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

A multiple component carrier (CC) solution has been agreed as the underlying structure of the Long Term Evolution - Advanced (LTE-A) systems, which are currently being standardized by the 3rd Generation Partnership Project (3GPP). In this deployment, the bundling of Modulation and Coding Scheme (MCS) and Hybrid Automatic Repeat Request (HARQ) parameters across multiple CCs is foreseen to maintain the feedback signaling comparable with the one of the previous LTE Release 8. This paper focuses on the transmission over multiple CCs in LTE-A Uplink. Issues related to the bundling of the link level parameters is discussed and a simple Codeword (CW) mixing strategy is proposed to boost the spectral efficiency performance while keeping low feedback overhead. Results show that, combined HARQ/MCS bundling can achieve the same performance of HARQ only bundling when used together with our proposed CW mixing strategy, with the advantage of a lower feedback overhead. CW mixing also leads to similar spectral efficiency when the bundling is performed over 2 and 3 CCs. The use of a turbo Successive Interference Cancellation (turbo SIC) receiver further improves the spectral efficiency, especially when antenna gain imbalance (AGI) occurs.

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Link Parameters Bundling across Multiple Component Carriers in LTE-A Uplink

Time synchronization in a large network of small cells enables efficient interference management as well as advanced transmission techniques which can boost the network throughput. In this paper, we focus on the distributed initial synchronization problem and propose different solutions aiming at reducing the residual asynchronous interference while limiting the number of large timing reconfigurations at each node. Simulation results show the effectiveness of an ID-based approach in limiting the asynchronous interference, however at the expense of a potential high number of timing reconfigurations. A pathlossbased solution limits such reconfigurations but also leads to a higher residual interference than the ID-based solution. An hybrid solution is shown to be an effective trade-off between the two former approaches.

Distributed Initial Synchronization for 5G Small Cells

Spatial channel characterization of a cellular Unmanned Aerial Vehicle (UAV) Air-to-Ground (AG) communication link is a vital step to understand the potential of beam-forming in the take-off zone, when a UAV flies in the vicinity of other objects. In this paper, we evaluate the variation of mean Angle of Arrival (AoA) and Angular Spread (AS) with height based on the experimental measurements using live Long Term Evolution (LTE) networks. The LTE signals are recorded at different heights from a ground level up to 40 m in rural and urban environments. Space-Alternating Generalized Expectation-Maximization (SAGE) algorithm is used for the estimation of the angular parameters. Results show similar mean AoA at different heights, with less than 55 degrees deviation in urban environment and no more than 20 degrees change in rural scenarios. Observed AS is reduced to less than 30 degrees at increasing heights as the Line of Sight (LoS) propagation becomes dominant. However the comparison between urban and rural environments clearly indicates the presence of relevant multipath components in the urban scenarios even 20 m above the rooftops level.

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Angular Distribution of Cellular Signals for UAVs in Urban and Rural Scenarios

While operators have to upgrade the capacity of their networks, they have committed themselves to reduce their CO2 emissions, partly by reducing their energy consumption. This article investigates the challenges faced by operators and quantifies, through a number of case studies, the impact of specific solutions and how the energy consumption trend can be expected to develop over the next decade. With different options for upgrading capacity, studies show that a hybrid macro-pico upgrade is more energy-efficient than a macro or pico only solution. The study is extended further by quantifying the possible savings by adopting an energy-efficient capacity evolution together with an equipment replacement and site upgrade strategy. Results show that network operators can get relatively close to their targets, with energy reductions of up to 40% noted. While this can be improved further through software-based energy saving features, further CO2 emissions can be offset through the use of carbon-neutral energy sources.

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Mobile operators have set ambitious targets – Is it possible to boost network capacity while reducing its energy consumption?

A device includes circuitry adapted to place at least a portion of at least one common channel on a fraction of an available bandwidth of a cell in a cellular communication system that uses soft reuse such that different orthogonal transmission resources are transmitted with different transmission powers and power usage is planned on a cell-by-cell basis; and a transmitter to transmit the common channel into the cell for reception by a plurality of receivers. Also disclosed is a method and a computer program product operable with the device.

Preben Mogensen

Papers

Link Parameters Bundling across Multiple Component Carriers in LTE-A Uplink

Distributed Initial Synchronization for 5G Small Cells

Angular Distribution of Cellular Signals for UAVs in Urban and Rural Scenarios

Mobile operators have set ambitious targets – Is it possible to boost network capacity while reducing its energy consumption?

Apparatus, method and computer program product providing common channel arrangements for soft frequency reuse