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.

Experimental investigation of correlation properties of MIMO radio channels for indoor picocell scenarios

Abstract: The present paper describes the set-up for the measurement of MIMO (multi-input-multi-output) radio channels as part of the European project METRA (Multi Element Transmit and Receive Antenna). Inputs for the stochastic model described by Pedersen, Andersen, Kermoal and Mogensen (see IEEE Vehicular Technology Conference VTC 2000 Fall, Boston, USA, 2000) are extracted from the measurement results and fed into a COSSAP(R) block implementing this model. A good matching between the eigenanalysis performed on both measured and simulated signals is shown.

A flexible 5G frame structure design for frequency-division duplex cases

Abstract: A 5G frame structure designed for efficient support of users with highly diverse service requirements is proposed. It includes support for mobile broadband data, mission-critical communication, and massive machine communication. The solution encompasses flexible multiplexing of users on a shared channel with dynamic adjustment of the transmission time interval in coherence with the service requirements per link. This allows optimizing the fundamental tradeoffs between spectral efficiency, latency, and reliability for each link and service flow. The frame structure is based on in-resource physical layer control signaling that follows the corresponding data transmission for each individual user. Comparison against the corresponding LTE design choices shows attractive benefits.

Coverage Comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7800 km² Area

Abstract: In this simulation work the coverage of GPRS, Narrowband-IoT, LoRa, and SigFox is compared in a realistic scenario, covering 7800 km2 and using Telenor's commercial 2G, 3G, and 4G deployment. The target is to evaluate which of the four technologies provides the best coverage for Internet of Things devices, which may be located deep indoor. The results show that Narrowband-IoT, having the best Maximum Coupling Loss performance of 164 dB, also provides the best coverage. This is despite the fact that LoRa and SigFox deployments with omnidirectional antennas are found to provide 3 dB lower link loss on average. In the deployment 11 % of the geographical area contains devices, located both in rural and urban areas. The NB-IoT has an outage below 1 % for locations experiencing 20 dB indoor penetration loss in addition to the outdoor path loss. SigFox performs similarly, while LoRa cannot provide coverage for 2 % of those locations. For the challenging deep indoor case, where 30 dB additional penetration loss is expected, NB-IoT has 8 % outage while SigFox and LoRa is unable to cover 13 % and 20 % of the locations. The four technologies may not be deployed at all existing site locations and therefore the work also includes a study of the coverage as a function of the minimum Inter-Site Distance, where sites closer than 2, 4, and 6 km are filtered out. The results show that SigFox and NB-IoT have outage probabilities below 5 % even though sites closer than 4 km are removed from the simulations.

Interference Measurements in the European 868 MHz ISM Band with Focus on LoRa and SigFox

Abstract: In this measurement study the signal activity and power levels are measured in the European Industrial, Scientific, and Medical band 863-870 MHz in the city of Aalborg, Denmark. The target is to determine if there is any interference, which may impact deployment of Internet of Things devices. The focus is on the Low Power Wide Area technologies LoRa and SigFox. The measurements show that there is a 22-33 % probability of interfering signals above -105 dBm within the mandatory LoRa and SigFox 868.0-868.6 MHz band in a shopping area and a business park in downtown Aalborg, which thus limits the potential coverage and capacity of LoRa and SigFox. However, the probability of interference is less than 3 % in the three other measurement locations in Aalborg. Finally, a hospital and an industrial area are shown to experience high activity in the RFID subband 865-868 MHz, while the wireless audio band 863-865 MHz has less activity.

5G small cell optimized radio design

Abstract: The 5th generation (5G) of mobile radio access technologies is expected to become available for commercial launch around 2020. In this paper, we present our envisioned 5G system design optimized for small cell deployment taking a clean slate approach, i.e. removing most compatibility constraints with the previous generations of mobile radio access technologies. This paper mainly covers the physical layer aspects of the 5G concept design.

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.