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

The recent introduction of carrier aggregation in LTE-Advanced enables new possibilities in designing frequency domain interference reduction and management schemes. These methodologies are of extreme interest in the case of dense and uncoordinated deployments of femtocells. In such scenarios, dense deployment of cells coupled with the scarcity of frequency resources may lead to a potentially disruptive amount of interference, which severely affects the performance of the system. This contribution presents a novel method inspired by graph and coalitional game theories. The proposed algorithm consists of a set of distributed and scalable rules for building coalitions; these rules essentially resolve the conflicts among avid femtocells competing for a limited amount of resources. The proposed scheme has been designed by targeting localized reconfigurations, thus avoiding reconfiguration storms in the network. Furthermore, the rules governing the resource redistribution ensure overall system performance improvements while maintaining a certain degree of fairness among the competing nodes. Simulation results prove the effectiveness of the proposed method.

Self-Organizing Coalitions for Conflict Evaluation and Resolution in Femtocells

5th Generation (5G) radio access technology aims at supporting services with different latency requirements in the same air interface. The usage of an early Hybrid Automatic Repeat reQuest (HARQ) feedback based on a prediction of the decoder's outcome allows reducing the over-the-air latency, with particular advantages for mission critical applications. However, predicting the ACK/NACK feedback before detection occurs is unfortunately error prone. In this paper, we discuss the potential of using an early HARQ feedback in 5G networks considering the occurrence of wrong predictions. Two options are studied: only the early feedback is transmitted, and both early and regular feedback are transmitted, with the possibility of the latter one of correcting wrong early predictions. Numerical analysis shows the potential of the early feedback of reducing the over-the-air latency up to 50%. Wrong estimates can, however, significantly affect outage rate and latency for the case of only early feedback, while their impact is very minor in case the regular feedback is empowered to correct them.

On the benefits of early HARQ feedback with non-ideal prediction in 5G networks

This paper describes a method to increase the capacity of a digital cellular network, like GSM, without having to increase the number of available frequencies or base stations. The idea is to split the frequency spectrum into two bands. One consisting of frequencies that can only be used when a high C/I ratio is ensured, the super frequencies, and the other consisting of frequencies that can be used throughout the whole cell, the regular frequencies. The minimum frequency reuse distance of the super frequencies can therefore be smaller than the reuse distance of the regular layer frequencies. By combining this reuse partitioning with frequency hopping, an increase in the network capacity in terms of carried traffic per cell is achieved. Simulations have indicated that for slow moving mobiles a gain of approximately 35% is achieved by this new feature when compared with a frequency hopping network. For faster moving mobiles the simulated gain is lower, approximately 26%. Modifications are proposed to optimise the simulated algorithms, to improve the capacity in terms of carried traffic per cell even further.

On the capacity of a GSM frequency hopping network with intelligent underlay-overlay

The theoretical BER is calculated for downlink in UMTS including interpath interference and multiple access interference. The analog interface to the digital receiver is analyzed and an architecture including AGC and ADC is suggested. It is further analyzed that an ADC with 4-5 bit and sampling of 4 times the chip rate will give an acceptable performance within 0.2 dB from the theoretical performance.

Performance analysis for UMTS downlink receiver with practical aspects

The ambitious data rate target of the Long Term Evolution - Advanced (LTE-A) systems, which are currently being standardized by the 3rd Generation Partnership Project (3GPP), can only be achieved by using advanced Multiple Input Multiple Output (MIMO) antenna techniques on both uplink and downlink. In this paper, the focus is on LTE-A uplink and we discuss the suitability of two options for the closed loop precoded MIMO transmission. The first option is to use the same codebook of precoding matrices defined for LTE downlink, while the second option exploits the Singular Value Decomposition (SVD) of the channel matrix, which could theoretically achieve the MIMO capacity. Qualitative benefits of both solutions are discussed. Link level simulation results show that, the SVD-based approach only lead to a poor performance gain in terms of spectral efficiency over the LTE downlink codebook because of the losses due to the quantization of the right singular vectors of the channel matrix. Since SVD-based solution doesn't even exploit some useful properties (e.g., power balance over the antennas) which LTE Release 8 codebook offers, the latter results to be a better candidate for the closed loop MIMO transmission in LTE-A uplink.

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Preben Mogensen

Papers

Self-Organizing Coalitions for Conflict Evaluation and Resolution in Femtocells

On the benefits of early HARQ feedback with non-ideal prediction in 5G networks

On the capacity of a GSM frequency hopping network with intelligent underlay-overlay

Performance analysis for UMTS downlink receiver with practical aspects

SVD-Based vs. Release 8 Codebooks for Single User MIMO LTE-A Uplink