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

This study addresses the performance of the high speed downlink packet access (HSDPA) concept when transmit and/or receive antenna diversity techniques are used. Two branch space-time transmit diversity (STTD), closed loop mode-1 transmit diversity (CLTD), dual RAKE receivers at the user equipment (UE), and the combination of dual RAKE and CLTD are analyzed for round robin and proportional fair packet scheduling. These schemes are compared against a reference scenario with single antenna transmission and reception. In pedestrian-A channels at 3 km/h, the combination of CLTD and dual RAKE receivers results in a cell capacity gain of 123% for round robin, while the gain is reduced to 38% for proportional fair. For round robin, the throughput gain per UE is found to be significantly larger for UEs at the cell edge (up to 320%), compared to UEs close to the base station. The equivalent throughput gain per UE for proportional fair equals 115%.

Network performance of transmit and receive antenna diversity in HSDPA under different packet scheduling strategies

The support of mission-critical communication (MCC) opens the possibility to implement a broad range of novel applications. V2X communication for traffic safety and automation is, among others, one of these innovative applications expected to bring big benefits to society: accidents are prevented, driving times are reduced, and carbon dioxide is saved. In this regard, we first present a system model and fundamental definitions of reliability, latency and availability. Relying on these definitions, a systematic review of requirements for the huge variety of V2X applications is provided, including insights into the expected evolution towards autonomous driving. The many challenges introduced by V2X use cases are emphasized and compared to today's wireless system capabilities. Finally, we give our vision on the design of future radio technologies for the support of this kind of communications.

/pdf/automation-for-on-road-vehicles-use-cases-and-requirements-4ica8mz4ea.pdf

Automation for On-Road Vehicles: Use Cases and Requirements for Radio Design

In this paper, an iterative enhancement of the robust Wiener filter (RWF) estimator is presented for a turbo-coded orthogonal frequency division multiplexing (OFDM) system under the umbrella of the 3GPP long term evolution. The proposed scheme can operate with uncoded or coded feedback, and outperforms the conventional linear RWF in the whole signal-to-noise ratio (SNR) range with both approaches. Results show that most of the gain is obtained in the first iteration of the algorithm, and better performance is achieved with the coded feedback scheme. A good tradeoff between accuracy and complexity is achieved by selecting a low number of turbo coding iterations (TCI) in the iterative loop and concentrating most of them at the final decoding stage. Following this design, cell spectral efficiency gains of around 2.7% and 6.5% can be obtained with respect to linear RWF for micro- and macro-cell scenarios respectively.

Iterative Channel Estimation with Robust Wiener Filtering in LTE Downlink

A simple time-variant stochastic radio channel model is proposed. The model includes temporal and azimuthal dispersion, polarization effects, fast fading, and dynamic evolution of paths. All relevant model parameters are extracted from extensive measurement campaigns in a typical urban environment.

Dual-polarized model of outdoor propagation environments for adaptive antennas

The invention is related to a data transmission method in a telecommunication system. The method comprises determining (202) the number of bit rate classes, setting (204) bit rates for the bit rate classes, setting (206) a maximum transmission power target, arranging (208) resource requests into a queue, allocating (210) resources according to the requests in the queue until the maximum power target is achieved.

Preben Mogensen

Papers

Network performance of transmit and receive antenna diversity in HSDPA under different packet scheduling strategies

Automation for On-Road Vehicles: Use Cases and Requirements for Radio Design

Iterative Channel Estimation with Robust Wiener Filtering in LTE Downlink

Dual-polarized model of outdoor propagation environments for adaptive antennas

Data transmission method, radio network controller and base station