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

The Design and Analysis of Experiments

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.).

Machine learning

Wireless Communications

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].

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Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

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.

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Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

An ultra-dense deployment of small cells is foreseen as the solution to cope with the exponential increase of the data rate demand targeted by the 5th Generation (5G) radio access technology. In this article, we propose an interference-robust air interface built upon the usage of advanced receivers as main interference mitigation technique. Both Interference Rejection Combining (IRC) and Successive Interference Cancellation (SIC) principles are considered. An efficient usage of such receivers is ensured by a proper frame structure design and system assumptions. Different approaches for the rank adaptation are also investigated. Simulation results show that proposed air interface built upon advanced receiver can be a feasible alternative to traditional solutions based on frequency reuse planning. Open issues and further challenges are also addressed.

Interference-Robust Air Interface for 5G Ultra-dense Small Cells

The performance of uplink synchronous WCDMA systems for low dispersive environments is evaluated at network level. The capacity increase is computed for the particular case of UMTS, taking into account the impact of effects like the code shortage, the number of receiver antennas, the penetration rate, and the use of soft handover. This is done theoretically and by means of a dynamic simulator, which considers more realistic conditions. Theoretical calculations match the simulation results. According to the results generated for speech service, the main problem in uplink synchronous WCDMA systems is the code shortage. For Pedestrian A environments with the most realistic conditions uplink synchronous WCDMA provides a 9.6% capacity gain. However, a very high potential exists for situations where this code shortage can be solved, i.e. 35.8% capacity gain assuming no code restriction.

Performance of uplink synchronous WCDMA at network level

Network-Assisted Interference Cancellation and Suppression (NAICS) receivers have appeared as a promising way to curb inter-cell interference in future dense network deployments. This investigation compares the performance of a NAICS receiver with successive interference cancellation capabilities, known as Symbol-Level Interference Cancellation (SLIC), with respect to a baseline Minimum Mean Square Error-Interference Rejection Combining (MMSE-IRC) receiver. The study is carried out on a dense, clusterized small cell network, illustrating to which extent NAICS can overcome the additional interference associated with such deployments. Moreover, we analyse how much the data rates could be potentially improved by estimating the throughput with ideal cancellation of the dominant interferer. The results point to limited gains of up to 12% in the coverage data rates when NAICS is used, and that the instantaneous throughput might increase up to 100% in the most favourable case, falling significantly below the estimated 200% maximum gain with ideal cancellation.

Sensitivity Analysis of Centralized Dynamic Cell Selection

The usage of beamforming in Unmanned Aerial Vehicles (UAVs) has the potential of significantly improving the air-to-ground link quality. This paper presents the outcome of experimental trial of such a UAV-based beamforming system over live cellular networks. A testbed with directional antennas has been built for the experiments. It is shown that beamforming can extend the signal coverage due to antenna gain, as well as spatially reduce interference leading to higher signal quality. Moreover, it has a positive impact on the mobility performance of a flying UAV by reducing handover occurrences. It is also discussed, in which situations beamforming should translate into the uplink throughput gain.

Experimental evaluation of beamforming on UAVs in cellular systems

Dynamic spectrum approaches are steadily gaining momentum, especially in the context of femtocells. Yet designing efficient, stable, fair and scalable distributed algorithms is no easy feat, specially if the cells in a wireless network tend to act selfish and independently. Game Theory is a powerful toolbox which models the interaction of autonomous agents. In this paper we present a game theoretic model for a dynamic spectrum sharing framework recently proposed for femtocells. Our analysis includes cases where femtocells compete for spectrum as well as cooperative cases towards a common goal. The system level simulation results show that strict adherence to the game-theoretic selfish behavior performs poorly compared to the non-adherent rules which balance altruism and rational egoism. The main conclusion is that practical solutions should be guided but not limited by purely theoretical assumptions.

Preben Mogensen

Papers

Interference-Robust Air Interface for 5G Ultra-dense Small Cells

Performance of uplink synchronous WCDMA at network level

Sensitivity Analysis of Centralized Dynamic Cell Selection

Experimental evaluation of beamforming on UAVs in cellular systems

Dynamic Spectrum Sharing in Femtocells: A Comparison of Selfish versus Altruistic Strategies