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

Previous studies show a high potential capacity gain for uplink synchronous WCDMA systems in low dispersive environments when users in the same cell are separated by orthogonal codes. However, the number of available orthogonal codes tends to limit the capacity gain of this scheme. In this paper, variable modulation and coding is introduced as a solution to improve the channelization code utilization per user equipment and thereby increase the capacity of uplink synchronous WCDMA. The radio resource management algorithms to support this scheme are presented. Theoretical and simulation results on the capacity gain of uplink synchronous WCDMA combined with variable modulation and coding are presented. A 29% capacity gain is obtained for a pedestrian micro-cell environment.

Uplink synchronous WCDMA combined with variable modulation and coding

With an expected broadband traffic volume growth of 1000x in the coming years, the need to provide coverage and capacity to the high rise buildings scenarios becomes a challenge for operators. Due to the difficulties in deploying their networks inside buildings, an outdoor alternative is needed. This paper investigates the usage of Long Term Evolution-Advanced (LTE-A) uptilted macros pointing to the upper floors, as an outdoor solution to provide coverage and capacity to high rise buildings. This solution, in combination with the existing downtilted macro and street level micro networks, is able to provide a minimum data rate of 10Mbit/s to 95\% of the traffic beyond 2020.

Uptilted Macros as an Outdoor Solution for Indoor Users in High Rise Buildings

This paper aims at evaluating different mechanisms for providing Inter-Frequency (IF) Load Balancing (LB) in multi-layer heterogeneous deployments. More specifically, the performance of IF mobility management based on signal quality measurements is compared against a load-dependent Traffic Steering (TS) framework that triggers IF mobility events only if load imbalance is detected. To evaluate the joint interaction of the aforementioned schemes with more advanced LB features, system level simulations have been conducted with and without Carrier Aggregation (CA) capable users. Results have shown that although quality-based handoff procedures can act as a passive TS mechanism, they are costly in handovers and measurements gaps. The developed TS scheme utilizes cell neighbor measurements more efficiently, achieving significant handover reduction. Finally, CA makes the proposed framework even more attractive, since its careful parameterization becomes less relevant and load imbalances can be tackled by the packet scheduler as well.

/pdf/load-based-traffic-steering-in-multi-layer-scenarios-case-2q3f2prbso.pdf

Load-based traffic steering in multi-layer scenarios: Case with & without carrier aggregation

Impact of Channel Quality Signalling on Frequency-Domain Link Adaptation Performance

Integrating Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) is crucial to providing fifth generation (5G) ubiquitous coverage and exploiting the potential of connectivity-based solutions. This article presents an initial empirical evaluation of both broadband satellite and terrestrial cellular connectivity solutions. End-to-end latency, Packet Error Rate (PER), and uplink (UL) and downlink (DL) throughput statistics are obtained using a SpaceX Starlink satellite antenna terminal connected to the Starlink satellite network and a multiband 5G New Radio (NR) modem connected over a public 5G Non-Standalone (NSA) network. Furthermore, we study how the maximum end-to-end latency can be reduced by using multi-connectivity between terrestrial and LEO satellite networks. Results suggest that the integration of terrestrial and NTN is a competitive solution to fill the existing coverage gaps and provide seamless service to low-latency and high throughput requiring applications.

Preben Mogensen

Papers

Uplink synchronous WCDMA combined with variable modulation and coding

Uptilted Macros as an Outdoor Solution for Indoor Users in High Rise Buildings

Load-based traffic steering in multi-layer scenarios: Case with & without carrier aggregation

Impact of Channel Quality Signalling on Frequency-Domain Link Adaptation Performance

An Empirical Analysis of Multi-Connectivity between 5G Terrestrial and LEO Satellite Networks