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.

Analysing self interference cancellation in full duplex radios

Abstract: Full duplex communication promises a theoretical 100% throughput gain by doubling the number of simultaneous transmissions. Such compelling gains are conditioned on perfect cancellation of the self interference power resulting from simultaneous transmission and reception. Generally, self interference power is modelled as a noise-like constant level interference floor. However, experimental validations have shown that the self interference power is in practice a random variable depending on a number of factors such as the surrounding wireless environment and the degree of interference cancellation. In this study, we derive an analytical model for the residual self interference power, and demonstrate various applications of the derived model in analysing the performance of a Full Duplex radio. In general, full duplex communication is found to provide only modest throughput gains over half duplex communication in a dense network scenario with practical self interference cancellation models.

Performance of WCDMA HSDPA in a beamforming environment under code constraints

Abstract: The application and performance of WCDMA HSPDA (high speed downlink packet access) in a beamforming environment is studied under channelization code constraints, using a blind and an intelligent packet scheduler. Results for the optimal number of beams and scrambling codes per cell are presented, where it is found that more scrambling codes and less beams are typically required when using intelligent scheduling, compared to blind scheduling. The cell capacity over single antenna transmission is observed to be larger for radio channels with low temporal dispersion, compared to scenarios with high temporal dispersion. Mainly because an additional code-efficiency gain is obtained over single antenna transmission for environments with low temporal dispersion. The code-efficiency gain originates from a lower selection probability of the higher modulation and coding schemes, when beamforming and multiple scrambling codes are used.

Analyzing the potential of full duplex in 5G ultra-dense small cell networks

Abstract: Full-duplex technology has become an attractive solution for future 5th generation (5G) systems for accommodating the exponentially growing mobile traffic demand. Full duplex allows a node to transmit and receive simultaneously in the same frequency band, thus, theoretically, doubling the system throughput over conventional half-duplex systems. A key limitation in building a feasible full-duplex node is the self-interference, i.e., the interference generated by the transmitted signal to the desired signal received on the same node. This constraint has been overcome given the recent advances in the self-interference cancellation technology. However, there are other limitations in achieving the theoretical full-duplex gain: residual self-interference, traffic constraints, and inter-cell and intra-cell interference. The contribution of this article is twofold. Firstly, achievable levels of self-interference cancellation are demonstrated using our own developed test bed. Secondly, a detailed evaluation of full-duplex communication in 5G ultra-dense small cell networks via system level simulations is provided. The results are presented in terms of throughput and delay. Two types of full duplex are studied: when both the station and the user equipments are full duplex capable and when only the base station is able to exploit simultaneous transmission and reception. The impact of the traffic profile and the inter-cell and intra-cell interferences is addressed, individually and jointly. Results show that the increased interference that simultaneous transmission and reception causes is one of the main limiting factors in achieving the promised full-duplex throughput gain, while large traffic asymmetries between downlink and uplink further compromise such gain.

On the performance of successive interference cancellation in 5G small cell networks

Abstract: The ideal successive interference cancellation paradigm helps to achieve the capacity of some multiuser channels, such as the Gaussian multiple access and broadcast channels. However, its performance is much more modest under realistic constraint on the decodability of the interference signal. In this paper, we rely on basic stochastic geometry models to analytically evaluate the performance of interference cancellation receivers in a local area network scenario, under `realistic' rate-constraints on the decodability of the interference signal. Analytical findings are validated by extensive Monte Carlo experiments. Alongside, complementary system level simulations results are presented to demonstrate the performance in a `practical'-like system. Our findings explicitly quantify how the gains from interference cancellation techniques depend on the spatial density of the active interferers in the network, and their respective data rates. The findings further highlight the importance of properly dimensioning the system in order to fully benefit from such interference cancellation techniques.

Transmission over multiple component carriers in LTE-A uplink

Abstract: Long Term Evolution - Advanced systems are currently being standardized by 3GPP and aim at very high peak data rates of 1 Gb/s in the downlink and 500 Mb/s in the uplink. Those ambitious targets can only be achieved by using advanced MIMO antenna techniques as well as wide spectrum allocation, up to 100 MHz. A multiple component carrier structure has been agreed on in the 3GPP Work Item as a solution to extend the 18 MHz bandwidth of the previous LTE Release 8 up to 100 MHz. The multiple access schemes on both uplink and downlink now have to be adapted to the new spectrum configuration. Furthermore, in the link adaptation design the transmission over multiple CCs would reasonably lead to an increase of the feedback overhead. Bundling of the spatial or frequency parameters can keep the overhead low at the cost of lower throughput. In this article, we consider as a study case the LTE-A uplink, where NxDFT-spread- OFDM has been selected as the multiple access scheme. The validity of this scheme for the uplink is evaluated in terms of cubic metric, which is an indicator of the power de-rating needed at the transmitter to avoid intermodulation distortion. Furthermore, the impact of bundling the link adaptation parameters on the link performance is discussed considering both linear and turbo successive interference cancellation (SIC) receivers. Two codeword mixing stategies in the frequency and spatial domains are also proposed to boost the performance when the bundling is made per antenna or per CC, respectively. Results show that when a linear receiver is used in the base station, mixing techniques can increase spectral efficiency, thus reducing the performance gap to the no bundling case, which is the most expensive solution in terms of feedback signaling. However, when a turbo SIC receiver is used, only mixing over CCs is a valid option to achieve link performance gain.

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.