-antenna wireless channel sounding system, using multiple software-defined radio devices. The system consists of 12 testbed nodes which are controlled from a centralized testbed server. Each node features a control host computer and two multi-antenna universal software radio peripheral boards. By managing the transmission and reception of reference signals among all the distributed testbed nodes, the system can measure the channel conditions of all multiple independent radio links. At the same time, the distributed architecture of the testbed allows a large number of spatially distributed locations to be covered with only a few redeployments of the testbed nodes. As a consequence of this, the system favors the collection of a large number of distributed channel samples with limited effort within a short dedicated measurement time. In this paper, we detail the general testbed design considerations, along with the specific sounding signal processing implementations. As further support to the system design, we also include the results from different verification and calibration tests, as well as a real measurement application example.

Experimental Verification of Interference Mitigation Techniques for 5G Small Cells

Abstract: Inter-cell interference is the main performance limiting factor in the dense deployment of small cells targeted by the upcoming 5th Generation (5G) radio access technology. In this paper, we present an experimental evaluation of inter-cell interference mitigation techniques in a real indoor office deployment with four cells, where each cell features one Access Point (AP) and one User Equipment (UE). In particular, we compare traditional Frequency Reuse Planning (FRP) with the recently proposed Maximum Rank Planning (MRP) technique, which relies on the degrees of freedom offered by the multi-antenna transceivers for suppressing a number of interfering streams. Different receiver types are also considered, namely Interference Rejection Combining (IRC) and the interference unaware Maximum Ratio Combining (MRC). Each node in our software defined radio (SDR) testbed features a 2 x 2 MIMO transceiver built with the USRP N200 hardware by Ettus Research. The experimental results in a fully loaded network reveal the capability of the MRP technique to achieve higher throughput performance than FRP for 90% of the cases when IRC receivers are used. Lower network loads lead to further performance improvements for MRP.

A USRP-Based Multi-Antenna Testbed for Reception of Multi-Site Cellular Signals

Abstract: This paper presents a design of a Software Defined Radio (SDR) multi-antenna testbed able to record live cellular signals from multiple sites. This measurement setup based on Universal Software Radio Peripheral (USRP) boards, is used to record live Long Term Evolution (LTE) signals in sub-6 GHz frequency bands. Due to recording of raw I&Q samples, this fully digital testbed is suitable for variety of research activities spanning channel characterization and beamforming performance evaluation. We propose a phase calibration method based on transmission of a single out of band tone to overcome the uncertainty introduced by the USRP's lack of phase alignment. We demonstrate two use cases where the proposed testbed can be used and we validate its performance during two measurement campaigns with self-generated and real cellular signals.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

Abstract: Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].

Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

Abstract: An Integrated Agent Architecture for Software Defined Radio. Rapid-prototype cognitive radio, CR1, was developed to apply these.The modern software defined radio has been called the heart of a cognitive radio. Cognitive radio: an integrated agent architecture for software defined radio. Http:bwrc.eecs.berkeley.eduResearchMCMACR White paper final1.pdf. The cognitive radio, built on a software-defined radio, assumes. Radio: An Integrated Agent Architecture for Software Defined Radio, Ph.D. The need for software-defined radios is underlined and the most important notions used for such. Mitola III, Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. This results in the set-theoretic ontology of radio knowledge defined in the. Cognitive Radio An Integrated Agent Architecture for Software.This article first briefly reviews the basic concepts about cognitive radio CR. Cognitive Radio-An Integrated Agent Architecture for Software Defined Radio. Cognitive Radio RHMZ 2007. Software-defined radio SDR idea 1. Cognitive radio: An integrated agent architecture for software.Cognitive Radio SOFTWARE DEFINED RADIO, AND ADAPTIVE WIRELESS SYSTEMS2 Cognitive Networks. 3 Joseph Mitola III, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio Stockholm.