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

Various communication systems may benefit from an improved signaling protocol For example, machine type communications may benefit from improved scheduling signaling A method includes receiving at a user equipment a signal comprising a radio configuration time trace from a base station The radio configuration time trace parameter defines future, time-limited radio configuration options for a user equipment The method also includes determining at the user equipment a radio access action based on the received radio configuration options In addition, the method includes using the radio configuration options to carry out the determined radio access action

Radio configuration for machine type communications

Unique Word Discrete Fourier Transform -spread - Orthogonal Frequency Division Multiplexing (UW DFT-s-OFDM) waveform replaces the Cyclic Prefix (CP) with a known sequence which is contained in the symbol itself rather than being appended to it. In this paper, we propose a new UW DFT-s-OFDM design which aims at exploiting such known sequence at the tail of the signal for tracking the variations of the radio channels on a symbol basis, thus overcoming the limited flexibility of DFT-s-OFDM waveforms in multiplexing reference sequences and data. Further, we also present a criterion for efficiently selecting the reference sequences to be adopted by users experiencing fast time-varying channel conditions. Initial simulation results show the capability of the proposed design of improving the link performance of high speed users, even without additional overhead.

/pdf/unique-word-dft-spread-ofdm-for-fast-time-varying-channels-15cjp514dq.pdf

Unique Word DFT-Spread-OFDM for Fast Time-Varying Channels

In Direct Device-to-Device (D2D), the device awareness procedure known as the discovery phase is required prior to the exchange of data. This work considers autonomous devices where the infrastructure is not involved in the discovery procedure. Commonly, the transmission of the discovery message is done according to a fixed probability. However, this configuration may be not appropriate to meet the 10 ms control plane latency target defined for the next $5^\mathrm{th}$ generation (5G) system. In this work, we propose a distributed radio resource management framework supporting full duplex technology to provide D2D fast discovery. Such framework provides an algorithm to estimate the number of neighbor devices and to dynamically decide the transmission probability, for adapting to network changes and meeting the 10 ms target. Finally, a signaling scheme is proposed to reduce the network interference. Results show that our framework performs better than a static approach, reducing the time it takes to complete the discovery phase. In addition, supporting full duplex allows to further reduce the discovery time compared to half duplex transmission mode.

/pdf/providing-fast-discovery-in-d2d-communication-with-full-1vjrpfv7mv.pdf

Providing Fast Discovery in D2D Communication with Full Duplex Technology

Robotic swarms are becoming relevant across different industries. In an indoor factory, collective perception of the environment can be used for increased factory automatization. It requires reliable, high throughput and low latency communication of broadcasted video data among robots within proximity. We introduce two new decentralized resource allocation schemes that meet these stringent requirements. The two proposed decentralized schemes are denoted as: (i) device sequential, where robots take turns to allocate resources, and (ii) group scheduling, where robots select local group leaders who perform the resource allocation. A comparative evaluation is performed by simulation against a centralized resource allocation scheme and the current 3GPP release 16 NR sidelink mode 2 scheme. Our results show that the two proposed decentralized resource allocation schemes outperform sidelink mode 2 due to the mitigation of the half-duplex problem. The proposed schemes reach the throughput target of 10 Mbps with a reliability of 99.99% for a swarm size of 50 robots.

Cooperative Resource Allocation for Proximity Communication in Robotic Swarms in an Indoor Factory

This paper presents our vision and initial design of a wireless manufacturing execution system (MES) solution, which will be soon integrated in the fully-automated small production line in the Smart Production Lab at Aalborg University. The replacement of the current Ethernet-based control system with our wireless solution, will allow to remove all communication wires between the different stations of the production line and thus, enabling a faster re-configuration of the production facilities. The proposed solution also sets the base for the future integration of new industrial use cases requiring full mobility support.

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Preben Mogensen

Papers

Radio configuration for machine type communications

Unique Word DFT-Spread-OFDM for Fast Time-Varying Channels

Providing Fast Discovery in D2D Communication with Full Duplex Technology

Cooperative Resource Allocation for Proximity Communication in Robotic Swarms in an Indoor Factory

On the Design of a Wireless MES Solution for the Factories of the Future