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

Long Term Evolution (LTE)-Advanced is expected to aggregate multiple Component Carrier (CC)s to fulfil the high data rate requirement. It may serve users with different capabilities in accessing these CCs, e.g., some can access all CCs, whereas some may operate on only one CC. This gives challenges to the packet scheduler to maximize the system performance over all CCs. In this paper we provide a mathematical model of the log-measure utility in an LTE-Advanced system, and give proof that our previously developed cross-CC Proportional Fair (PF) packet scheduler maximizes this utility. System level simulations are performed, which confirm that cross-CC PF scheduling offers much higher utility than independent PF and channel blind schedulers. This scheduler is then generalized to adjust the resource sharing among users. It can trade off between average cell throughput and cell edge user throughput. However, any adjustment in the resource sharing leads to a loss in utility.

Utility Maximization in LTE-Advanced Systems with Carrier Aggregation

This article introduces G-DFT-s-OFDM as a potential 5G waveform candidate. G-DFT-sOFDM replaces the CP with a sequence having a tunable length; this sequence is part of the IFFT output rather than being appended to it. Benefits in terms of flexibility, spectral containment, low latency, and robustness to Doppler spread and phase noise are discussed in the article.

Generalized DFT-Spread-OFDM as 5G Waveform

This paper addresses the interference management problem in the context of LTE-Advanced femtocells. Due to the expected large number of user-deployed cells, centralized network planning becomes increasingly less viable. Consequently, we consider an architecture of autonomous decision makers. Our main contribution in this paper, denominated Generalized Autonomous Component Carrier Selection (G-ACCS), is a distributed carrier-based inter-cell interference coordination scheme that represents one step towards cognitive radio networks. The algorithm relies on expected rather than sensed interference levels. This approach facilitates scheduler-independent decisions, however, it can lead to overestimation of the interference coupling among cells when the resources are not fully utilized. Acknowledging this fact, G-ACCS leverages the power domain to circumvent the restrictive nature of expected interference coupling. This work focuses on the downlink and also provides an extensive characterization of the network performance as a function of the topology as well as the often overlooked temporal traits of traffic. We compare G-ACCS with other carrier-based solutions, including the simplest universal reuse strategy. The encouraging simulation results demonstrate that G-ACCS achieves an efficient and fair distribution of resources in all considered traffic and deployment conditions. More importantly, this is attained in a truly autonomous fashion, without any explicit parametrization.

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Autonomous Component Carrier Selection for 4G Femtocells — A Fresh Look at an Old Problem

Beyond 4th Generation (B4G) local area networks will be characterized by the dense uncoordinated deployment of small cells. This paper shows that inter-cell interference, which is a main limiting factor in such networks, can be effectively contained using Interference Rejection Combining (IRC). By simulation we investigate two significantly different interference scenarios with dense small cell deployment. The results show that IRC brings considerable improvement in outage as well as in peak and median throughputs in both scenarios, and thus has a big potential as a capacity and coverage enhancing technique for B4G. The IRC gain mechanism depends strongly on the interference scenario and to some extent on the use of frequency reuse. These results are achieved with no coordination between cells and suggests that MIMO rank adaptation and IRC can be done independently.

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On the Potential of Interference Rejection Combining in B4G Networks

Aerial users, such as unmanned aerial vehicles (UAVs), experience different radio propagation conditions than users on the ground. This is a concern regarding the integration of such users into cellular networks in the near future. This letter investigates the impact of uplink transmissions from an aerial user equipment. Full buffer transmissions were performed by a device at ground level and also flying attached to a UAV at 100 m height. The field measurements show a higher number of cells affected by the aerial transmission, with an increase of up to 7.7 dB in the interference over thermal noise in cells within 15 km of the test location. This letter also assesses two strategies to reduce the uplink interference caused by aerial users: 1) UAV’s cruise height control and 2) directional transmissions. Results show the directional transmission is a more promising technique, and has the advantage of not reducing the uplink received power.

/pdf/measured-uplink-interference-caused-by-aerial-vehicles-in-361136cx6s.pdf

Preben Mogensen

Papers

Utility Maximization in LTE-Advanced Systems with Carrier Aggregation

Generalized DFT-Spread-OFDM as 5G Waveform

Autonomous Component Carrier Selection for 4G Femtocells — A Fresh Look at an Old Problem

On the Potential of Interference Rejection Combining in B4G Networks

Measured Uplink Interference Caused by Aerial Vehicles in LTE Cellular Networks