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

Adaptive base station antennas also called smart antennas have recently become a hot research topic. In principle the adaptive antenna concept consists of an antenna array and a flexible beamforming network. In terms of commercial exploitation there are mainly two driving factors for introducing adaptive base station antennas, (i) range extension, (ii) capacity enhancement. The issue of range extension is briefly discussed in Section 2, but the main emphasis is put on capacity enhancement of adaptive base station antennas. As sketched in Figure 1, an adaptive antenna array typically creates a narrow beam in the direction of the desired mobile station and thereby reduces the co-channel interference level in other azimuth directions. This is called spatial filtering. There are two methods to exploit a capacity gain from spatial filtering: In the concept of Same Cell Reuse (SCR), the physical radio channels (timeslot, frequency channel) = (t,f) are

Antenna Arrays and Space Division Multiple Access

Ultra-reliable low-latency communications (URLLC) sets high service requirements for the fifth generation (5G) new radio (NR) standard. Grant-free (GF) transmissions is considered a promising technique for reducing the latency in the uplink. To achieve efficient radio resources utilization, sharing of resources is required for sporadic uplink traffic. Repetitions based transmission schemes aims to enhance the reliability of GF transmissions. However, repetitions may also generate excessive interference and cause additional queuing, harming the reliability and latency. In this work, we explore radio resource management (RRM) configurations for repetition based transmission schemes. That includes the number of repetitions, the allocation size per transmission (sub-band), sub-band hopping and uplink power control. Evaluations are conducted in a 5G NR compliant multi-user multi-cell simulation scenario with sporadic uplink GF URLLC transmissions. Our findings suggest that repetitions based schemes can, with a careful selection of the sub-band size and uplink power control parameters, achieve comparable URLLC performance with retransmission based schemes when the effect of queuing is disregarded.

/pdf/system-level-analysis-of-k-repetition-for-uplink-grant-free-1lqyl9eut2.pdf

System Level Analysis of K-Repetition for Uplink Grant-Free URLLC in 5G NR

Networks with different reuses, with and without DTX are compared by using the effective frequency load concept (EFL). EFL makes it possible to compare networks under different circumstances, like for example different weekdays or networks using different configurations. The simulations results show that networks using high frequency reuse factors give the best quality, while the gain from DTX is higher than the gain from power control. Both features can be combined to get an even higher gain. Field trial results show the same tendencies.

Comparison of networks with different frequency reuses, DTX, and power control using the effective frequency load approach

This paper presents the results of wireless channel measurement campaign in the 3 GHz to 8 GHz frequency range. The measurements were performed with focus on the short-range with a transmitter-receiver separation distance less than 9 m in two typical industrial environments: a low clutter density manufacturing space, and a high clutter density one. We analyzed the statistical properties of the most important temporal and large-scale propagation characteristics including total received energy, path loss exponent, maximum excess delay (MED) and root mean square (RMS) delay spread based on the measurements. Statistical models for the RMS delay spread and MED are also presented using the log-normal and Gamma distributions.

/pdf/short-range-uwb-wireless-channel-measurement-in-industrial-4xy7sl8s4n.pdf

Short-Range UWB Wireless Channel Measurement in Industrial Environments

A communication network has a network element adapted to transmit data to a mobile station using at least two carriers. The network element transmits a first set of channels on a first main carrier, and a second set of channels on a second dependent carrier. The first and second set of channels may include a shared downlink channel, but are otherwise different each other.

Preben Mogensen

Papers

Antenna Arrays and Space Division Multiple Access

System Level Analysis of K-Repetition for Uplink Grant-Free URLLC in 5G NR

Comparison of networks with different frequency reuses, DTX, and power control using the effective frequency load approach

Short-Range UWB Wireless Channel Measurement in Industrial Environments

Transmitting information using multiple carriers