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

We address the fundamental tradeoffs among latency, reliability and throughput in a cellular network. The most important elements influencing the KPIs in a 4G network are identified, and the inter-relationships among them is discussed. We use the effective bandwidth and the effective capacity theory as analytical framework for calculating the maximum achievable rate for a given latency and reliability constraint. The analysis is conducted in a simplified LTE network, providing baseline — yet powerful — insight of the main tradeoffs. Guidelines to extend the theory to more complex systems are also presented, including a semi-analytical approach for cases with intractable channel and traffic models. We also discuss the use of system-level simulations to explore the limits of LTE networks. Based on our findings, we give some recommendations for the imminent 5G technology design phase, in which latency and reliability will be two of the principal KPIs.

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Fundamental tradeoffs among reliability, latency and throughput in cellular networks

This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared channel. This scenario presents multiple challenges in terms of radio resource scheduling, link adaptation, and inter-cell interference, which are identified and addressed throughout this paper. We propose a joint link adaptation and resource allocation policy that dynamically adjusts the block error probability of URLLC small payload transmissions in accordance with the instantaneous experienced load per cell. Extensive system-level simulations of the downlink performance showpromising gains of this technique, reducing the URLLC latency from 1.3 to 1 ms at the 99.999% percentile, with less than 10% degradation of the eMBB throughput performance as compared with conventional scheduling policies. Moreover, an exhaustive sensitivity analysis is conducted to determine the URLLC and eMBB performance under different offered loads, URLLC payload sizes, and link adaptation and scheduling strategies. The presented results give valuable insights on the maximum URLLC offered traffic load that can be tolerated while still satisfying the URLLC requirements, as well as what conditions are more appropriate for dynamic multiplexing of URLLC and eMBB traffic in the upcoming 5G systems.

Joint Link Adaptation and Scheduling for 5G Ultra-Reliable Low-Latency Communications

In this paper we present an initial performance evaluation of the 3GPP UTRA long term evolution (UTRA LTE) uplink with baseline settings. The performance results are obtained from a detailed UTRA LTE uplink link level simulator supporting OFDMA and SC-FDMA schemes. The basic transmission scheme for uplink direction is based on single-carrier transmission in the form of DFT spread OFDM with an MMSE receiver. Two antenna configurations, SISO and 1times2 SIMO are considered in the analysis of spectral efficiency in addition to adaptive modulation and coding (AMC) and L1-HARQ. For assessment purposes, the performance results of SC-FDMA are compared with OFDMA. It is shown that 1times2 SIMO greatly increases the spectral efficiency of SC-FDMA making it comparable to OFDMA, especially for high coding rate. Furthermore, SC-FDMA has a flexibility to increase BLER performance by exploiting frequency diversity.

Initial Performance Evaluation of DFT-Spread OFDM Based SC-FDMA for UTRA LTE Uplink

Global warming has put the energy consumption of all industries into focus. In 2005 mobile communications contributed to about 0.2% of global CO 2 emissions. Mobile operators have been reporting annual increases from 300% to 700% in 3G data traffic volumes. Such a steady growth in traffic requires regular upgrades in the infrastructure. While network equipment is in itself becoming more efficient, these upgrades still increase the overall energy consumption of the networks. This paper investigates the energy saving potential of exploiting cell size breathing by putting low loaded cells into sleep mode. The energy consumption and network performance of the resulting network are used to quantify the potential of this feature. The investigation is carried out on a tilt optimized network. Since putting cells into sleep mode results in a non- optimum antenna tilt configuration, this paper also investigates the possible gains of re-optimizing antenna tilting. The results show that by allowing sleep mode, over a restricted period of 12 hours, an energy saving of 33% is possible. While this energy saving comes at no expense of the overall network performance, the gain in average user data rate is noted to decrease by 21%.

Cell size breathing and possibilities to introduce cell sleep mode

In the existing scheduled radio standards using Orthogonal Frequency Division Multiplexing (OFDM) or Discrete Fourier Transform-spread-OFDM (DFT-s-OFDM) modulation, the Cyclic Prefix (CP) duration is usually hard-coded and set as a compromise between the expected channel characteristics and the necessity of fitting a predefined frame duration. This may lead to system inefficiencies as well as bad coexistence with networks using different CP settings. In this paper, we propose the usage of zero-tail DFT-s-OFDM signals as a solution for decoupling the radio numerology from the expected channel characteristics. Zero-tail DFT-s-OFDM modulation allows to adapt the overhead to the estimated delay spread/propagation delay. Moreover, it enables networks operating over channels with different characteristics to adopt the same numerology, thus improving their coexistence. An analytical description of the zero-tail DFT-s-OFDM signals is provided, as well as a numerical performance evaluation with Monte Carlo simulations. Zero-tail DFT-s-OFDM signals are shown to have approximately the same Block Error Rate (BLER) performance of traditional OFDM, with the further benefit of lower out-of-band (OOB) emissions.

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Preben Mogensen

Papers

Fundamental tradeoffs among reliability, latency and throughput in cellular networks

Joint Link Adaptation and Scheduling for 5G Ultra-Reliable Low-Latency Communications

Initial Performance Evaluation of DFT-Spread OFDM Based SC-FDMA for UTRA LTE Uplink

Cell size breathing and possibilities to introduce cell sleep mode

Zero-tail DFT-spread-OFDM signals