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

This paper presents a measurement-based comparison of cm-wave propagation in urban and suburban scenarios at 24 GHz with transmitter antennas located above rooftop level. Different sets of directional measurements, exploring the full azimuth and the range from −30 to +30 degrees in elevation, were performed with horn antennas located close to street level, in order to explore the spatial characteristics of the channel in both LOS and NLOS conditions. The statistical analysis of different directional indicators shows how, at 24 GHz, outdoor propagation is quite different in the suburban scenario as compared to the urban case. Increased spatial multipath, in average 1.23 times higher, is observed in the suburban scenario, mainly due to the strong presence of vegetation. This results in reduced suburban NLOS path loss exponents (3.4) in comparison to the urban scenario (3.7), as detailed in the outdoor path loss analysis. The paper also highlights the potential of using beam combining techniques in order to improve cell-edge coverage by 17% and 37% in the urban and suburban scenarios, respectively. Outdoor-to-indoor propagation was also investigated, finding an average penetration loss of 6.5 dB for buildings composed of light construction materials. The different results and observations provided in the paper are useful for modeling and simulation of future wireless networks operating at 24 GHz in urban and suburban scenarios.

/pdf/analysis-and-comparison-of-24-ghz-cmwave-radio-propagation-406ynbkox1.pdf

Analysis and comparison of 24 GHz cmWave radio propagation in urban and suburban scenarios

5th generation radio networks should efficiently support services with diverse requirements. For achieving better resource utilization, the sharing of the radio channel between the different services is an attractive solution. While the downlink multiplexing can be well accomplished with dynamic scheduling, efficient multiplexing of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) in uplink is still an open problem. In particular, we consider the case of URLLC using grant-free allocation for sporadic transmissions, multiplexed on shared resources with eMBB with high data volume. Since the moment in which a grant-free transmission occurs is not known, URLLC and eMBB transmissions overlay. Power control settings are then assessed as a way to manage the performance trade-off between the services. Due to the complexity of 5G new radio, the evaluation is based on advanced system level simulations. Insights regarding the configuration of fractional power control settings upon the coexistence of the different services are presented.

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System Level Analysis of eMBB and Grant-Free URLLC Multiplexing in Uplink

Dual connectivity for reliability is considered one of the most important enablers of ultra-reliable low-latency communication in fifth-generation cellular systems. In this paper, we focus on downlink dual connectivity, proposing two methods that prevent unnecessary duplicates transmissions to enhance spectral efficiency when using data duplication. In the first method, the duplicates of packets already successfully delivered to the terminal are promptly dropped from the transmission queues of the involved base stations thanks to an uplink indication provided by terminal itself. In the second method, the transmission of duplicates from the secondary node is enforced only upon a failure of the master node. The performance of the two proposed schemes is evaluated in terms of i) achievable packet reliability within the 1-ms latency target of ultra-reliable low-latency communication, and ii) resource-block utilization via system-level simulation campaigns. The results show that, in the investigated scenario, the second proposal can support a four-times higher offered load with respect to the baseline approach.

Resource-Efficient Dual Connectivity for Ultra-Reliable Low-Latency Communication

A method of allocating resources in a communication system and a station for the communication system is disclosed. The communication system is such that a plurality of user equipment can communicate data on a dedicated channel. In the method a request for communication resources may be sent from a user equipment to the station on a first protocol layer. It may then be detected that priority based resource allocation is required, where after priority information is obtained at the station from a data flow on a second protocol layer. Resource allocation for comunicatioan on the first protocol layer may then at least partially be based on said priority information.

Resource allocation in a communication system

Full duplex communication promises a 100% throughput gain by enabling simultaneous transmission and reception. However, such simultaneous communication leads to a corresponding increase in the network interference. In addition, full duplex communication can only be exploited when traffic is available in both uplink and downlink directions; while, cellular network traffic tend to be downlink heavy in practice. The potential throughput gains of full duplex communication over conventional half duplex transmission in a small cell network with asymmetric traffic conditions are investigated in this contribution using network analysis tools from stochastic geometry. The analytical findings are further confirmed through computer-based Monte-Carlo simulations. Asymmetric downlink/uplink traffic pattern and the increased network interference stemming from full duplex transmissions are found to limit its potential performance to well below the promised 100% throughput gain.

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Preben Mogensen

Papers

Analysis and comparison of 24 GHz cmWave radio propagation in urban and suburban scenarios

System Level Analysis of eMBB and Grant-Free URLLC Multiplexing in Uplink

Resource-Efficient Dual Connectivity for Ultra-Reliable Low-Latency Communication

Resource allocation in a communication system

Throughput Analysis of Full Duplex Communication with Asymmetric Traffic in Small Cell Systems