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.

Turbo Receivers for Single User MIMO LTE-A Uplink

Abstract: The paper deals with turbo detection techniques for Single User Multiple-Input-Multiple-Output (SU MIMO) antenna schemes. The context is on the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems. Iterative approaches based on Parallel Interference Cancellation (PIC) and Successive Interference Cancellation (SIC) are investigated, and a low-complexity solution allowing to combine interstream interference cancellation and noise enhancement reduction is proposed. Performance is evaluated for Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency Division Multiplexing (SC-FDM) as candidate uplink modulation schemes for LTE-A. Simulation results show that, in a 2times2 antenna configuration, the turbo processing allows a consistent improvement of the link performance, being SC-FDM the one having higher relative gain with respect to linear detection. The turbo receiver's impact is however much reduced for both modulation schemes in a 2times4 configuration, due to the higher diversity gain provided by the additional receive antennas.

QoS-Aware Single Cell Admission Control for UTRAN LTE Uplink

Abstract: UTRAN Long Term Evolution (LTE) architecture shall support end-to-end quality of service (QoS). To maintain the QoS of in-progress sessions in a cell it is important to admit a new radio bearer only if all the existing sessions and the new bearer can be guaranteed QoS according to their requirements. Hence admission control (AC) has an important role to play for QoS provisioning. In this paper we propose an AC algorithm for LTE uplink utilizing the fractional power control formula agreed in 3GPP. The proposed AC algorithm is compared with a reference AC algorithm. It is shown that at 10% unsatisfied user probability the carried traffic gain of the proposed AC algorithm can be up to around 29% over the reference AC algorithm. Furthermore, the proposed AC algorithm inherently tunes itself for varying load conditions without additional complexity.

Empirical LTE Smartphone Power Model with DRX Operation for System Level Simulations

Abstract: An LTE smartphone power model is presented to enable academia and industry to evaluate users' battery life on system level. The model is based on empirical measurements on a smartphone using a second generation LTE chipset, and the model includes functions of receive and transmit data rates and power levels. The first comprehensive Discontinuous Reception (DRX) power consumption measurements are reported together with cell bandwidth, screen and CPU power consumption. The transmit power level and to some extent the receive data rate constitute the overall power consumption, while DRX proves to be a very efficient method to prolong the battery life.

Evaluation of Empirical Ray-Tracing Model for an Urban Outdoor Scenario at 73 GHz E-Band

Abstract: In the summer of 2013, a wideband propagation measurement campaign using rotating directional antennas at 73 GHz was conducted at the New York University (NYU) campus, in order to collect extensive field measurements for use in a millimeter wave (mmWave) E-band statistical channel model. While the measurement campaign provided over 50 Gigabytes of wideband power delay profiles and angular responses [1], [2], the time and labor intensive measurements were based on only 5 transmitter (Tx) locations and 27 receiver (Rx) locations, making up a total of 74 Tx-Rx link combinations. To help generalize the measurements for immediate model development and eventual site planning, this paper presents an empirical ray-tracing model, with the goal of finding a suitable approach such that ray-tracing (RT) can fill in the gaps of the measurements. Here, we use the measured data to investigate the prediction capability of an empirical RT model, in which the 3D model of New York City (including the building structures and interaction losses) are greatly simplified. The comparison between the measured and predicted results show good accuracy is obtained when a simplified RT model is used, suggesting that fast and simple ray tracers will be able to correctly predict the propagation characteristics at mmWave bands.

Enabling Early HARQ Feedback in 5G Networks

Abstract: Besides coping with the increasing demand of broadband services, 5th Generation (5G) radio access technology is expected to support mission critical communication (MCC) services targeting very low latencies. In this paper, we investigate the feasibility of the usage of an early Hybrid Automatic Repeat reQuest (HARQ) feedback for reducing the latency of acknowledged transmissions without disregaring spectral efficiency. As enabler of such early feedback, a new technique for predicting the decoder outcome before decoding occurs, is proposed. This technique is intended to generate an early ACK/NACK, or an uncertain feedback in case a reliable prediction cannot be achieved. Simulation results show a very limited occurrence of false positives and false negatives, and uncertain feedback rates not exceeding 6% when adaptive modulation and coding (AMC) and a 10% Block Error Rate (BLER) target is assumed. A correct early ACK/NACK can be generated for around 90% of the transmissions or more.

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.