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.

Can full duplex reduce the discovery time in D2D communication

Abstract: Device-to-device (D2D) communication is considered as one of the key technologies to support new types of services, such as public safety and proximity-based applications. D2D communication requires a discovery phase, i.e., the node awareness procedure prior to the communication phase. Conventional half duplex transmission may not be sufficient to provide fast discovery and cope with the strict latency targets of future 5G services. On the other hand, in-band full duplex, by allowing simultaneous transmission and reception, may complete the discovery phase faster. In this paper, the potential of full duplex in providing fast discovery for the next 5th generation (5G) system supporting D2D communication is investigated. A design for such system is presented and evaluated via simulations, showing that full duplex can accelerate the discovery phase by supporting a higher transmission probability compared to half duplex. Simulation results show that, in order to meet the strict 5G control plane latency target, advanced receivers are required. In that case, full duplex can reduce the latency up to 80%.

On the Impact of Receiver Imperfections on the MMSE-IRC Receiver Performance in 5G Networks

Abstract: The usage of Minimum Mean Square Error - Interference Rejection Combining (MMSE-IRC) receivers is expected to be a significant performance booster in the ultra-dense deployment of small cells envisioned by an upcoming 5th generation (5G) Radio Access Technology (RAT). However, hardware limitations of the radio- frequency front-end and poor covariance matrix estimation may severely compromise its ideal gains. In this paper, we evaluate the network performance of MMSE-IRC receivers by including the effects of the receiver imperfections as well as realistic covariance matrix estimates. System level simulation results confirm that a realistic MMSE- IRC receiver can achieve throughput gains close to ideal, provided a reasonably high resolution Analog-to-Digital Converter (ADC) as well as a supportive radio frame format design are used.

Reference sequence design for zero-tail DFT-spread-OFDM

Abstract: Zero-tail Discrete Fourier Transform-spread OFDM (ZT DFT-s-OFDM) modulation replaces the Cyclic Prefix (CP) with a low power tail whose length can be dynamically configured to cope with the instantaneous delay spread of the channel. In this paper, we discuss the reference sequence design for ZT DFT-s-OFDM. Our proposed approach is based on a deliberate distortion of the known Zadoff-Chu (ZC) sequences aiming at adapting them to the ZT DFT-s-OFDM waveform while preserving their attractive properties. A numerical analysis confirms the validity of our design in generating ZT DFT-s-OFDM reference sequences with zero autocorrelation, limited cross-correlation, flat frequency response and low Peak-to-Average Power Ratio (PAPR).

Improving Drone's Command and Control Link Reliability through Dual-Network Connectivity

Abstract: In this work, we analyze the end-to-end latency measured in a client-server application that emulates the traffic requirements for the Unmanned Aerial Vehicle (UAV)’s Command and Control (C2) link. The connectivity is provided by two real LTE-A networks to a client attached to a flying UAV. Measurements are performed at 4 different heights: ground level, 15 m, 40 m and 100 m. In single operator scenarios, the reliability measured at the target latency, 50 ms, was between 99.6 % and 97.6 % in downlink, and 91.3% and 99.4% in uplink. These results are below the 99.9 % target reliability defined for UAVs and they show that several consecutive packets can be missed when the radio link connectivity degrades, leading to high (> 1 s) values for the 99.9%-ile of latency. To circumvent this, a dualoperator hybrid access scheme is proposed in this paper. The results show that the hybrid access strategy managed to reach the performance requirements in most cases. The solution shows potential to enable C2 over cellular networks, without requiring optimization or modifications in the network.

Subjective evaluation of packet service performance in UMTS and heterogeneous networks

Abstract: Quality of Service in mobile telecommunication systems is usually identified by some basic performance metrics such as delay, throughput and jitter However, the main impact of service quality is on the end user, and as such a detailed study of service performance should involve the end user In this paper, such an approach is taken where subjective performance evaluation is undertaken for web browsing and video streaming services in Universal Mobile Telecommunication System (UMTS) and a heterogeneous network comprised of UMTS and Wireless LAN (WLAN)Using a real-time network emulation testbed, users rated the perceived quality of the services under different network conditions The users' ratings are analyzed and the results showed clear trends with very few outliers The objective and subjective measures also were found to be in line except for one case where a higher layer effect influenced the users' perception

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.