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.

LTE UE Power Consumption Model: For System Level Energy and Performance Optimization

Abstract: In this work a novel LTE user equipment (UE) power consumption model is presented. It was developed for LTE system level optimization, because it is important to understand how network settings like scheduling of resources and transmit power control affect the UE's battery life. The proposed model is based on a review of the major power consuming parts in an LTE UE radio modem. The model includes functions of UL and DL power and data rate. Measurements on a commercial LTE USB dongle were used to assign realistic power consumption values to each model parameter. Verification measurements on the dongle show that the model results in an average error of 2.6 %. The measurements show that UL transmit power and DL data rate determines the overall power consumption, while UL data rate and DL receive power have smaller impact.

Frequency Domain Scheduling for OFDMA with Limited and Noisy Channel Feedback

Abstract: In this study we analyze the downlink OFDMA system level performance for three different channel quality indicator (CQI) reporting schemes. The effect of terminal measurement and estimation errors, quantization from formatting and compression, and uplink reporting delays and detection errors are included. We find that a simple threshold-based CQI scheme provides an attractive trade-off between downlink system level performance and uplink CQI signaling overhead, as compared to using a best-M scheme. When applied to the UTRAN LTE system in a 10 MHz bandwidth, we find that a frequency domain packet scheduling gain of 40% is achievable with a CQI word size of only 30-bits. Finally, the effect of applying a so-called outer loop link adaptation algorithm is reported.

High Speed Downlink Packet Access: WCDMA Evolution

Abstract: This article gives an overview of the high speed downlink packet access (HSDPA) concept; a new feature which is coming to the Release 5 specifications of the 3GPP WCDMA/UTRA-FDD standard. To support an evolution towards more sophisticated network and multimedia services, the main target of HSDPA is to increase user peak data rates, quality of service, and to generally improve spectral efficiency for downlink asymmetrical and bursty packet data services. This is accomplished by introducing a fast and complex channel control mechanism based on a short and fixed packet transmission time interval (TTI), adaptive modulation and coding (AMC), and fast physical layer (L1) hybrid ARQ. To facilitate fast scheduling with a per-TTI resolution in coherence with the instantaneous air interface load, the HSDPA-related MAC functionality is moved to the Node-B. The HSDPA concept facilitates peak data rates exceeding 2 Mbps (theoretically up to and exceeding 10 Mbps), and the cell throughput gain over previous UTRA-FDD releases has been evaluated to be in the order of 50-100% or even more, highly dependent on factors such as the radio environment and the service provision strategy of the network operator.

Performance aspects of WCDMA systems with high speed downlink packet access (HSDPA)

Abstract: In this paper, we consider the link and network layer performance aspects of a WCDMA/UTRA system with high speed downlink packet access. The study considers packet scheduling and the tradeoff among user fairness and cell throughput. We show performance numbers for different network setups and study the applicability of proportional scheduling methods. Even with conservative system and traffic settings, the best effort methods produce high user data rates and cell throughput.

An overview of downlink radio resource management for UTRAN long-term evolution

Abstract: Radio resource management algorithms ranging from bearer admission control to semi-persistent and dynamic packet scheduling, fast link adaptation, and transmission control of multi-antenna transmission modes are addressed in this article for UTRAN long-term evolution. First, a high-level system overview of LTE is given, with special emphasis on the important components related to RRM. The quality of service parameter framework is outlined, as one of the main objectives for the families of RRM algorithms is to maximize system capacity while serving all users according to their minimum QoS constraints. It is demonstrated how the collocation of the RRM algorithms at the base station with easy access to air interface measurements offers opportunities for efficient cross-functional optimization between layers 1, 2, and 3. Examples of performance results for different traffic mixes and antenna transmission schemes are also presented, and the article is concluded with recommendations on how to operate the various RRM options under different load and traffic conditions.

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.