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

5G is targeting a peak data rate in the order of 10 Gb/s and at least 100 Mb/s data rate is generally expected to be available everywhere. For fulfilling such 5G broadband targets, massive deployment of small cells is considered as one of the promising solutions. However, inter-cell interference leads to significant limitations on the network throughput in such deployments. In addition, network densification introduces difficulty in network deployment. This paper presents a study on the benefits of advanced receiver in a practical uncoordinated dense small cells deployment. Our aim is to show that advanced receivers can alleviate the need for detailed cell planning. To this end we adopt a hybrid simulation evaluation approach where propagation data are obtained from experimental analysis, and by which we analyse how MIMO constellation and network size impacts to the aim. The experimental data have been obtained using a software defined radio (SDR) testbed network with 12 testbed nodes, configured as either access point or user equipment. Each node features a $4 \times 4$ or a $2 \times 2$ MIMO configuration. The results demonstrate that advanced receivers with a larger MIMO antenna configuration significantly improves the throughput performance in a practical dense small cells network due to the interference suppression capability. In addition, the results prove that the operators can rely on uncoordinated deployment of small cells, since the resulting interference can be suppressed by the advanced receiver processing with sufficiently capable MIMO antenna configuration.

An Experimental Study of Advanced Receivers in a Practical Dense Small Cells Network

Uplink Single-User MIMO Study for LTE-A: Performance Evaluation of OFDMA vs. SC-FDMA

Link level SINR simulation results and network level sector throughput simulation results that quantify the benefit of dual antenna MMSE reception in a macrocellular HSDPA system are provided. The dual antenna RAKE receiver serves as baseline reference. Link-level simulation results are confirmed by a novel analytical expression that in flat Rayleigh fading and for uncorrelated rx antenna branches provides spatial interference suppression mean SINR gain as a function of the dominant other-sector interference ratio (DIR). It is shown that the MMSE receiver’s spatial interference suppression gain depends heavily on the amount of experienced DIR. The higher the DIR the higher is the SINR gain. Nevertheless, seen on network level SINR gain turns into moderate sector throughput gain, well below 50%. This is due to the fact that high DIR situations are rare in the investigated macrocellular scenario. Moreover, dynamic range limitations hinder translation of the full SINR gain into sector throughput.

Effects of Dominant Other-Sector Interference on Multi-Antenna HSDPA Performance

The use of millimeter wave communication for replacing wires in industrial environments has received much interest as part of the Industry 4.0 revolution. MM-wave communication may be used to have manufacturing entities communicate device to device in a decentralized private communication network, e.g. for letting autonomous mobile robots cooperate for improved efficiency and speed. Our objective in this paper is to understand how the device to device channel may be different from the characteristics reported in the literature on the infrastructure-based communication channel, as well as between cm- and near mm-wave. By transmitting and measuring a 5G synchronization signal burst, time-synchronized with the position of two moving robots, we characterize the large- and small-scale propagation characteristics as a function of the line of sight distance within a cluttered manufacturing space. Overall, we find only small differences between 3.8 and 26.4 GHz, however, with a clear indication that propagation at 26.4 GHz is more dominated by the geometric line of sight condition. For distances up to 35 m, path loss characteristics at the two frequencies are almost identical, close to free space propagation, and with shadowing decorrelation according to the (Gaussian) exponential decay model.

Comparison of cm- and mm-Wave Channel Characteristics between Autonomous Mobile Robots in a Small I4.0 Manufacturing Facility

Dynamic time division duplexing (TDD) is one of the major novelties of the 5G new radio standard. It notably improves the network resource utilization with sporadic directional packet arrivals. Although, the feasibility of the ultra-reliable and low-latency communications (URLLC) within such deployments is critically challenged, mainly due to the cross-link interference (CLI). In this work, we propose a semi-static and computationally-efficient TDD radio frame adaptation algorithm for 5G macro deployments. Particularly, we first identify the quasi-static variance of the cross-cell traffic buffering performance, with various CLI co-existence conditions. Accordingly, a common radio frame pattern is dynamically estimated based on the filtered multi-cell traffic statistics. Our system-level simulation results show that the proposed solution achieves a highly improved URLLC outage performance, i.e., offering 40% reduction gain of the achievable URLLC outage latency compared to perfect static-TDD, and approaching the optimal interference-free flexible-TDD case; though, with a significantly lower control overhead size.

Preben Mogensen

Papers

An Experimental Study of Advanced Receivers in a Practical Dense Small Cells Network

Uplink Single-User MIMO Study for LTE-A: Performance Evaluation of OFDMA vs. SC-FDMA

Effects of Dominant Other-Sector Interference on Multi-Antenna HSDPA Performance

Comparison of cm- and mm-Wave Channel Characteristics between Autonomous Mobile Robots in a Small I4.0 Manufacturing Facility

Semi-Static Radio Frame Configuration for URLLC Deployments in 5G Macro TDD Networks