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

This paper provides packet scheduler design and performance simulations for running VoIP services over high-speed downlink packet access (HSDPA) in WCDMA. The main challenge of supporting VoIP service on HSDPA is the tight delay requirement combined with the small VoIP packet size. A packet scheduler design incorporating VoIP packet aggregation and user multiplexing is proposed and the VoIP capacity is studied for a macro-cellular environment. Results are obtained for different delay budgets and packet scheduling settings, using either blind round robin or a slightly modified version of proportional fair scheduling. For proportional fair scheduling with code-multiplexing of 4-users, the downlink VoIP cell capacity on HSDPA is found to be in the range 72-104 users depending on whether the delay budget for the node-B scheduling and user reception equals 80 ms or 150 ms, respectively.

Performance of VoIP on HSDPA

COST Action 231: Digital Mobile Radio Towards Future Generation System, Final Report.

This paper addresses the performance targets and the technology components being studied by 3GPP for LTE-Advanced. LTE-Advanced is the next major step in the evolution of UTRAN Long Term Evolution (LTE) release 8, currently being finalized by 3GPP. The high level targets of LTE-Advanced are to meet or exceed the IMT-Advanced requirements set by ITU-R and furthermore, meet any additional operator requirements. This for instance includes the target of supporting more than one gigabit/s data rates, higher cell throughput and lower cost per bit. The technology components being identified as part of the LTE-Advanced Study Item include component carrier aggregation to enable up to 100MHz bandwidth, advanced MIMO options up to 8×8 in DL and 4×4 in UL, coordinated multiple point transmission and reception (CoMP), relay nodes (RN) and autonomous component carrier selection (ACCS) for uncoordinated femto cell deployment.

LTE-Advanced: The path towards gigabit/s in wireless mobile communications

Ultra-reliable low-latency communications (URLLC) entail the transmission of sporadic and small packets, with low latency and very high reliability. Among many potential areas of optimization for URLLC, the problems of large delays during HARQ retransmissions, and inaccurate link adaptation as a consequence of the rapidly-varying interference conditions are studied. The former is addressed by reducing the TTI length and HARQ round-trip time, as compared to what is used in LTE; whereas including low-pass filtered interference information in the CQI report is also proved to have great potential. Extensive system-level simulations of the downlink performance show that the URLLC requirements, i.e. latencies below 1 ms and 99.999% reliability, are achievable at low load scenarios, whereas some performance degradation (1 – 3 ms latency) is experienced at higher loads due to the increased queuing delay and inter-cell interference.

/pdf/mac-layer-enhancements-for-ultra-reliable-low-latency-35u14833jc.pdf

MAC layer enhancements for ultra-reliable low-latency communications in cellular networks

The target for a new 5G radio access technology is to support multi-Gbps and ms latency connectivity simultaneously at noticeably lower energy consumption and cost compared to the existing 4G technologies, such as LTE-Advanced. Extremely short air interface latency is required to achieve these requirements in a TDD-based local area network. In this paper, we discuss how the required short TDD latency can be achieved and further utilized in 5G physical air interface. First, we investigate the enablers and limits of TDD latency by analyzing the performance of OFDM in different channel environments and discussing on the consequent frame length limits. We then provide a description on how the achieved short TDD latency can further be utilized to enable remarkably low energy consumption. A numerical analysis comparing the battery life time of the suggested 5G TDD air interface and LTE is provided, showing remarkable gains for the 5G air interface concept.

Preben Mogensen

Papers

Performance of VoIP on HSDPA

COST Action 231: Digital Mobile Radio Towards Future Generation System, Final Report.

LTE-Advanced: The path towards gigabit/s in wireless mobile communications

MAC layer enhancements for ultra-reliable low-latency communications in cellular networks

Achieving low latency and energy consumption by 5G TDD mode optimization