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.

Autonomous Inter Cell Interference Avoidance under Fractional Load for Downlink Long Term Evolution

Abstract: The main source of interference in OFDMA system in downlink is inter-cell interference, which can severely limit the throughput of users near the cell edge. The inter-cell interference coordination (ICIC) is one method to improve the performance. In this paper autonomous inter-cell interference avoidance schemes under Fractional Load (FL) conditions in the downlink for 3 rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) are proposed. The proposed schemes do not require any inter-cell signaling for ICIC; rather the decision about the allocation of the spectrum in order to avoid the inter- cell interference is taken based on the information available within the cell itself. We show that the schemes for spectral resource selection is important for FL scenario to avoid high BLER. The proposed schemes further improve the SINR condition therefore higher cell throughput and coverage are realized.

QoS Considerations for HSDPA and Performance Results for Different Services

Abstract: The functional split between the centralized radio network controller (RNC) and the base station (node-B) presents new challenges and opportunities for implementing QoS control for HSDPA. In this paper we present different options for implementing QoS over HSDPA, and we focus particularly on the shared responsibility between the QoS aware algorithms at the RNC and the node-B. Different strategies for QoS aware node-B packet schedulers are discussed. The paper is concluded with network performance results for different services over HSDPA, ranging from simple best effort traffic, to constant bit rate streaming, and voice over IP. For these services we show capacity gains of up to 40% from using QoS-aware packet scheduling.

LTE Part I: Core network

Abstract: Part I of this Feature Topic covers several key aspects of the evolved packet system (EPS) architecture.

Feasibility Study of DC Offset Filtering for UTRA-FDD/WCDMA Direct-Conversion Receiver

Abstract: This paper presents a feasibility study of DC offset filtering for direct-conversion UTRA-FDD/WCDMA receivers. Spectral characteristics and properties of continuous transmission facilitate the use of simple high-pass filtering and this method is evaluated in the paper. Design equations for DC offset power to down-converted signal power ratio are presented and the impact on bit error rate of high-pass filtering WCDMA signals is evaluated by UTRA-FDD system simulations.

Achieving high UAV uplink throughput by using beamforming on board

Abstract: High-throughput unmanned aerial vehicle (UAV) communication may unleash the true potential of novel applications for aerial vehicles but also represents a threat for cellular networks due to the high levels of generated interference. In this article, we investigate how a beamforming system installed on board a UAV can be efficiently used to ensure high-throughput uplink UAV communications with minimum impact on the services provided to users on the ground. We study two potential benefits of beamforming, namely, spatial filtering of interference and load balancing, considering different beam switching methodologies. Our analysis is based on system-level simulations followed by a series of measurement campaigns in live Long-Term Evolution (LTE) networks. Our results show that using UAV-side beamforming has a great potential to increase uplink throughput of a UAV while mitigating interference. When beamforming is used, even up to twice as many UAVs may be served within a network compared with UAVs using omni-directional antennas, assuming a constant uplink throughput target. However, to fully exploit the potential of beamforming, a standardized solution ensuring alignment between network operators and UAV manufacturers is required.

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.