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

Summary form only given, as follows. High speed downlink packet access (HSDPA) is currently being standardized in 3GPP for R5 and R6. HSDPA offers peak data rates exceeding 10 Mbit/s and significantly enhances the spectral efficiency of UMTS for packet services compared to R99. The draft HSDPA specifications includes advanced concepts of: adaptive modulation and coding schemes (up to 64 QAM); fast SAW hybrid ARQ; fast packet scheduling; potentially, also, fast cell selection and MIMO channel concepts.

High speed downlink packet access (HSDPA)-the path towards 3.5G

This article presents a method to model the W-CDMA radio receiver performance, which is usable in network simulation tools for third generation mobile cellular systems. The method represents a technique to combine link level simulations with network level simulations. The method is derived from that of Malkamaki, de Ryck and Mourot (1994), which defines a stochastic mapping function from a signal-to-interference ratio into a bit-error-rate for a TDMA system. However, in order to work in a W-CDMA based system, the fact that the multiple access interference in the downlink consists of both Gaussian inter-cell interference and orthogonal intra-cell interference must to taken into account. The mapping function was used to estimate the BER for a number of different simulation setups, and good agreement with link simulation results is demonstrated. Finally, an extension of the method to a soft handover situation is considered.

Modelling radio link performance in UMTS W-CDMA network simulations

This paper studies the capability of Long-Term Evolution-Advanced (LTE-A) together with WiFi 802.11ac to accommodate the traffic growths of 80x, 500x and 1000x in a high rise building scenario. It provides a guideline on the configuration of the spectrum and the best deployment options for the evolution of the heterogeneous network in order to enhance the capacity for the coming years. The results indicate that, although the deployment of outdoor cells is very important to increase the capacity of the current networks, the expansion with indoor small cells is essential in order to provide coverage and capacity for the future wireless service consumers, specially those located in the high rise buildings.

Heterogeneous Network Evolution Studies for a Dense Urban High Rise Scenario

This paper focuses on reducing the uplink overhead caused by Acknowledgement (ACK)/Non-acknowledgement (NACK) signaling in an Long Term Evolution (LTE)-Advanced system. Such a system typically aggregates multiple Component Carrier (CC)s to obtain a wide bandwidth. Usually it has much larger feedback overhead than the single-CC system, and hence reducing the overhead is of special importance. A technique called ACK/NACK bundling has been used for the overhead reduction of the ACK/NACK signaling. Along with the overhead reduction, the downlink performance is also degraded. The tradeoff between the downlink performance and the overhead is analyzed under various load conditions and antenna configurations. The presented results can be used for selecting the proper bundling scheme based on specific system setting.

System Level Analysis of ACK/NACK Bundling for Multi-Component Carrier LTE-Advanced

Advanced receivers are a key component of the 5th Generation (5G) ultra-dense small cells concept given their capability of efficiently dealing with the ever-increasing problem of inter-cell interference. In this paper, we evaluate the potential of interference suppression receivers in real network scenarios using a software defined radio (SDR) testbed. The experimentation is carried out in an indoor office and open hall scenarios. In particular, we study to what extent the interference suppression receiver is an alternative to traditional frequency reuse techniques. To this end we evaluate the Interference Rejection Combining (IRC) and Successive Interference Cancellation (SIC) receivers and different rank adaptation approaches. Each node in our software defined radio (SDR) testbed features a 2X2 MIMO transceiver built with the USRP N200 hardware by Ettus Research. Our experimental results confirm that interference suppression receivers can be a valid alternative to frequency reuse, by achieving nearly the same outage and higher peak throughput performance.

Preben Mogensen

Papers

High speed downlink packet access (HSDPA)-the path towards 3.5G

Modelling radio link performance in UMTS W-CDMA network simulations

Heterogeneous Network Evolution Studies for a Dense Urban High Rise Scenario

System Level Analysis of ACK/NACK Bundling for Multi-Component Carrier LTE-Advanced

Experimental Evaluation of Interference Suppression Receivers and Rank Adaptation in 5G Small Cells