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.

Simulation of dual-polarized propagation environments for adaptive antennas

Abstract: A simple method for simulating a propagation environment with temporal, azimuthal, and polarization dispersion is proposed. The channel is simply modeled by a time-variant number of discrete paths with different amplitude, delay, and azimuth. Each path is controlled by a birth death process in order to make the channel as dynamic as possible. All model parameters are extracted from experimental data. Simulation results are presented in order to illustrate how the model behaves.

Modeling of Wi-Fi IEEE 802.11ac Offloading Performance for 1000x Capacity Expansion of LTE-Advanced

Abstract: This paper studies indoor Wi-Fi IEEE 802.11ac deployment as a capacity expansion solution of LTE-A (Long Term Evolution-Advanced) network to achieve 1000 times higher capacity. Besides increasing the traffic volume by a factor of x1000, we also increase the minimum target user data rate to 10Mbit/s. The objective is to understand the performance and offloading capability of Wi-Fi 802.11ac at 5GHz. For the performance evaluation of Wi-Fi, we propose a novel analytical throughput model that captures both key 802.11ac enhancements and multi-cell interference. We provide a quantitative evaluation of large-scale indoor Wi-Fi 802.11ac deployment in a real urban scenario by extensive simulations. We conclude that deploying indoor Wi-Fi access points in almost every building is essential to carry the x1000 traffic volume and ensure a minimum user data rate of 10Mbit/s.

Modelling frequency correlation of fast fading in frequency hopping GSM link simulations

Abstract: The spaced frequency correlation affects the frequency diversity gain in frequency hopping (FH) GSM systems. However, GSM link level simulations have not taken this into account. In this paper, a method for generation of correlated multipath fading is described, taking the spaced frequency correlation into account. The method can be used to emulate frequency hopping in a wideband channel, and is usable with both network and link level simulations. FH-GSM link simulations with low-speed mobiles exhibit a loss in frequency diversity gain due to the spaced frequency correlation. Two factors influence this loss: the frequency reuse factor and the channel coherence bandwidth. In networks with frequency reuse 1, the loss in diversity gain is up to 0.5 dB in outdoor urban environments and up to 4 dB in indoor environments.

A Distributed Taxation Based Rank Adaptation Scheme for 5G Small Cells

Abstract: The further densification of small cells impose high and undesirable levels of inter-cell interference. Multiple Input Multiple Output (MIMO) systems along with advanced receiver techniques provide us with extra degrees of freedom to combat such a problem. With such tools, rank adaptation algorithms allow us to use our antenna resources to either exploit multiple spatial streams in low interference scenarios, or suppress interference in high interference scenarios. In this paper, we propose and evaluate an interference-aware distributed rank adaptation algorithm. The concept behind our approach is to discourage the choice of transmitting with multiple spatial streams in highly interfered scenarios, and to exploit and encourage the usage of multiple spatial transmission streams in low interference scenarios. We show that our proposed algorithm can be adjusted to preserve and guarantee a good outage performance, while providing the benefit of higher average throughputs, in both low and highly interfered scenarios, when compared to fixed rank configurations, and distributed selfish schemes.

A Link-Level MIMO Radio Channel Simulator for Evaluation of Combined Transmit/Receive Diversity Concepts within the METRA Project

Abstract: This document presents a simple framework for Monte-Carlo simulations of a multipleinput-multiple-output (MIMO) radio channel. A stochastic model including the partial correlation between the paths in the MIMO channel, as well as fast fading and time dispersion is proposed. Its implementation in a COSSAP primitive model is described. Simulations verify the features of this primitive model. However, the stochastic model still needs to be validated by comparison with results of measurement campaigns currently in progress [1,2]. This COSSAP block will later help to investigate combined transmit/receive diversity concepts as part of the European IST (Information Society Technologies) METRA (Multi Element Transmit and Receive Antennas) project [3].

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.