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

Full duplex transmission is envisioned as one of the potential breakthrough for a $5^{th}$ Generation (5G) radio access technology. In this paper, we evaluate the performance of full duplex transmission in a network of dense small cells by using real channel measurements. A large measurement campaign is carried out in an indoor office and an open hall scenario, and such measurements are then fed to a system level simulator for offline analysis. Cell throughput results show that the promised 100\% performance gain of full duplex over traditional half duplex transmission is significantly jeopardized by the increase of the intercell interference due to the simultaneous transmission and reception, and is further reduced in case of low cell activity factor.

Evaluating Full Duplex Potential in Dense Small Cells from Channel Measurements

Low Earth orbit (LEO) satellite networks will play a major role in future broadband communications. To study LEO satellite mobility performance, and guarantee reliable service, accurate channel characterization is necessary. Current models, such as the ones reported by 3GPP and ITU, define channel parameters as a function of elevation angle, frequency and deployment scenario. However, key parameters including the line-of-sight (LOS) probability and the shadow fading do not describe any time component or correlation and, therefore, the movement of the satellite is not captured. This fact might result in unrealistic changes of the LOS/NLOS states hampering the radio mobility studies for 5G-based LEO satellite networks. In this paper, we present a Markov-based channel state model that takes into consideration the time correlations of the LOS variations along the satellite’s orbit. The proposed model is based on ray-tracing measurements of suburban and dense urban scenarios and it can accurately reflect the behaviour of the channel state variations of those scenarios, essentially leading to a realistic number of state changes per satellite pass.

A Time-correlated Channel State Model for 5G New Radio Mobility Studies in LEO Satellite Networks

The Radio Link Control (RLC) protocol of Universal Mobile Telecommunication System (UMTS) provides link layer reliability that could mitigate the effects of the hostile radio propagation channel on packet data transmission. In this paper, the impact of some of the RLC reliability mechanisms on the performance of File Transport Protocol (FTP) is investigated. The investigations are carried out using a real time emulation platform, which makes the results from this study more realistic than simulation or simplified analytical studies as the overall End-2-End performance is analyzed involving real world protocol implementations.

Emulation based performance investigation of FTP file downloads over UMTS dedicated channels

An algorithm for code phase acquisition in direct-sequence spread-spectrum systems is presented and analyzed. The algorithm is a window based double dwell searcher. The algorithm with a moderate window size has shorter acquisition than the immediate verification algorithm has-especially at low SNR. The first-dwell uses a robust method based on a rank filter while the second dwell uses known threshold hypothesis. The code phase acquisition algorithm is compared with both single and double threshold hypothesis algorithms.

https://ieeexplore.ieee.org/iel5/6849/18414/00851458.pdf

Adaptive threshold settings in code phase acquisition for the double dwell architecture

Extensive field strength measurements were made in Aalborg and Copenhagen at different base station locations and antenna heights in order to determine the antenna height influence on path loss. The measurements were performed simultaneously at 955 and 1845 MHz to get the exact difference in path loss between the two frequencies. Results from measurements in two areas in Aalborg are presented. They are compared with the path loss prediction model developed by Hata (1980) to see if it is suitable for very small cell size propagation modelling. The authors conclude that it is only applicable if extra terms are added. >

http://ieeexplore.ieee.org/iel3/1216/3116/00098286.pdf

Preben Mogensen

Papers

Evaluating Full Duplex Potential in Dense Small Cells from Channel Measurements

A Time-correlated Channel State Model for 5G New Radio Mobility Studies in LEO Satellite Networks

Emulation based performance investigation of FTP file downloads over UMTS dedicated channels

Adaptive threshold settings in code phase acquisition for the double dwell architecture

Urban area radio propagation measurements for GSM/DCS 1800 macro and micro cells