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

Path-loss in urban area small and microcell environments is discussed. Results from dual-frequency measurements performed in urban areas at 955 MHz and 1845 MHz are presented. The mean difference in path loss between the two frequencies was found to be 10 dB, whereas the free-space loss is only 6 dB. The shadow-fading at the two frequencies was highly correlated. Therefore much of the experience from the 900-MHz band can be used for planning of new cellular systems in the 1800-2000-MHz band. M. Hata's (1980) prediction model was fitted to the data at both frequencies, but predicted too-low path loss at 1845 MHz. A modified Hata prediction model is proposed for use in the frequency range 1500 to 2000 MHz. The effect of low base station antenna heights is also discussed. >

Urban area radio propagation measurements at 955 and 1845 MHz for small and micro cells

We present our vision for a Beyond 5G Wireless Isochronous Real Time (WIRT) system for industrial control networks, designed for supporting fast closed loop control applications. WIRT aims at ultra-reliable short range wireless links with ∼0.1 ms latencies and a wired-like 10−9 reliability. The usage of a large spectrum and frequency/interference diversity are considered fundamental components for WIRT. Ultrawideband (UWB) spectrum access and unlicensed transmission at millimeter-waves (60 GHz band) are identified as possible solutions towards ultra-reliable ultra-low latency communication. The suitability and challenges of both approaches are extensively discussed, along with the way forward for WIRT design.

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Beyond 5G Wireless IRT for Industry 4.0: Design Principles and Spectrum Aspects

The ideal radio waveform for an upcoming 5th Generation (5G) radio access technology should cope with a set of requirements such as limited complexity, good time/frequency localization and simple extension to multi-antenna technologies. This paper discusses the suitability of Orthogonal Frequency Division Multiplexing (OFDM) and its recently proposed enhancements as 5G waveforms, mainly focusing on their capability to cope with our requirements. Significant focus is given to the novel zero-tail paradigm, which allows boosting the OFDM flexibility while circumventing demerits such as poor spectral containment and sensitivity to hardware impairments.

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On the Potential of OFDM Enhancements as 5G Waveforms

The present paper analyzes the paradigm of providing streaming services over HSDPA. The provision of QoS to streaming users imposes two major challenges on the HSDPA technology, namely, satisfying the guaranteed bit rate and delay jitter constraints. Focusing on the latter QoS requirement, the operation of the packet scheduling functionality completely determines the node B queuing streaming packets prior to their transmission through the radio interface, and therefore the scheduler plays a key role in the satisfaction of the delay jitter constraint.

Performance of the M-LWDF scheduling algorithm for streaming services in HSDPA

Massive MIMO has emerged as one technology enabler for the next generation mobile communications 5G. The gains promised by massive MIMO are augured to overcome the capacity crunch in today's mobile networks and to pave the way for the ambitious targets of 5G. The challenge to realize massive MIMO for 5G is a successful and cost-efficient integration in the overall network concept. This work highlights deployment and implementation strategies for massive MIMO in the context of 5G indoor small cell scenarios. Different massive MIMO deployment scenarios are analyzed for a standard 3GPP indoor office scenario. In particular stand-alone MIMO at a single location, distributed MIMO without cooperation and network MIMO with full cooperation are investigated for varying array configurations. For the performance analysis of the different MIMO configurations the ratio of total transmit antennas to spatial streams is varied stepwise from equality to a factor of ten. For implementation of massive MIMO in 5G networks trends in beamforming techniques, mutually coupled subarrays, over the calibration procedure and estimated ADC performance in 2020 time-frame are discussed. Based on the debate the paper indicates how to integrate large-scale arrays in future 5G networks.

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Preben Mogensen

Papers

Urban area radio propagation measurements at 955 and 1845 MHz for small and micro cells

Beyond 5G Wireless IRT for Industry 4.0: Design Principles and Spectrum Aspects

On the Potential of OFDM Enhancements as 5G Waveforms

Performance of the M-LWDF scheduling algorithm for streaming services in HSDPA

Deployment and implementation strategies for massive MIMO in 5G