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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
24 Oct 2008
TL;DR: A novel way to help improving fairness performance in the physical layer, via fair power allocation together with resource allocation, in MU-MIMO precoding scenarios where the common approach of guaranteeing fairness at MAC layer is not feasible is presented.
Abstract: In future time division duplex (TDD)-based broadband wireless systems, it will be possible to exploit the channel reciprocity to implement channel state information (CSI)-based multi user multiple input multiple output (MU-MIMO) techniques, which will ensure highly efficient spectrum usage. To increase the cell coverage while ensuring the quality of service (QoS) for all UEs across the cell area, fairness should be maximized as much as possible. This paper presents a novel way to help improving fairness performance in the physical layer, via fair power allocation together with resource allocation, in MU-MIMO precoding scenarios where the common approach of guaranteeing fairness at MAC layer is not feasible. The results presented in this paper show that the proposed algorithm is able to reduce the system outage event to a large extent, thus increases fairness.

1 citations

Journal ArticleDOI
TL;DR: The investigation in [1] shows that in the high signal-to-interference-plus-noise (SINR) regime, geometrical programming (GP) can be used to efficiently and reliably solve the problem and a new condensation method is proposed that makes the power control practically solvable for both small- and large-scale networks.
Abstract: Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al. , 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm.

1 citations

Journal ArticleDOI
TL;DR: The empirical results indicate that early 5G deployments are already capable of reliably serving data-driven agriculture vertical use cases such as those related to agricultural logistics or configuration of machinery and diagnostics in 65.8-99% of the cases; but it will be necessary to wait for 5G network upgrades and coming 5G Releases in order to operate the more low latency demanding use cases.
Abstract: Data-driven agriculture and Internet of Farming (IoF) require reliable communication systems. Nowadays, only some of the key use cases demanded by the agricultural industry verticals get support from multiple state of the art wireless technologies such as 4G, Wi-Fi, or Low Power Wide Area Network (LPWAN) technologies, combined with satellite and cloud access. However, the ones demanding very high data rates or very low latency are still not feasible. With 5G, designed for flexible support of Extreme Mobile Broadband (xMBB), Massive Machine-Type Communications (mMTC) and Ultra-reliable Machine-Type Communications (uMTC), more agricultural use cases will be possible. This paper provides a reference list of data-driven agriculture scenarios and use cases with their associated communication requirements, and whose feasibility is evaluated in a live 5G trial performed in a representative rural area scenario in the south of Denmark. The paper details a reference methodology for assessing 5G Quality of Service (QoS), including multi-connectivity schemes and reports the empirical 5G performance results, which are put in perspective of the requirements for the different IoF reference scenarios. The empirical results indicate that early 5G deployments are already capable of reliably serving data-driven agriculture vertical use cases such as those related to agricultural logistics or configuration of machinery and diagnostics in 65.8-99% of the cases; but it will be necessary to wait for 5G network upgrades and coming 5G Releases in order to operate the more low latency demanding use cases.

1 citations

Proceedings ArticleDOI
01 Sep 2019
TL;DR: The results show that the downlink-based beam sweep leads to higher Signal to Interference and Noise Ratio (SINR) than beamforming based on the estimated AoA, and the feasibility of signal tracking techniques exploiting the location of the vehicle and the BS are investigated to alleviate the need for continuous direction acquisition.
Abstract: This work evaluates the concept of uplink beamforming for vehicular communications in the sub-6 GHz frequency bands to improve throughput, latency and coverage of the vehicle to Base Station (BS) link. The data recorded in the experimental measurements using live cellular signals are used to study the performance of two direction acquisition methods: the Angle of Arrival (AoA) estimation and downlink-based beam sweep. Next, the feasibility of signal tracking techniques exploiting the location of the vehicle and the BS are investigated to alleviate the need for continuous direction acquisition. The results show that the downlink-based beam sweep leads to higher Signal to Interference and Noise Ratio (SINR) than beamforming based on the estimated AoA. Evaluated tracking techniques are shown to be capable of correctly estimating the beamforming angle for distances in order of hundreds of meters when BS's location is known to the vehicle.

1 citations

Proceedings ArticleDOI
18 May 2014
TL;DR: This paper adopts an experimental procedure for acquiring almost 1000 different radio link conditions between the nodes of a relevant wireless indoor network scenario and used them as input to a system level simulator in order to evaluate the performance of a local area decentralized ICIC scheme.
Abstract: The characteristics of the deployment scenario are fundamental elements in the performance evaluation of wireless networks inter-cell interference coordination (ICIC) schemes. The statistical validation of such concepts is typically achieved by means of system-level simulation campaigns where regular reference scenarios and stochastic channel models are employed. It is an important next step to verify that the trends observed in the reference scenarios compare equally well in more practical deployments. For such comparison, it is required to evaluate an extensive set of link conditions, reflecting the many possible configurations that can be experienced in a practical scenario. In this paper we adopt an experimental procedure, using a software defined radio testbed, for acquiring almost 1000 different radio link conditions between the nodes of a relevant wireless indoor network scenario. The acquired measurements, were used as input to a system level simulator in order to evaluate the performance of a local area decentralized ICIC scheme. The obtained performance results highlight the contribution of the selected scheme and provide a new insight for the validation of the related simulation-based studies, previously published in literature.

1 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