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

The 3rd Generation Partnership Project (3GPP) is currently specifying the system requirements for Long Term Evolution - Advanced (LTE-A), having as a target peak data rates of 1 Gbit/s in local areas and 100 Mbit/s in wide areas. To meet these ambitious requirements for the uplink, multiple-input-multiple-output (MIMO) antenna techniques are expected to be deployed. In this paper, several channel-aware MIMO precoding techniques are presented. Specifically, precoded single user spatial multiplexing for both Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency Division Multiplexing (SCFDM) is studied, and its feasibility in a LTE-A uplink system is discussed. Particular emphasis is given to the limited feedback precoding, where a codebook generation method based on the Lloyd algorithm is proposed. Results show that, when full channel knowledge is available at both the transmitter and the receiver, precoding leads to a spectral efficiency gain up to 4 dB over open loop transmission; furthermore, OFDM slightly outperforms SC-FDM because of its higher robustness to the noise. Limited feedback precoding has been shown to be effective and consistently robust to the subcarrier grouping in a urban micro scenario. However, the performance is severely degraded in the blind precoding case, where transmitter and receiver compute the precoding matrix independently, due to the high sensitivity to the delay. Finally, the precoding operation over the SCFDM signal is shown to increase its Peak to Average Power Ratio (PAPR), thus reducing its advantage with respect to OFDM.

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On the Feasibility of Precoded Single User MIMO for LTE-A Uplink

In this paper we present the results of an extensive measurement campaign performed at two large iron ore mining centers in Brazil at the 2.6 GHz band. Although several studies focusing on radio propagation in underground mines have been published, measurement data and careful analyses for open-pit mines are still scarce. Our results aim at filling this gap in the literature. The research is motivated by the ongoing mine automation initiatives, where connectivity becomes critical. This paper presents the first set of results comprising measurements under a gamut of propagation conditions. A second paper detailing sub-GHz propagation is also in preparation. The results indicate that conventional wisdom is wrong, in other words, radio-frequency (RF) propagation in surface mines can be far more elaborate than plain free-space line-of-sight conditions. Additionally, the old mining adage “no two mines alike” seems to remain true in the RF domain.

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Radio propagation in open-pit mines: A first look at measurements in the 2.6 GHz band

In this paper, we present a novel approach for pilot-aided channel estimation in OFDM systems with synchronous co-channel interference. The estimator is derived based on the Kullback-Leibler divergence minimization framework. The obtained solution iteratively updates both the desired user's and the interferer's channels, using a combination of linear minimum mean squared-error (LMMSE) filtering and interference cancellation, avoiding the complex matrix inversions involved in the full LMMSE channel estimation approach. Estimation of the noise variance is also included in the iterative algorithm, accounting for Gaussian noise and residual interference after each iteration. The estimates of both channels are used at the equalizer to reject the interfering signal, thus mitigating the degradation due to co-channel interference. Simulation results show that the receiver using the proposed estimator performs as good as the one employing the full LMMSE estimator and very closely to a receiver having perfect knowledge of the channel coefficients.

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Channel Estimation Based on Divergence Minimization for OFDM Systems with Co-Channel Interference

In the last years, wireless communications in industrial scenarios are becoming an increasingly important market. Some of these communications have tight reliability requirements, but harsh propagation conditions in industrial scenarios represent a major challenge. In this paper, multi-connectivity is explored as a solution for assuring high reliability in industrial scenarios. Several multi-connectivity techniques are compared, using real channel measurements from two factories. Multiconnectivity comes at the cost of a reduced throughput in the mobile broadband services on the same network. In this paper, this impact is quantified to assess for the cost of implementing multi-connectivity.

/pdf/multi-connectivity-for-ultra-reliable-communication-in-1yu4hq3zdy.pdf

Preben Mogensen

Papers

On the Feasibility of Precoded Single User MIMO for LTE-A Uplink

Radio propagation in open-pit mines: A first look at measurements in the 2.6 GHz band

Channel Estimation Based on Divergence Minimization for OFDM Systems with Co-Channel Interference

Multi-Connectivity for Ultra-Reliable Communication in Industrial Scenarios

Network Performance of Transmit and Receive Diversity in HSDPA under Different Packet Scheduling Strategies