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.

A Simple Statistical Signal Loss Model for Deep Underground Garage

Abstract: In this paper we address the channel modeling aspects for a deep-indoor scenario with extreme coverage conditions in terms of signal losses, namely underground garage areas. We provide an in- depth analysis with regard to the path loss (gain) and large-scale signal shadow fading, and propose a simple propagation model which can be used to predict cellular signal levels in similar deep- indoor scenarios. The measurement results indicate that the signal at 800 MHz band penetrates external concrete walls to reach the lower levels, while for 2000 MHz wall openings are required for the signal to propagate. It is also evident from the study that the shadow fading at different levels of an underground garage are highly correlated. The proposed frequency-independent floor attenuation factor (FAF) is shown to be in range of 5.2 dB per meter deep. Therefore, the attenuation rate in the z dimension is much higher than the in-building attenuation in x and y dimension, which is often assumed at 0.6 dB/m.

Interference cancellation and 4-branch antenna diversity for WCDMA uplink packet access

Abstract: This paper addresses the performance gain of interference cancellation (IC) and 4-branch antenna diversity for enhanced packet data access in WCDMA uplink. A theoretical analysis is derived showing that the cell throughput gain from IC mainly depends on three factors: 1) the efficiency of the multi-user detection (MUD) receiver, 2) the other-to-own cell interference ratio, and 3) the uplink load. The gain from 4-branch antenna diversity and IC is then estimated bv means of system level simulations for two different node B scheduling concepts. Given specific outage constraints for both network load and user performance, the capacity gain from 4-branch compared to 2-branch antenna diversity is approx. 100% The cell throughput gain from IC is approx. 20% for an IC efficiency of 30% and 50% for an IC efficiency of 70% The estimated gain numbers from IC are almost independent of the considered scheduling scenario and are in accordance with the theoretical results. In the most optimistic case, the total cell throughout gain from fast node B scheduling, IC and 4-branch antenna diversity is 364% compared to a system implementation with RNC scheduling, no interference cancellation and 2-branch antenna diversity.

Preemption-Aware Rank Offloading Scheduling for Latency Critical Communications in 5G Networks

Abstract: This paper introduces a preemptive rank offloading scheduling framework for joint ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic in 5G new radio (NR). Proposed scheduler dynamically adapts the overall system optimization among the network-centric ergodic capacity and the user-centric URLLC one-way latency, based on the instantaneous traffic and radio resources availability. The spatial degrees of freedom, offered by the transmit antenna array, are fully exploited to maximize the overall spectral efficiency. However, when URLLC traffic buffering is foreseen, proposed scheduler immediately enforces scheduling pending URLLC payloads through preemption-aware subspace projection. Compared to the state-of-the-art schedulers from industry and academia, proposed scheduler framework shows significant scheduling flexibility in terms of the overall ergodic capacity and URLLC latency performance. The presented results therefore offer valuable insights of how to most efficiently multiplex joint URLLC-eMBB traffic over the 5G NR spectrum.

Learning to Dynamically Allocate Radio Resources in Mobile 6G in-X Subnetworks

Abstract: This paper investigates efficient deep learning based methods for interference mitigation in independent wireless subnetworks via dynamic allocation of radio resources. Resource allocation is cast as a mapping from interference power measurements at each subnetwork to a class of shared frequency channels. A deep neural network (DNN) is then trained to approximate this mapping using data obtained via application of centralized graph coloring (CGC). The trained network is then deployed at each subnetwork for distributed channel selection. Simulation results in an environment with mobile subnetworks have shown that relatively small-sized DNNs can be trained offline to perform distributed channel allocation. The results also show that regardless of the choice of initialization, a DNN for distributed channel selection can achieve similar performance as CGC up to a probability of loop failure (PLF) of 6 × 10–5 in diverse environments with only aggregate interference power measurements as input.

Measurement and Analysis of Radio Frequency Interference in the UWB Spectrum

Abstract: Ultra-wide band (UWB) radio systems are expected to operate in co-existence with a myriad of other systems over a large unlicensed bandwidth. Thus, UWB devices need to incorporate efficient inter-system interference mitigation mechanisms. In this paper, we present interference measurements covering the UWB spectrum from 3GHz to 11GHz conducted at two locations (indoor and outdoor) on the campus of Aalborg University, Denmark. We analysed the measurements in terms of occurrence probability, interference power distribution and inter-arrival time statistics. The goal is to understand the characteristics of signals emanating from systems operating on this ultra-wide bandwidth as a basis for the development of models and methods for interference characterization and mitigation. Results indicate that signal activity vary significantly across the spectrum with the 5GHz - 6GHz and 9GHz - 10GHz sub-bands having the strongest power levels in the indoor and outdoor measurements, respectively. Statistical analysis results further show significant variation of the power distribution, occurrence probability and inter- arrival time statistics for the various signals detected in the measurements. Results also show that time between interference occurrence is exponentially distributed for most of the sources.

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.