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].

/pdf/scaling-up-mimo-opportunities-and-challenges-with-very-large-puqp9v7gzk.pdf

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

This article studies the impact on the design of scheduling algorithms for Orthogonal Frequency Division Multiple Access (OFDMA) systems of two traffic models described in the evaluation methodology proposals from standardization bodies: the full buffer and the finite buffer traffic models. The analysis concentrates on utility-based scheduling with an α-fair utility function for Non-Real Time (NRT) services. The results show that a gradient scheduling algorithm is able to maximize the aggregate utility over all the users when the less realistic full buffer model is adopted; but not when the finite buffer model is applied. The results also show that with the full buffer model a gradient scheduler exhibits a trade-off between average user throughput and the user throughput at 5% outage, but it does not when the more realistic finite buffer is used. Therefore, it is concluded that designs of scheduling algorithms for NRT services for OFDMA systems carried out under the full buffer model assumption may fail to provide the desired performance benefits in realistic scenarios. Based on the results presented, a recommendation on scheduling design is provided.

/pdf/traffic-models-impact-on-ofdma-scheduling-design-1ae0k4t1io.pdf

Traffic models impact on OFDMA scheduling design

In this paper we study the downlink latency performance in a multi-user cellular network. We use a flexible 5G radio frame structure, where the TTI size is configurable on a peruser basis according to their specific service requirements. Results show that at low system loads using a short TTI (e.g. 0.25 ms) is an attractive solution to achieve low latency communications (LLC). The main benefits come from the low transmission delay required to transmit the payloads. However, as the load increases, longer TTI configurations with lower relative control overhead (and therefore higher spectral efficiency) provide better performance as these better cope with the non-negligible queuing delay. The presented results allow to conclude that support for scheduling with different TTI sizes is important for LLC and should be included in the future 5G.

/pdf/on-the-impact-of-multi-user-traffic-dynamics-on-low-latency-3i12vdec72.pdf

On the impact of multi-user traffic dynamics on low latency communications

The continuous proliferation of applications requiring wireless connectivity will eventually result in latency and reliability requirements beyond what is achievable with current technologies. Such applications can for example include industrial control at the sensor-actuator level, intra-vehicle communication, fast closed loop control in intra-body networks and intra-avionics communication. In this article, we present the design of short range Wireless Isochronous Real Time (WIRT) in-X subnetworks aimed at life-critical applications with communication cycles shorter than 0.1 ms and outage probability below 10
 -6
. Such targets are clearly beyond what is supported by the 5th Generation (5G) radio technology, and position WIRT as a possible 6th Generation (6G) system. WIRT subnetworks are envisioned to be deployed for instance in industrial production modules, robots, or inside vehicles. We identify technology components as well as spectrum bands for WIRT subnetworks and present major design aspects including frame structure and transmission techniques. The performance evaluation considering a dense scenario with up to 2 devices per m
 2
 reveal that a multi-GHz spectrum may be required for ensuring high spatial service availability. The possibility of running WIRT as an ultra-wideband underlay system in the centimeter-wave spectrum region is also discussed. Aspects related to design of techniques for the control plane as well as enhancements to the presented design is the focus of our ongoing research.

/pdf/towards-6g-in-x-subnetworks-with-sub-millisecond-3p0rc41nba.pdf

Towards 6G in-X Subnetworks With Sub-Millisecond Communication Cycles and Extreme Reliability

The 38 GHz mm-wave frequency band is a strong candidate for the future deployment of wireless systems. Compared to lower frequency bands, propagation in the 38 GHz band is relatively unexplored for access networks in urban scenarios. This paper presents a detailed measurement-based analysis of urban outdoor and outdoor-to-indoor propagation characteristics at 38 GHz. Different sets of measurements were performed in order to understand, quantify and model the behavior of the different underlying propagation mechanisms. The study considers line-of-sight propagation, reflection, scattering, diffraction, transmission, as well as polarization effects. The measurement results confirm that, at this particular frequency, propagation in urban scenarios is mainly driven by line-of-sight and reflection. The proposed models are practical for implementation in system level simulators or ray-tracing tools. The different observations presented along the paper are useful for future radio network planning considerations.

/pdf/analysis-of-38-ghz-mmwave-propagation-characteristics-of-1umn2nl14q.pdf

Analysis of 38 GHz mmWave Propagation Characteristics of Urban Scenarios

In evolving 3G systems, the proportional fair packet scheduling algorithm promises an attractive trade-off between average cell capacity and user fairness by exploiting multi-user selection diversity in time-shared channels. However, in these systems the option of antenna transmit diversity already exist, whose performance benefit is dependent on the considered scheduling strategy. This study focuses on the interaction between proportional fair packet scheduling and open or closed-loop transmit diversity techniques. The basic interaction is studied by using an analytical model. Additionally, Monte Carlo simulations are conducted to study the effect of scheduling delays. In the low mobility Rayleigh fading case, the potential average cell capacity gain with proportional fair scheduling, compared to a simple round robin in time approach, is on the order of 60% for single-antenna transmission. It reduces, however, to around 35% when dual antenna transmit diversity is additionally deployed. Comparing the performance of the different transmit diversity schemes to single-antenna transmission, conditioned on proportional fair scheduling, it is found that the closed-loop schemes provide a benefit over a wide range of terminal speeds, whereas open-loop space-time block coding exhibits limited performance and in some cases even a loss.

Preben Mogensen

Papers

Traffic models impact on OFDMA scheduling design

On the impact of multi-user traffic dynamics on low latency communications

Towards 6G in-X Subnetworks With Sub-Millisecond Communication Cycles and Extreme Reliability

Analysis of 38 GHz mmWave Propagation Characteristics of Urban Scenarios

Interaction of transmit diversity and proportional fair scheduling