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.

Deployment Strategies for the Industrial IoT: A Case Study Based on Surface Mines

Abstract: The mining industry is on a transition towards unmanned operations. This implies a step change in wireless infrastructure expansion to support autonomous and teleoperated machinery. This paper investigates how the topographic changes over the course of 10 years of continuous mining affect the propagation conditions, and impacts the performance associated with different deployment strategies for wireless networks in a large open-pit mining complex in Brazil. Through a series of system-level simulations, using detailed terrain models, realistic traffic volumes and a dedicated propagation model, we compare the ability of different deployment strategies, and network features, to meet given performance targets with existing technology. The results show that heterogeneous deployments can be exploited to continuously guarantee coverage in this ever- changing topography, while interference mitigation techniques, such as enhanced inter-cell interference coordination (eICIC) and beamforming, can be used to reduce the system outage without need to increase the spectrum.

Impact of the Pilot Signal per Beam on the Ideal Number of Beams and Capacity Gain of Switched Beam Forming for WCDMA

Abstract: This study devises a simple mathematical model that predicts the number of beams that maximizes system capacity for different WCDMA network configurations. The ideal number of beams to be synthesized within a cell by an antenna array (AA) with a fixed number of elements at the base station (BS) is primarily influenced by the average antenna gain and the relative power allocated to the secondary common pilot channel (S-CPICH) transmitted on each directional beam. It is shown that for a fixed maximum Node-B transmit power the overall system capacity gain in the downlink (DL) provided by switched beam forming (SBF) decreases with the cell size, that is, as more power is needed by the pilot channels to assure coverage. Predictions from our model are compared with simulation results obtained from a detailed dynamic system level simulator in order to be validated. A remarkably good match is observed.

Reversing the Energy Trend in Mobile Networks: Equipment Replacement for Increased Capacity at a Fraction of the Energy

Abstract: In order to meet the expected boost in mobile data traffic, mobile network operators are planning and upgrading the capacity of their networks. Through a previous study it has been shown that over a period of eight years, different network upgrade strategies have a different impact on the energy consumption and cost of the network. However, irrespective of the upgrade strategy, all lead to an overall increase in the energy consumption of the network. This is based on the assumption that all sites are equipped with the same version of the equipment. In reality, it is likely to find a variety of equipment generations at different base station sites. This paper extends the previous study by considering a realistic equipment replacement strategy. In addition to considering three equipment generations, a number of sites are also upgraded to remote radio head, which reduces the energy consumption even further. Results show, that over the evolution period, it is in fact possible to boost capacity while maintaining or even reducing the energy consumption of the network. For the macro-only upgrade case, a reduction of 9% is experienced between the first and the last year. For the joint macro-pico case, a reduction in energy consumption of 41% is noted. Such reductions are well in line with what mobile network operators are aiming at achieving over the next years.

A centralized inter-cell rank coordination mechanism for 5G systems

Abstract: Multiple transmit and receive antennas can be used to increase the number of independent streams between a transmitter-receiver pair, or to improve the interference resilience property with the help of linear minimum mean squared error (MMSE) receivers. An interference aware inter-cell rank coordination framework for the future fifth generation wireless system is proposed in this article. The proposal utilizes results from random matrix theory to estimate the mean signal-to-interference-plus-noise ratio at the MMSE receiver. In addition, a game-theoretic interference pricing measure is introduced as an inter-cell interference management mechanism to balance the spatial multiplexing vs. interference resilience trade-off. Exhaustive Monte Carlo simulations results demonstrating the performance of the proposed algorithm indicate a gain of around 40% over conventional non interference-aware schemes; and within around 6% of the optimum performance obtained using a brute-force exhaustive search algorithm.

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.