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

UTRAN long term evolution is currently under standardization within 3GPP with the aim of providing a spectral efficiency 2 to 4 times higher than its predecessor HSUPA/HSDPA release 6. Single carrier FDMA has been selected as multiple access for the uplink. This technology requires the subcarriers allocated to a single user to be adjacent. The consequence is a reduced allocation flexibility which makes it challenging to design effective packet scheduling algorithms. This paper proposes a channel aware packet scheduling algorithm which exploits the bandwidth flexibility offered by the system to perform an allocation which closely resembles the frequency domain envelope of the metric to be optimized. Compared to a fixed bandwidth approach, the proposed algorithm provides a greater flexibility given the inbuilt adaptation to different scenarios and loads, as well as an improvement in term of performance for the Macro 3 case. In this case the uplink capacity is increased by approximately 20% in average cell throughput and 10% in UE outage compared to a fixed bandwidth channel aware approach.

Adaptive Transmission Bandwidth Based Packet Scheduling for LTE Uplink

Turbo equalization is known as an advanced iterative equalization and decoding technique that allows to enhance the performance of the data transmission over a frequency selective fading channel. The turbo equalizer will result in extra receiver complexity, but isolated to the base-station, which does not have stringent power constraints. In this paper, a turbo equalization technique to improve Single Carrier Frequency Division Multiple Access (SC-FDMA) performance is proposed. A new adaptive coefficients solution for frequency domain equalization is considered. The work is in the context of UTRA Long Term Evolution (LTE) Uplink. The performance is evaluated for 1x2 Single Input Multiple Output (SIMO) antenna configuration in a 6 paths Typical Urban (TU-06) channel profile. For assessment purpose, the results are compared with SC-FDMA MMSE and OFDMA schemes. Simulation results show that the turbo equalizer can improve the BLER performance around 1 dB with only a few iterations, and improve the SC-FDMA performance over OFDM, especially at high coding rate.

/pdf/improving-sc-fdma-performance-by-turbo-equalization-in-utra-3wfnqnkm32.pdf

Improving SC-FDMA Performance by Turbo Equalization in UTRA LTE Uplink

UTRAN long term evolution is currently under standardization within 3GPP with the aim of providing a spectral efficiency 2 to 4 times higher than its predecessor HSUPA/HSDPA release 6. Single carrier FDMA has been selected as multiple access for the uplink. This technology requires the subcarriers allocated to a single user to be adjacent. The consequence is a reduced allocation flexibility which makes it challenging to design effective packet scheduling algorithms. This paper provides a search-tree based channel aware packet scheduling algorithm and evaluates its performance in terms of throughput and noise rise distributions. It is shown that, despite measurement errors and high inter-cell interference variability, the proposed algorithm can increase the uplink capacity by 24% for the macro 1 scenario and 19% for the macro 3 scenario.

Search-Tree Based Uplink Channel Aware Packet Scheduling for UTRAN LTE

Smartphone users struggle with short battery life, and this affects their device satisfaction level and usage of the network. To evaluate how chipset manufacturers and mobile network operators can improve the battery life, we propose a Long Term Evolution (LTE) smartphone power model. The idea is to provide a model that makes it possible to evaluate the effect of different terminal and network settings to the overall user equipment energy consumption. It is primarily intended as an instrument for the network engineers in deciding on optimal network settings, but could also be beneficial for chipset manufacturers to identify main power consumers when taking actual operating characteristics into account. The smartphone power consumption model includes the main power consumers in the cellular subsystem as a function of receive and transmit power and data rate, and is fitted to empirical power consumption measurements made on state-of-the-art LTE smartphones. Discontinuous Reception (DRX) sleep mode is also modeled, because it is one of the most effective methods to improve smartphone battery life.

/pdf/an-empirical-lte-smartphone-power-model-with-a-view-to-530hmx18n2.pdf

An Empirical LTE Smartphone Power Model with a View to Energy Efficiency Evolution

This paper considers the influence of the cross-correlated shadowing between base-stations on network level simulations. A method to generate correlated shadow fading processes is proposed, which makes it possible to study position dependent correlation models between multiple base stations (BS). The method has been used for static network level simulations with different synthetic correlation models, both angle-of-arrival- and distance-dependent, in order to study the C/I outage gain for different frequency reuse patterns and BS sectorizations.

Preben Mogensen

Papers

Adaptive Transmission Bandwidth Based Packet Scheduling for LTE Uplink

Improving SC-FDMA Performance by Turbo Equalization in UTRA LTE Uplink

Search-Tree Based Uplink Channel Aware Packet Scheduling for UTRAN LTE

An Empirical LTE Smartphone Power Model with a View to Energy Efficiency Evolution

Modelling cross-correlated shadowing in network simulations