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Author

Preben Mogensen

Other affiliations: Nokia, Bell Labs, Aalto University  ...read more
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.


Papers
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Proceedings ArticleDOI
06 May 2001
TL;DR: This paper presents an extensive set of measurements made on a large distributed antenna system, known to give a capacity increase due to extended channel reuse, and analyses the measurements in terms of the discrimination between measured user signals.
Abstract: This paper presents an extensive set of measurements made on a large distributed antenna system. Distributed antenna systems are known to give a capacity increase due to extended channel reuse. How far this concept can be taken is of particular interest. We present a partial answer by analysing the measurements in terms of the discrimination between measured user signals. The measurements are considered to be significant since they include all the effects of a real user scenario in the indoor office environment.

3 citations

Proceedings ArticleDOI
15 May 2000
TL;DR: In this article, a concatenated receiver structure is proposed, where an antenna array is used to form a simple spatial filter which provides protection against fast fading and spatial white interference and noise.
Abstract: A concatenated receiver structure is proposed, where an antenna array is used to form a simple spatial filter which provides protection against fast fading and spatial white interference and noise. Subsequently, a multiuser detector (MUD) is applied, which cancels the residual interference from the dominant high bit rate users. Low bit rate users are not considered in the MUD. A decision directed MUD is implemented, where a new adaptive scheme for reducing the cost of wrong interference estimates is proposed. Simulation results indicate that such a structure provides a significant gain of 69% in throughput when implemented for UMTS.

3 citations

Proceedings ArticleDOI
01 Sep 2018
TL;DR: The relationship between target failure probability and saturation rate represents the maximum achievable rate over shared resources in the interference limited regime, and is derived from Maximum Ratio Combining and Minimum Mean Square Error receivers at the base station.
Abstract: In this paper, we discuss the achievable transmission rates over collision-prone radio resources shared by a number of devices, representative of novel Internet-of-Things (IoT) scenarios We consider Maximum Ratio Combining (MRC) and Minimum Mean Square Error (MMSE) receivers at the base station, and derive the relationship between target failure probability and saturation rate, which represents the maximum achievable rate over shared resources in the interference limited regime MRC receiver is shown to be sensitive to the presence of statistically relevant interferers operating over the same resources, rapidly leading to rate saturation The MMSE receiver adds a tier of protection to collisions thanks to its interference suppression capabilities, suffering for a rate penalty only in case of a high number of users A realistic system analysis in an indoor hotspot scenario validates the analytical trends and suggests insights on practical link adaptation strategies

3 citations

Proceedings ArticleDOI
01 May 2017
TL;DR: The potential of full-duplex nodes in improving the physical-layer security of a communication link is investigated and the information-theoretic secrecy degrees of freedom measure for a pair of nodes communicating in full- duplex mode is derived.
Abstract: Full-duplex communication enables simultaneous transmission from both ends of a communication link, thereby promising gains in terms of the throughput and the delay. Such compelling gains are conditioned on a number of design constraints. Generally, it has been shown that the throughput gain and the delay reduction of full-duplex communication are conditioned on a number of assumptions. This has lead researchers to study other possible applications of full-duplex communication which can provide significantly higher gains over half-duplex communication in general. Physical-layer security is an example of such an application. The potential of full-duplex nodes in improving the physical-layer security of a communication link is investigated in this contribution. We specifically derive the information-theoretic secrecy degrees of freedom measure for a pair of nodes communicating in full-duplex mode. The secrecy degrees of freedom with full-duplex is shown to be two as opposed to that of zero in half-duplex mode. In addition, closed-form expressions for the instantaneous and ergodic throughput gain of full-duplex communication over conventional half-duplex are derived.

3 citations


Cited by
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Journal Article
TL;DR: 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.
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

13,333 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Book
01 Jan 2005

9,038 citations

Journal ArticleDOI
TL;DR: 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.
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].

5,158 citations

01 Jan 2000
TL;DR: This article briefly reviews the basic concepts about cognitive radio CR, and the need for software-defined radios is underlined and the most important notions used for such.
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.

3,814 citations