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.

Qos differentiation for wcdma services mapped onto an e-dch channel

Abstract: A SYSTEM HAS USER EQUIPMENT (1; 2) EACH HAVING BUFFERS (3, 4, 5, 6; 7, 8, 9, 10) FOR STORING PACKETS CLASSIFIED ACCORDING TO PRIORITY AND READY FOR TRANSMISSION OVER A RADIO INTERFACE TO A NETWORK ELEMENT (12) IN SUCH A SYSTEM, USER EQUIPMENT SENDS CAPACITY REQUEST SIGNALS (11; 13) INDICATIVE OF THE VARIOUS PRIORITIES OF THE BUFFERED PACKETS ON THE RADIO INTERFACE TO THE NETWORK ELEMENT RATHER THAN SEEKING A CAPACITY ALLOCATION DECISION FROM A HIGHER LEVEL IN THE CORE NETWORK, THE NETWORK ELEMENT ITSELF MAKES THE CAPACITY ALLOCATION DECISION AND PROVIDES A CAPACITY ALLOCATION SIGNAL (14) TO THE USER EQUIPMENT PACKETS THAT HAVE THUS BEEN ALLOCATED CAPACITY BY THE NETWORK ELEMENT ARE THEN SENT FROM THE USER EQUIPMENT TO THE NETWORK ELEMENT HAVING THE NETWORK ELEMENT MAKE THE CAPACITY ALLOCATION DECISIONS ITSELF is A MORE EFFICIENT SYSTEM SINCE THE NETWORK ELEMENT IS IN DIRECT COMMUNICATION WITH THE USER EQUIPMENT OVER THE RADIO INTERFACE AND THE DECISION IS MADE IN AN UNMEDIATED WAY

Performance of uplink packet services in WCDMA

Abstract: In this paper, we study the operation of release'99 packet scheduler of the UMTS system. We focus on the uplink direction of data transmission. The aim is to investigate the performance of the packet scheduler data scheduling on dedicated transport channels. We emphasize in particular a power based packet scheduler (PS) with fair resource sharing. The PS performance has been evaluated in a time-dynamic network simulator with packet data users characterized by a bursty source traffic model. We shortly describe the source traffic model and the network simulator. The results concentrate on the uplink noise rise (NR) behavior for different settings of PS related timers and show that a reduction of some of these timers to allow faster scheduling can increase the network capacity. However, the results also demonstrate that this potential capacity gain is achievable only for bursty and high user equipment (UE) source data rate.

Interference Aware Inter-Cell Rank Coordination for 5G Systems

Abstract: Multiple transmit and receive antennas can be used to increase the number of independent streams between a transmitter–receiver pair, and/or to improve the interference resilience with the help of linear minimum mean squared error (MMSE) receivers. Typically, rank adaptation algorithms aim at balancing the tradeoff between increasing the spatial multiplexing gain through independent streams, and improving the interference resilience property. An interference aware inter-cell rank coordination framework for the future fifth generation wireless system is proposed in this paper. 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 versus interference resilience tradeoff. Centralized and distributed implementations of the proposed inter-cell rank coordination framework are presented, followed by exhaustive Monte Carlo simulation results demonstrating its performance. The obtained results indicate that the performance of the proposed method is up to 56% better than conventional non interference-aware schemes; and within 6% of the optimum performance obtained using a brute-force exhaustive search algorithm though it incurs much lower computational complexity.

Analysis and comparison of 24 GHz cmWave radio propagation in urban and suburban scenarios

Abstract: This paper presents a measurement-based comparison of cm-wave propagation in urban and suburban scenarios at 24 GHz with transmitter antennas located above rooftop level. Different sets of directional measurements, exploring the full azimuth and the range from −30 to +30 degrees in elevation, were performed with horn antennas located close to street level, in order to explore the spatial characteristics of the channel in both LOS and NLOS conditions. The statistical analysis of different directional indicators shows how, at 24 GHz, outdoor propagation is quite different in the suburban scenario as compared to the urban case. Increased spatial multipath, in average 1.23 times higher, is observed in the suburban scenario, mainly due to the strong presence of vegetation. This results in reduced suburban NLOS path loss exponents (3.4) in comparison to the urban scenario (3.7), as detailed in the outdoor path loss analysis. The paper also highlights the potential of using beam combining techniques in order to improve cell-edge coverage by 17% and 37% in the urban and suburban scenarios, respectively. Outdoor-to-indoor propagation was also investigated, finding an average penetration loss of 6.5 dB for buildings composed of light construction materials. The different results and observations provided in the paper are useful for modeling and simulation of future wireless networks operating at 24 GHz in urban and suburban scenarios.

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.