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

MIMO (Multiple Input Multiple Output) channels are known for some time now to offer either greater link gains or better spectral efficiency, depending on the correlation level of the scattering environment [1]. However, the benefits to be gained from the use of smarter antenna topologies do definitely not rule out gains available by the use of other techniques, such as polarisation diversity. The present paper investigates the benefits offered by this technique in an outdoor-to-indoor microcell scenario. More specifically, it corroborates previous conclusions drawn from measurements presented in [2] with the help of simulation results. These results have been produced using the COSSAP ® implementation of a stochastic model of MIMO radio channels developed in the framework of European-funded IST (Information Society Technologies) METRA (Multi Element Transmit and Receive Antenna) project [3]. Moreover, two power allocation strategies are applied to the scenario under study and their performance is compared.

Simulating polarisation diversity and power allocation in MIMO channels

/pdf/wireless-communications-and-networking-conference-wcnc-2014-3gx8j7qiua.pdf

Wireless Communications and Networking Conference (WCNC), 2014 IEEE

This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared downlink channel. Such a scenario presents multiple challenges in the context of radio resource scheduling, link adaptation and inter-cell interference, which are identified and addressed throughout the paper. Specifically, a joint link adaptation and resource allocation technique is proposed that dynamically adjusts the block error probability (BLEP) of the URLLC payload transmissions in accordance with the instantaneous experienced load per cell. Extensive system-level simulations of the downlink performance show promising gains of this technique, reducing the URLLC latency from 1.3 ms to 1 ms at the 99.999% percentile, with less than 10% degradation of the eMBB throughput performance as compared to conventional scheduling policies.

Multiplexing of latency-critical communication and mobile broadband on a shared channel

For the UTRA long term evolution (LTE), the transmit signal spectrum must comply with stringent spectrum emission mask requirements because of very low guard band size for high spectral efficiency. Compared to e.g. WCDMA, the requirements are tightened, which calls for efficient user equipment (UE) transmitter design, to balance the tradeoff among performance and hardware complexity. In this paper a spectrum shaping technique suitable for LTE user equipment is proposed and evaluated. The key component of this technique is an inverse Chebyshev digital IIR filter which has maximally flat in the passband and sharp transition from passband to stopband. We compare this technique with the well-known time windowing method which offers low implementation complexity. We study the two techniques impact on both error vector magnitude (EVM) and spectrum emission for two different LTE system bandwidths and for various bandwidth allocations for the user. Results show that for wideband setting (10 MHz) both techniques are able to produce acceptable EVM values less than 0.2%. However, for narrowband setting (1.4 MHz), only digital filtering performs sufficiently well since the time windowing method fails when the window overlap size is greater than the cyclic prefix length.

A spectrum shaping technique to control spectrum emission of UTRA LTE user equipment

In this paper, the usage of dedicated portions of cellular spectrum to provide the high-reliable Command and Control (C2) link for Unmanned Aerial Vehicles (UAVs) is evaluated. Simulations are performed using data settings of a real operating Long-Term Evolution (LTE) network in Denmark, in order to assess the reliability of the C2 link. Up to date databases of drone registrations and market projections are used to infer the drone densities and estimate the future traffic demand. Based on these estimations, network capacity results show that, deploying a sparse network with reservation of 1.4 MHz is sufficient for most cases according to current demands. In the next 20 years, the increase in demand can be followed by a continuous deployment of sites and an increase in the bandwidth up to 5 MHz. The paper also presents a discussion about which solutions can be used to further boost network capacity, and help to achieve high reliability even for the most stringent traffic demand cases.

/pdf/forecasting-spectrum-demand-for-uavs-served-by-dedicated-2qkewbaofs.pdf

Preben Mogensen

Papers

Simulating polarisation diversity and power allocation in MIMO channels

Wireless Communications and Networking Conference (WCNC), 2014 IEEE

Multiplexing of latency-critical communication and mobile broadband on a shared channel

A spectrum shaping technique to control spectrum emission of UTRA LTE user equipment

Forecasting Spectrum Demand for UAVs Served by Dedicated Allocation in Cellular Networks