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.

Light Cognitive Radio Enabled Flexible Spectrum Usage in Local Area Deployment

Abstract: In this paper we propose a mechanism to enable flexible spectrum usage (FSU) in local area indoor deployment scenario with several operators in the given geographical area. The proposed scheme is referred to as policy assisted light cognitive radio (CR) enabled FSU, because it follows the CR cycle and considers policy as an important element to assist FSU. It facilitates allocation of spectral resources from a common pool in flexible manner and ensures coexistence of several operators in the given geographical area on the shared spectrum. It also provides an autonomous, self adjustable and scalable solution for the emerging large scale Local Area (LA) indoor deployment of Home eNode-Bs (HeNBs).

5G in Open-Pit Mines: Considerations on Large-Scale Propagation in Sub-6 GHz Bands

Abstract: 5G will play a pivotal role in the digitization of the industrial sector and is expected to make the best use of every bit of spectrum available. In this light, this paper presents the results of an extensive measurement campaign in two iron-ore open-pit mining complexes, at the 700 MHz and 2.6 GHz bands, considering macro and small cell deployments. The study is further motivated by the rise of unmanned machinery in the mining industry. We present values of path loss exponents, shadow fading standard deviations, autocorrelation distances and inter-frequency cross-correlation, which are all useful for the future wireless network design, simulation and performance evaluation. The results show that, in order to comply with ultra-reliable communications (URC) availability requirements, larger shadowing margins will have to be considered in the network planning in open-pit mines, when compared to traditional industrial environments. Furthermore, large cross-correlation between the shadowing in both frequency bands limits the gains when using multi-connectivity in order to enhance overall network availability.

Effect of Phase Noise on Spectral Efficiency for Utra Long Term Evolution

Abstract: In this paper, the effects of phase noise on the spectral efficiency of the next generation of OFDM based mobile systems with channel estimation is investigated. The simulation context and parameter settings are taken from the 3GPP Evolved UTRA (E-UTRA) study item, focusing on an OFDM downlink single antenna system in 20 MHz bandwidth. Phase noise is modeled as a Wiener-Levy process and several phase noise powers are evaluated. The OFDM coherent detection method is based on Pilot Assisted Channel Estimation (PACE) with Wiener based frequency domain interpolation and second order gaussian interpolation for the time domain interpolation. The cell level spectral efficiency is also evaluated for micro and macro-cell scenarios. The simulation results indicate that the phase noise effect in E-UTRA downlink can be reduced by using high performance local oscillator or by placing pilots in every OFDM symbols.

Impact of Transport Control Protocol on Full Duplex Performance in 5G Networks

Abstract: Full duplex (FD) communication has attracted the attention of the industry and the academia as an important feature in the design of the future 5th generation (5G) wireless communication system. Such technology allows a device to simultaneously transmit and receive in the same frequency band, with the potential of providing higher throughput and lower latency compared to traditional half duplex (HD) systems. In this paper, the interaction between Transport Control Protocol (TCP) and FD in 5G ultra- dense small cell networks is studied. TCP is a well- known transport layer protocol for providing reliability, which comes at the price of increased delay and reduced system throughput. FD is expected to accelerate the TCP congestion control mechanism and hence mitigate such consequences. System level results show that FD can outperform HD and alleviate the TCP drawbacks when the inter-cell interference is not the main limiting factor. On the other hand, under strong inter-cell interference, results show that the capabilities of the system to cope with such interference dictates the gain that FD may provide over HD.

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.