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.

A preamble based diversity scheme for DECT using a new signal quality measure

Abstract: PCS and RLL (radio in the local loop) are applications which set new demands on the DECT receiver, due to the fact that the RMS delay spread often exceeds 200 ns for outdoor environments. For comparison a typical low-cost DECT receiver based on a limiter-discriminator detector is not able to cope with more than 100 ns delay spread. A well-known solution which increases the receiver resistance to time dispersion is antenna diversity. The optimum realizable selection diversity scheme is based on the status of the R-CRC (cyclic redundancy check) check and it requires two parallel receiver chains. The simpler switching diversity scheme, normally used in commercial equipment, requires only one receiver chain, but it suffers from a strong velocity dependency. In this paper a diversity scheme using a new signal quality measure is presented. The signal quality is extracted successively in the two antenna branches during the DECT preamble which makes the scheme velocity independent as no significant delay is introduced. With the new quality measure it is possible to closely emulate genuine R-CRC selection diversity by use of only one receiver chain. For an E/sub b//N/sub 0/ of 25 dB, the maximum allowable RMS delay spread is increased from 180 ns for RSSI driven diversity to 220 ns with the new scheme. This is only 10 ns less than R-CRC selection diversity. For realistic hardware conditions, the performance is only degraded 10 ns which makes the new diversity scheme applicable for a practical implementation.

Experimental Investigation of Capacity and Quality Enhancement of GSM Using Adaptive Beamforming

Abstract: At Aalborg University there is an ongoing project on the application of base station antenna arrays for increased spectrum eeciency and enhanced quality in GSM and UMTS. The project is part of the EU sponsored program TSUNAMI(II) on the same topic. Aalborg has been given the task of developing the algorithms for beam steering. These algorithms will be used in a eld-trial using Motorola base stations. The trials will be conducted by the English DCS1800 operator Orange on their test facility which includes several base stations. In order to evaluate the algorithms under realistic but controlled conditions Aalborg University is building their own nine channel DCS1800 stand-alone testbed (which will be used for other purposes as well). This paper will describe the stand-alone testbed, the proposed algorithms and some results.

Method and a controller for controlling a connection

Abstract: A method of controlling a connection comprising a first link and a second link is disclosed. The method comprises determining if one of the links is limiting capacity the connection and if so, changing one or more parameters relating to one of the links to change the capacity of the one of the links.

A Non-Stationary 6G V2V Channel Model With Continuously Arbitrary Trajectory

Abstract: In this article, a novel three-dimensional (3D) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed for sixth-generation (6G) vehicle-to-vehicle (V2V) channels. In the proposed GBSM, clusters in the environment are divided into static clusters and dynamic clusters. Furthermore, the time-variant acceleration together with the integration of time during the transmission distance update are exploited. As a result, the continuously arbitrary trajectory of the transceiver and dynamic clusters is successfully captured. To jointly model space-time-frequency (S-T-F) non-stationarity of 6G V2V channels, a new method, which properly integrates the frequency-dependent factor, birth-death (BD) process, and selective evolution of static and dynamic clusters, is developed. Key channel statistics, including the space-time-frequency correlation function (STF-CF), time stationary interval, and Doppler power spectral density (DPSD) are obtained. Simulation results demonstrate that S-T-F non-stationarity is modeled and the impacts of vehicular traffic density (VTD) and vehicular movement trajectory (VMT) on channel statistics are further analyzed thoroughly. Finally, the generality and accuracy of the proposed GBSM are validated through the comparison of simulation results and available measurement data.

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.