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

An other-sector interference model is developed for the downlink of 3G cellular systems. It is solely based on the cell geometry factor and a user’s line of sight angle of connection to a serving base station antenna array. The dependency on just two parameters makes it especially suitable to support a decoupled approach of link and system level simulations, where the link simulations already account for other-sector interference effects. A comparison of the model with a standard 57 sector macrocellular set-up shows that for a 3-sector site deployment, modelling 5 out of 57 sectors with multipath fading characteristics delivers a good trade-off between simulation complexity and other-sector interference modelling accuracy. Keywords— Other-cell interference, other-sector interference, G-factor, quasi-static downlink system simulations.

/pdf/geometry-based-other-sector-interference-modelling-for-3dnjmkdit8.pdf

Geometry Based Other-Sector Interference Modelling for Downlink System Simulations

Full duplex (FD) communication promises significant performance gains under ideal network settings. Generally, it has been shown that the throughput and delay gains of FD communication are somewhat limited in realistic conditions, leading researchers to study other possible applications where significantly higher gains over half duplex communication can be availed. The potential of FD nodes in improving the physical layer security of a communication link is investigated in this contribution. Specifically, we present a thorough analysis of the achievable secrecy rate for a transceiver pair in FD mode in the presence of a passive eavesdropper assuming a generic system model. The ergodic secrecy rate with FD communication is found to grow linearly with the log of the direct channel signal to noise ratio as opposed to the flattened out secrecy rate with conventional half duplex communication and irrespective of the eavesdropper channel strengths.

/pdf/on-the-ergodic-secrecy-capacity-with-full-duplex-hswdoksayx.pdf

On the ergodic secrecy capacity with full duplex communication

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.

Modeling radio link performance in UMTS W-CDMA network simulations

Interference management through power control is essential to optimize the system capacity. With the introduction of aerial user equipments in cellular networks, resulting in an increase of line of sight links, power control is becoming more and more vital to enable the (uplink) high-throughput data streaming and protect the users on the ground. The investigation in [1] shows that in the high signal-to-interference-plus-noise (SINR) regime, geometrical programming (GP) can be used to efficiently and reliably solve the problem. In the low SINR regime, a series of GPs are solved by condensation. However, the condensation method proposed in [1] is non-scalable, which hinders its application to a large-scale network, e.g. a densified network, where many more cells could be jointly optimized. In this communication, by transforming the original problem into a standard form introducing auxiliary variables, a new condensation method is proposed. Its complexity linearly increases with the number of links increasing, which makes the power control practically solvable for both small- and large-scale networks. A case study for the up-link UAV communications in cellular networks is performed using the proposed algorithm.

A Centralized and Scalable Uplink Power Control Algorithm in Low SINR: A Case Study for UAV Communications

This paper study the performance of an orthogonal frequency division multiple access (OFDMA) system under fractional load based in long term evolution (LTE) downlink. Fractional load is defined by a situation where only a fraction of the bandwidth is used for transmission due to lack of traffic. This type of situation should result in a global increase of signal to interference and noise ratio (SINR) conditions in the network. We propose different methods integrating the transmission pattern selection to the packet scheduling functionality of the enode-B depending only on the channel quality indicator (CQI) reports from the user equipments. Fractional load handling is operated without inter eNode-B coordination. We conclude that the system can operate under fractional load conditions if the CQI is calculated with wideband interference. Further gain can be obtained if the CQI is calculated with local interference, however, in that case, it is necessary to inject a certain time correlation in the transmission patterns due to reporting delay.

Preben Mogensen

Papers

Geometry Based Other-Sector Interference Modelling for Downlink System Simulations

On the ergodic secrecy capacity with full duplex communication

Modeling radio link performance in UMTS W-CDMA network simulations

A Centralized and Scalable Uplink Power Control Algorithm in Low SINR: A Case Study for UAV Communications

Integrated Fractional Load and Packet Scheduling for OFDMA Systems