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].

/pdf/scaling-up-mimo-opportunities-and-challenges-with-very-large-puqp9v7gzk.pdf

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

An uplink admission control algorithm for WCDMA base stations equipped with antenna arrays is presented. Conventional beamforming is used as a combining method. In order to capture the azimuth-dependent interference scenario, and take advantage of the spatial filtering properties of antenna arrays, an azimuth measure of load is defined. For each azimuth direction, the load is defined as the amount of received power.

Directional power based admission control for WCDMA systems using antenna arrays

In this paper, we investigate frequency domain link adaptation (FDLA), e.g. utilizing the frequency selectivity of the channel in an OFDMA system. To make the study specific and based on realistic parameters, we re-use the specifications from a recent 3GPP 5-MHz OFDMA study item. The link adaptation and the frequency domain link adaptation are developed in a way compliant with the basic HSDPA specifications. With FDLA we show up to 75% cell throughput gain over the OFDMA reference system at the cost of increased uplink channel quality signaling overhead for frequency selective channels. We find that optimum waterfilling power distribution only provides a marginal gain over a simpler on/off equal power distribution algorithm per sub-carrier pool when signaling imperfections are taken into account.

http://ieeexplore.ieee.org/iel5/10360/32960/01543562.pdf

Investigation of frequency-domain link adaptation for a 5-MHz OFDMA/HSDPA system

Unmanned Aerial Vehicles (UAVs) are expected to be connected through cellular networks As the radio characteristics are different for airborne UEs compared to terrestrial UEs, it is beneficial to identify whether a UE is airborne (on a UAV) or on the ground, such that interference and mobility management can be optimized for UAVs separately from terrestrial UEs In this paper, we present a classification algorithm using existing LTE UE radio measurements to identify whether a UE is airborne or terrestrial The method is verified with LTE measurements made in a rural area at different heights, including terrestrial measurements and it is shown that the method in 3 out of the 4 different measurement cases can detect a UE to be airborne with 99% likelihood, while the fourth case still can classify a UE correctly in 95% of the cases The right classification can further be improved by taking multiple consecutive samples into account before making a classification decision

Method for detection of airborne UEs based on LTE radio measurements

A Novel Centimeter-Wave Concept for 5G Small Cells

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.

Preben Mogensen

Papers

Directional power based admission control for WCDMA systems using antenna arrays

Investigation of frequency-domain link adaptation for a 5-MHz OFDMA/HSDPA system

Method for detection of airborne UEs based on LTE radio measurements

A Novel Centimeter-Wave Concept for 5G Small Cells

Interference Management with Successive Cancellation for Dense Small Cell Networks