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.

Mobile Broadband Traffic Forecast Modeling for Network Evolution Studies

Abstract: Market analyst research studies predict significant mobile data traffic increase over the next 5 to 10 years. The mobile network operators have to be able to meet these capacity demands by upgrading and optimizing their existing networks in parallel with the deployment of new radio access technologies. This paper proposes a mathematical modeling framework for the mobile broadband traffic growth to be employed in mobile network evolution studies. We show that an "S-curve" model based on the Gompertz function can be appropriately parameterized and provides sufficient flexibility for predicting various traffic growth scenarios. We exemplify the use of the proposed traffic modeling approach with a network evolution case study in the generic setting of a dense urban European network deployment scenario in combination with assumptions on the target network key performance indicator and user equipment receiver capabilities.

Capacity of a GSM network with fractional loading and random frequency hopping

Abstract: The need for more capacity in GSM networks is increasing. Using random frequency hopping and fractional loading is a potential way to obtain more capacity. In this paper the optimal reuse scheme for a GSM system with random frequency hopping is presented along with some methods to increase the capacity and to improve the link quality, like using DTX and fractional loading. The 1/3 reuse scheme appears to be best if the capacity is determined by looking at the distribution of signal to interference (CIR) values, while the 3/9 reuse scheme seems best if the focus is at the percentage of dropped calls (with the used dropped call, power control and handover algorithm). The 1/3 reuse scheme has the advantage over the 3/9 reuse scheme that it is able to profit from fractional loading, which gives better quality to the individual user.

Sleep Modes for Enhanced Battery Life of 5G Mobile Terminals

Abstract: In addition to higher data rates and lower latency the 5G Radio Access Technology concepts are targeting to provide better battery life for mobile broadband and Machine Type Communication users. In this overview paper we analyze how microsleep, Discontinuous Reception and Transmission, and a wake-up receiver concept can be combined to enhance the battery life of 5G mobile terminals. Due to the short and pipelined 5G frame structure microsleep provides 20 % energy savings as compared to LTE. The Discontinuous Reception and Transmission modes also benefit from the new frame structure leading to faster connection setup and up to 80 % lower energy consumption depending on the traffic type. Finally we estimate that the wake-up receiver concept, when adapted to scheduled and cellular communication, can provide 90 % lower energy consumption and ensure a predictable and consistent latency.

Baseline E-UTRA Downlink Spectral Efficiency Evaluation

Abstract: This paper evaluates spectral efficiency performance of the 3GPP Evolved UTRA (E-UTRA) downlink with baseline settings. A detailed link level tool has been developed including the majority of 3GPP E-UTRA link chain processing modules such as time-domain link adaptation and LI HARQ. Since MIMO is an integral part of the new system, we include four basic multiple antenna configurations in the analysis, namely the SISO, 1x2 SIMO, 2x2 SFC and 2x2 BLAST schemes. Since the BLAST is very sensitive to channel estimation error in cell edge, a case denoted as adapMIMO is considered as well, where the SFC is taken as a backup for the BLAST in cell edge. The system bandwidth is fixed at 20 MHz and a Typical Urban channel model is assumed for both macrocell and microcell evaluations. Simulation results show that in macrocell scenario, the spectral efficiency performance gain of SIMO over SISO is up to 54%, while SFC shows an additional gain of 14% over SIMO. Although the adapMIMO scheme has similar performance as SFC in the macrocellular case, it produces about 30% spectral efficiency gain in the microcell scenario.

Multi-cell admission control for UMTS

Abstract: Power-based multi-cell admission control is assessed as a solution to avoid unstable situations and to increase the system capacity in WCDMA networks, which are intended to provide high-speed data services. This paper proposes and analyses an uplink multi-cell admission control algorithm, which takes the interference level in the adjacent cells as well as in the serving one into account. Power increase estimation is crucial when performing power-based admission control tasks. With the aim of making the realisation of the multi-cell admission control algorithm possible, a method is derived to estimate the received power increase which a new mobile station generates in the adjacent base stations. Simulation results reveal that under non-homogeneous conditions the new algorithm provides a 34% capacity gain for a 5% of dropping connection probability.

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.