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 published on a yearly basis
Papers
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27 Sep 1995TL;DR: Simulation results show that optimum diversity combining is only useful for a TDD system with a short dwell time, and in the uplink direction M-1 interferers can be suppressed by an M element array.
Abstract: Optimum diversity combining in a wireless communications system using TDMA/TDD is considered. By the use of optimum diversity combining a receiving antenna array is able to suppress interference and thus the capacity of the system is increased. The results in this paper are obtained by simulations. In the simulations a DECT-like system is used as the TDMA/TDD system. The results show that in the uplink direction M-1 interferers can be suppressed by an M element array. However the downlink transmit diversity performance is much worse, due to the changes in the radio channel during the dwell time between the uplink and downlink burst. That is the reason why it is concluded that optimum diversity combining is only useful for a TDD system with a short dwell time.
8 citations
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01 Sep 2015TL;DR: FD communication is studied within the framework of 5th generation (5G) small cell systems in order to address its effective gain in such specific scenarios, and its performance is evaluated against a traditional HD communication.
Abstract: Full duplex (FD) communication promise of doubling the throughput of half duplex (HD) communication makes such type of system an attractive solution to cope with the expected mobile data traffic increase. Nevertheless, simultaneous transmission and reception in dense deployment scenarios increases the inter- cell interference compared to a traditional HD communication, due to a larger number of nodes simultaneously transmitting. Moreover, FD communication can only be exploited when there is traffic in both uplink and downlink directions simultaneously. In this paper, FD communication is studied within the framework of 5th generation (5G) small cell systems in order to address its effective gain in such specific scenarios. The factors that affect FD performance are analysed, and its performance is evaluated against a traditional HD communication. System level simulations show that the gain of FD over HD in the considered scenarios is lower than the expected 100% gain, with a strong dependency on the traffic and the interference conditions.
8 citations
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08 Jun 2015TL;DR: The proposed interference aware scheme only requires instantaneous channel state information at the transmitter end towards the desired receiver and can outperform a more complex benchmark precoding scheme in terms of the sum network throughput in certain scenarios and with realistic channel estimation errors, while delivering close to the benchmark performance under general conditions.
Abstract: An ultra-dense deployment of small cells with multi-antenna nodes is expected to be the solution for coping with the huge traffic growth expected in near future. Mutual interference among coexisting users is one of the main performance bottlenecks in such dense deployment scenarios. A distributed transmission technique that can efficiently manage the interference in an uncoordinated dense small cell network is investigated in this work. The proposed interference aware scheme only requires instantaneous channel state information at the transmitter end towards the desired receiver. Motivated by penalty methods in optimisation studies, an interference dependent weighting factor is introduced to control the number of parallel transmission streams. The proposed scheme can outperform a more complex benchmark precoding scheme in terms of the sum network throughput in certain scenarios and with realistic channel estimation errors, while delivering close to the benchmark performance under general conditions.
7 citations
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30 Aug 2002TL;DR: In this paper, the authors proposed a method and a device for controlling power in a network transmitted from a first station to a second station, where the second station determines a power target value for a signal received from the first station and sends power control commands to the first stations depending on a deviation between said power target values and a received power level.
Abstract: The present invention relates to a method and a device for controlling power in a network transmitted from a first station to a second station. The second station determines a power target value for a signal received from the first station and sends power control commands to the first station depending on a deviation between said power target value and a received power level. The second station detects faulty data blocks received from the first station and requests retransmission of faulty data blocks from the first station. The adjustment of the power target value to a temporary power target value during the retransmission is performed such that the temporary power target value is calculated depending on the quality of a faulty data block.
7 citations
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05 Sep 2013TL;DR: It is shown that a more detailed semi-deterministic model leads to both lower gains in terms of SINR, outage probability and downlink throughput and lower optimum tilt settings, and that care must be taken when using the 3GPP model to evaluate advanced adaptive antenna techniques, especially those operating in the elevation dimension.
Abstract: Despite being a simple and commonly-applied radio optimization technique, the impact on practical network performance from base station antenna downtilt is not well understood. Most published studies based on empirical path loss models report tilt angles and performance gains that are far higher than practical experience suggests. We motivate in this paper, based on a practical LTE scenario, that the discrepancy partly lies in the path loss model, and shows that a more detailed semi-deterministic model leads to both lower gains in terms of SINR, outage probability and downlink throughput and lower optimum tilt settings. Furthermore, we show that a simple geometrically based tilt optimization algorithm can outperform other tilt profiles, including the setting applied by the cellular operator in the specific case. In general, the network performance is not highly sensitive to the tilt settings, including the use of electrical and/or mechanical antenna downtilt, and therefore it is possible to find multiple optimum tilt profiles in a practical case. A broader implication of this study is that care must be taken when using the 3GPP model to evaluate advanced adaptive antenna techniques, especially those operating in the elevation dimension.
7 citations
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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
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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
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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