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.

/pdf/cognitive-radio-an-integrated-agent-architecture-for-1v855bviue.pdf

Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

The main goal of this letter is to obtain models for path loss exponents and shadowing for the radio channel between airborne unmanned aerial vehicles (UAVs) and cellular networks In this pursuit, field measurements were conducted in live LTE networks at the 800 MHz frequency band, using a commercial UAV Our results show that path loss exponent decreases as the UAV moves up, approximating freespace propagation for horizontal ranges up to tens of kilometers at UAV heights around 100 m Our findings support the need of height-dependent parameters for describing the propagation channel for UAVs at different heights

/pdf/radio-channel-modeling-for-uav-communication-over-cellular-17s30wg7r3.pdf

Radio Channel Modeling for UAV Communication Over Cellular Networks

Capacity increases promised by multiple-input multiple-output (MIMO) systems mostly depend on the spatial correlation properties of the radio channel. The paper investigates the connection between these properties and the capacity figures. It first derives the correlation coefficient between two antenna elements as a function of their spacing, the power azimuth spectrum (PAS), the azimuth spread (AS) and the mean angle of incidence of the waves, for three different types of PAS, namely uniform, truncated Gaussian and truncated Laplacian. With the help of the established relations, correlated flat-fading MIMO channels are generated, whose capacity performance and effective degrees of freedom (EDOF) are investigated for two power allocation schemes, water-filling and uniform. The impact of channel estimation errors is also evaluated.

From antenna spacings to theoretical capacities - guidelines for simulating MIMO systems

Empirical results characterizing the joint statistical properties of the local azimuth spread (AS), the local delay spread (DS), and the shadow (slow) fading component are presented. Measurement data from typical urban, bad urban, and suburban (SU) environments have been analyzed. It is found that a log-normal distribution accurately fits the distribution function of all the investigated parameters. The spatial autocorrelation function of both AS, DS, and shadow fading can be modeled with an exponential decay function. However, for SU environments the spatial autocorrelation function is better characterized by a composite of two exponential decaying functions. A positive cross correlation is found between the AS and the DS, while both parameters are negatively correlated with shadow fading. All essential parameters required for the implementation of a simulation model considering the joint statistical properties of the AS, DS, and shadow fading are provided.

/pdf/experimental-analysis-of-the-joint-statistical-properties-of-168922i7z7.pdf

Experimental analysis of the joint statistical properties of azimuth spread, delay spread, and shadow fading

This paper present measurement results obtained with a testbed equipped with an array antenna. The investigations focus on the power azimuth spectrum of the channel in urban and rural areas, for various base station antenna heights. The power azimuth spectrum is well modeled with a Laplacian function. The local azimuth spread is found to range from 1/spl deg/ to 25/spl deg/ depending on the environment and antenna height. The azimuth spread significantly increases as the antenna height is reduced. An analytical expression for the spatial correlation function is derived based on the Laplacian model. Finally, the power azimuth spectrums impact on a conventional beamformer ability to suppress interfering users is investigated.

Spatial channel characteristics in outdoor environments and their impact on BS antenna system performance

From extensive 2D channel measurements it is found that the shape of the power azimuth spectrum at the base station in outdoor environments is accurately described by a truncated Laplacian function in both rural and urban environments.

Preben Mogensen

Papers

Radio Channel Modeling for UAV Communication Over Cellular Networks

From antenna spacings to theoretical capacities - guidelines for simulating MIMO systems

Experimental analysis of the joint statistical properties of azimuth spread, delay spread, and shadow fading

Spatial channel characteristics in outdoor environments and their impact on BS antenna system performance

Power azimuth spectrum in outdoor environments