Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Wireless Communications

Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto

Wireless communications

Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

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Sequential Monte Carlo methods in practice

In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.

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Fading channels: information-theoretic and communications aspects

Some novel results on the space-time differential modulation schemes described by B.M. Hochwald and W. Sweldens (see ibid., vol.48, p.2041-52, 2000) and V. Tarokh and H. Jafarkhani (IEEE J. Select. Areas Commun., vol.18, p.1169-74, 2000), are derived under the assumption of a quasi-static multiple-antenna channel. First, the optimality (in the maximum-likelihood sense) of the one-shot detection algorithms of Hochwald and Sweldens and of Tarokh and Jafarkhani is proved. Then, the expression of the pairwise error probability for these detectors is derived, and, as a particular case, the bit-error rate is computed for the binary phase-shift keyed scheme of Tarokh and Jafarkhani. Finally, a per-survivor processing (PSP) detection algorithm for this class of modulations is illustrated. Numerical results evidence both the superiority of the PSP strategy over the one-shot space-time differential detectors and the accuracy of the above-mentioned bit-error rate formula.

Further results on differential space-time modulations

This book introduces the theoretical elements at the basis of various classes of algorithms commonly employed in the physical layer (and, in part, in MAC layer) of wireless communications systems. It focuses on single user systems, so ignoring multiple access techniques. Moreover, emphasis is put on single-input single-output (SISO) systems, although some relevant topics about multiple-input multiple-output (MIMO) systems are also illustrated.Comprehensive wireless specific guide to algorithmic techniquesProvides a detailed analysis of channel equalization and channel coding for wireless applicationsUnique conceptual approach focusing in single user systemsCovers algebraic decoding, modulation techniques, channel coding and channel equalisation

Wireless Communications: Algorithmic Techniques

In this paper, an expectation-maximization (EM) technique for maximum a posteriori estimation is employed to devise novel soft-in soft-out algorithms for symbol detection over frequency-flat Rayleigh fading channels. An application of these algorithms to iterative decoding of coded PSK signals is proposed and some performance results are illustrated.

MAP symbol estimation on frequency-flat Rayleigh fading channels via a Bayesian EM algorithm

In this paper, a novel feedback frequency synchronizer for orthogonal frequency-division multiplexing signals transmitted over multipath fading channels is described. Its derivation is based on maximum-likelihood estimation techniques and assumes an approximate statistical knowledge of the communication channel. The performance of the proposed algorithm is assessed by computer simulation, and is compared with that provided by other synchronization algorithms and with Cramer-Rao bounds.

Maximum-likelihood frequency recovery for OFDM signals transmitted over multipath fading channels

This paper describes novel detection algorithms for coded and uncoded M-ary phase shift keying (M-PSK) signals transmitted over frequency-flat fading channels. Fading distortion is estimated with per-survivor methods using the sampled outputs of two receive filters. Some of the proposed algorithms do not require information on the fading statistics and are dubbed blind algorithms. The error rate performance of blind and nonblind algorithms is assessed by simulation for uncoded and trellis-coded phase shift keying (PSK) and is compared to the performance of other detection schemes proposed in the literature.

Giorgio M. Vitetta

Papers

Further results on differential space-time modulations

Wireless Communications: Algorithmic Techniques

MAP symbol estimation on frequency-flat Rayleigh fading channels via a Bayesian EM algorithm

Maximum-likelihood frequency recovery for OFDM signals transmitted over multipath fading channels

Double-filtering receivers for PSK signals transmitted over Rayleigh frequency-flat fading channels