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.

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Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

Slow frequency hopping is a quality and capacity enhancement feature for the GSM system. However, in order to achieve a significant gain from frequency hopping, it has been shown that at least 3 to 4 frequencies are required in the hopping sequence. In band-limited networks that many frequencies per cell are not always available. Two network features, MAIO-management (mobile allocation index offset management) and using a lower reuse with admission load control, compensate for the lack of spectrum even with very narrow bandwidths. The performance of these two features have been investigated and compared to a non-hopping network. A network with a bandwidth of 21 frequencies has been analysed. Simulation results show that MAIO-management gives the best quality. The comparison is based on individually reported frame erasure rates of each mobile station.

Slow frequency hopping solutions for GSM networks of small bandwidth

In this paper, we present a novel iterative receiver for MIMO-OFDM systems with synchronous interferers. The receiver is derived based on the Kullback-Leibler divergence minimization framework, and combines channel estimation, interference cancellation and residual noise estimation in an iterative manner. By using both the pilot and data symbols, the channel estimator improves the accuracy of the estimates in each iteration, which leads to a more effective interference cancellation and data detection process. A performance evaluation based on Monte-Carlo simulations shows that the proposed scheme can effectively mitigate the effect of interferers, and operates very close to the single-user performance even in severe interference scenarios.

Interference Cancellation Based on Divergence Minimization for MIMO-OFDM Receivers

In Frequency Division Duplex (FDD) systems or Time Division Duplex (TDD) systems with perfect Uplink (UL)/Downlink (DL) alignment, all UL transmissions receive interference only from other UL links and vice versa. However, take into consideration one of the most important benefits that TDD offers, i.e., dynamic UL/DL switching points, the UL and DL transmission can be misaligned. This means UL might be corrupted by DL, and vice versa. The loss of network time-synchronization among different cells makes things even more complicated for TDD systems. Many techniques are developed to achieve network synchronization, e.g., GPS based or IEEE 1588 based techniques. In this paper, the effect that different synchronization and UL/DL switching points introduce to the system performance is studied. The purpose is to find out the benefit that synchronization can offer. Based on the observations, it is suggested to achieve at least loose network synchronization.

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Network Time-Synchronization in TDD Based LTE-Advanced Systems

Although the use of cellular networks to serve drones has been investigated in several recent works, the path loss or shadowing variation is still relatively unexplored. The variation and its dynamic behaviour is of importance in characterizing the reliability of the drone communication link, but difficult to assess by experimental means on a large scale. The main goal of this paper is to study the feasibility of ray tracing models to accurately predict shadow fading variations at different heights, so that the shadow fading correlation distances for the UAV channel can be found in both vertical and horizontal directions, without the need of performing extensive field measurement campaigns. For that, predictions obtained through a ray tracing tool are compared to field measurements in an urban scenario. Our results show that with accurate 3D maps, the tool is useful for predicting the dynamics of the UAV propagation channel, and therefore can be used partly as a substitute for field measurements.

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Shadow Fading Spatial Correlation Analysis for Aerial Vehicles: Ray Tracing vs. Measurements

The downlink performance enhancement provided by dual antenna terminals in a WCDMA network is evaluated. A UMTS network is taken as a case study and the behaviour of conventional power based radio resource management algorithms is discussed for both circuit and packet switched services. The aim is to clarify: (i) what the available capacity gain is; (ii) whether it is possible to capture this capacity gain without any higher layer overhead; and (iii) how the capacity is distributed among the users. A theoretical analysis of the dependency of the capacity gain upon the proportion of dual antenna terminals is conducted and the outcome matches the simulation results. Moreover, the impact of soft handover on the performance gain is considered. Furthermore, it is shown that the system becomes hard limited due to lack of channelisation codes, rather than excessive interference.

Preben Mogensen

Papers

Slow frequency hopping solutions for GSM networks of small bandwidth

Interference Cancellation Based on Divergence Minimization for MIMO-OFDM Receivers

Network Time-Synchronization in TDD Based LTE-Advanced Systems

Shadow Fading Spatial Correlation Analysis for Aerial Vehicles: Ray Tracing vs. Measurements

Radio resource management for WCDMA networks supporting dual antenna terminals