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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
04 Dec 2007
TL;DR: This paper studies common linear frequency direction pilot- symbol aided channel estimation algorithms for orthogonal frequency division multiplexing in a UTRA long term evolution context and shows that, in the presence of virtual subcarriers, the ML can suffer large performance degradation due to ill-conditioned matrix issues.
Abstract: This paper studies common linear frequency direction pilot- symbol aided channel estimation algorithms for orthogonal frequency division multiplexing in a UTRA long term evolution context. Three deterministic algorithms are analyzed: the maximum likelihood (ML) approach, the noise reduction algorithm (NRA) and the robust Wiener (RW) filter. A closed form mean squared error is provided for these three algorithms. Analytical and simulation results show that, in the presence of virtual subcarriers, the ML can suffer large performance degradation due to ill-conditioned matrix issues. A solution to this problem is to use the Tikhonov regularization method giving the NRA. The equivalence between the NRA and the RW is proved analytically. A practical implementation of the NRA and RW is proposed based on partial-input partial-output FFT, leading to 6 to 8 times lower complexity than the reference implementation.

5 citations

Proceedings ArticleDOI
01 Nov 2007
TL;DR: The results show that the most severe polar transmitter problem in LTE uplink is the timing delay alignment, especially for higher order modulation and coding scheme, and the EVM is a good direct measure to estimate the link level performance loss for various imperfections in the anticipated EVM range.
Abstract: The polar transmitter is an alternative RF transmitter architecture solution to achieve a high-efficiency power amplifier. In this paper the impact of polar transmitter imperfections on the UTRA long term evolution (LTE) link level performance is investigated. The most common imperfections in a polar transmitter are modelled, such as timing delay alignment and power amplifier non-idealities. Our approach is to analyze the relation between error vector magnitude (EVM) and link performance loss due to each imperfection. The results obtained are based on LTE uplink parameter settings and show that the most severe polar transmitter problem in LTE uplink is the timing delay alignment, especially for higher order modulation and coding scheme. Results also show that the EVM is a good direct measure to estimate the link level performance loss for various imperfections in the anticipated EVM range. For 16 QAM rate 3/4, an SNR loss of approximately 0.5 dB can be estimated by an EVM value of 8%.

5 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: This paper presents a novel height gain model applicable to line-of-sight urban micro cell scenarios and frequencies below 6 GHz, knife-edge diffraction-based, founded on simple geometrical and physical relationships.
Abstract: This paper presents a novel height gain model applicable to line-of-sight urban micro cell scenarios and frequencies below 6 GHz. The model is knife-edge diffraction-based, and it is founded on simple geometrical and physical relationships. Typical system level simulator scenario parameters are used as inputs to the model, where the only variable is outdoor-to-indoor penetration loss as it can vary depending on the external composition of the target building. The model is validated against two independently-obtained sets of measurements taken at different locations in China and Denmark. The model presents an average root-mean-square error accuracy of 6–7 dB, about 1–3 dB better than current existing models.

5 citations

Proceedings ArticleDOI
02 Jun 2013
TL;DR: This paper presents the first experimental activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells, and identifies the utilization of static thresholds in the decision making process, as a critical aspect for the optimization of network capacity.
Abstract: Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user- deployed base stations are expected to take place, thus making the centralized planning of frequency resources among the cells, a non-viable solution. Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) are the research paradigms which are expected to provide the network nodes the capabilities for an autonomous and efficient selection of the spectrum resources. In this paper we present the first experimental activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed. The obtained experimental results confirm the performance trends obtained from prior simulation studies. The analysis in dynamic environment conditions also allowed identifying the utilization of static thresholds in the decision making process, as a critical aspect for the optimization of network capacity.

5 citations

Proceedings ArticleDOI
01 Sep 2010
TL;DR: It is shown that the behavior of FPC when applied to femtocells is significantly different from that seen on macrocells, and it is demonstrated that ACCS is equally attractive and applicable to the uplink even though most decisions are based on UE downlink measurements.
Abstract: It has been identified in numerous contributions that dynamic interference coordination is very appealing in case of dense and uncoordinated deployments of home eNBs, also known as femtocells. One of the proposed schemes for LTE-Advanced is known as Autonomous Component Carrier Selection (ACCS). Previous contributions presented extensive downlink performance results attesting the effectiveness of ACCS. Nonetheless, the uplink has its own set of specificities, such as the use of Fractional Power Control (FPC). In this light, the purpose of this paper is two-fold: (i) Provide qualitative and quantitative answers to questions such as what is the impact of FPC on femtocells and how to best configure it. (ii) Evaluate in detail the uplink performance of ACCS under realistic power control settings. Using results derived from extensive uplink system level simulations we show that the behavior of FPC when applied to femtocells is significantly different from that seen on macrocells. In addition we demonstrate that ACCS is equally attractive and applicable to the uplink even though most decisions are based on UE downlink measurements.

5 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