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.

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Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

The development of non-terrestrial networks (NTN) aims to satisfy the requirements of ubiquitous and seamless coverage for fifth-generation (5G) services. Low Earth orbit (LEO) satellites introduce challenging mobility requirements to the 5G New Radio (NR) radio resource management procedures. Current research shows that the baseline 5G NR UE-assisted network-controlled handover (HO) procedure, meant for terrestrial scenarios, fails to ensure continuous and satisfactory service in LEO-based NTN. The conditional handover (CHO), specified by 3GPP in Rel-16, was designed for terrestrial networks to enhance mobility robustness by making HO decisions earlier. In this work, we conduct system-level simulations to evaluate the 5G NR mobility performance of the CHO in a LEO-based NTN with Earth-moving cells scenario. The simulation results show that the CHO procedure reduces the radio link failures and handover failures to 0 but increases the unnecessary HO rate by more than 60% in comparison with the baseline HO, which leads to an increase of the signalling and measurement reporting. Finally, future research directions are identified to address the increase of the signalling overhead by exploiting the predictability of the satellite’s movement.

Performance Evaluation of the 5G NR Conditional Handover in LEO-based Non-Terrestrial Networks

In this paper, we investigate the suitability of the propagation models ITU-R 526, Okumura Hata, COST Hata models and Standard Propagation Model (SPM) to predict the path loss in open-pit mines. The models are evaluated by comparing the predicted data with measurements obtained in two operational iron-ore mining complexes in Brazil. Additionally, a simple deterministic model, based on the inclusion of an effective antenna height term to the ITU-R 526, is proposed and compared to the other methods. The results show that the proposed model results in root-mean-square error (RMSE) values between 5.5 dB and 9.2 dB, and it is capable of providing a close approximation of the best predictions (i.e. those with lowest RMSE) as provided by the SPM. The proposed model, however, reduces the calibration complexity considerably.

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An Empirical Study of Propagation Models for Wireless Communications in Open-Pit Mines

This paper presents the performance of adaptive antennas in a 1/3 reuse frequency hopping GSM network using conventional beamforming. It mainly focuses on C/I improvement for the purpose of capacity enhancement. The performance evaluation has been conducted by means of network computer simulations, where measured time-space radio channel impulse responses are applied for the desired user in the network. Measurements with an M = 8 element uniform linear array were conducted in the cities of Aarhus, Denmark, and Stockholm, Sweden. The simulated C/I improvement shows an almost 10 a log10(M) behavior for low azimuth spread values. For large values of azimuth spread (relative to the antenna beamwidth), the performance gain is reduced significantly. For an azimuth spread of 10°–12°, which has been measured in urban macro-cellular environments, the C/I gain for M = 8 is reduced to approx. 5.5–7.5 dB (which should be compared to the theoretical value of 9 dB for a point source). The designed DoA algorithm is very robust to co-channel interference and only a small degradation in performance is observed for single element C/I down to approx. –8 dB. We conclude that the designed beamforming implementation facilitates a potential capacity gain of ×3 in a 1/3 reuse FH-GSM network for an array size of M = 4–6.

Performance of Adaptive Antennas in FH-GSM Using Conventional Beamforming

Cellular networks will be one of the main pillars in the development of future vehicular communications. However, downlink (DL) and uplink (UL) channels must be improved to cope with the required reliability and high throughput of the coming vehicular use cases. Vehicle side solutions which benefit from the high antenna gains could improve the performance of the UL channel whose coverage is limited by UL transmit power. In this paper we experimentally evaluate the performance of a directional antennas switching system based on live Long Term Evolution (LTE) measurements. A total of more than 150 km have been driven comprising different radio propagation scenarios. The results show considerable improvements of Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), together with a reduction of handovers specially in scenarios with high Line-Of-Sight probability. Additionally, it has been found that the UL throughput does not improve with the increase of antenna gain probably due to the UL Power Control mechanism used in LTE.

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Preben Mogensen

Papers

Performance Evaluation of the 5G NR Conditional Handover in LEO-based Non-Terrestrial Networks

An Empirical Study of Propagation Models for Wireless Communications in Open-Pit Mines

Performance of Adaptive Antennas in FH-GSM Using Conventional Beamforming

Enhancing Vehicular Link Performance Using Directional Antennas at the Terminal

Impact of TCP Flow Control in the Radio Resource Management of WCDMA Networks