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

This paper present an alternative promising approach to ISI mitigation by the use of single-carrier (SC) modulation combined with frequency-domain equalization (FDE).

Single-carrier frequency domain equalization

The problem of maximum likelihood (ML) detection for uncoded and coded M-PSK signals on Rayleigh fading channels is investigated. It is shown that, if the received signal is sampled at baud-rate, a ML receiver employing per-survivor processing can be implemented. The error rate performance of this receiver is evaluated by means of computer simulations and its limitations are discussed. In addition, it is shown that, on a fast fading channel, the error floor in the BER curve can be appreciably lowered if more than one received signal sample per symbol interval is processed by the receiver algorithm, Finally, a sub-optimum two-stage receiver structure for interleaved coded PSK systems is proposed. Its error rate performance is assessed for simple trellis-coded modulation schemes and compared to that provided by other receiver structures.

Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels

In this paper, novel equalization algorithms for continuous phase modulations (CPMs) are illustrated. Both conventional (linear and decision-feedback) and turbo equalization techniques are derived using the Laurent decomposition of CPM signals. All of them operate in the frequency domain and process two samples of the received signal per channel symbol. Numerical results show that on one hand, conventional equalization strategies offer good performance for binary partial response signaling over severely frequency-selective wireless channels at a moderate complexity. On the other hand, there is evidence that turbo techniques provide a small energy saving at the price of a substantial computational burden.

Equalization algorithms in the frequency domain for continuous phase modulations

A novel equalization/detection algorithm for orthogonal frequency division multiplexing (OFDM) signals transmitted over frequency-selective channels is introduced and investigated. The algorithm stems from the recognition that the Fourier transform processing inherent in OFDM turns a single wideband frequency-selective channel into a set of correlated narrowband frequency-flat fading channels. This suggests that sequence detection techniques, such as those discussed by Vitetta et al. (see IEEE Trans. Commun., vol.43, p.2750-8, 1995, IEEE Trans. Commun., vol.43, pt.II, p.1256-9, 1995, and Proc. IEEE Commun. Theory Mini-Conf (Globecom '96), London, UK, p.153-7, 1996), for time-selective flat-fading channels, can be also profitably utilized for joint equalization and decoding of OFDM signals in the frequency domain. Simulation results show that the proposed detection strategy, implemented via a standard Viterbi algorithm, provides improved performance over differential detection, with a moderate increase in receiver complexity and without requiring the periodic transmission of training blocks.

Blind equalization/detection for OFDM signals over frequency-selective channels

A data-aided feedforward algorithm has been proposed by Kuo and Fitz (see IEEE Trans. Commun., vol.45, p.1412-26, 1997) for carrier frequency estimation in M-ary phase-shift keying (PSK) transmissions over frequency-flat Rayleigh fading channels. Its accuracy is very good but the estimation range may be limited under certain operating conditions. Also, its application requires a knowledge of the Doppler bandwidth. We show that the estimation range can be greatly extended without sacrificing the estimation accuracy and a simple technique is indicated to measure the Doppler bandwidth. This allows the algorithm to operate in an adaptive manner in a time-varying environment.

Giorgio M. Vitetta

Papers

Single-carrier frequency domain equalization

Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels

Equalization algorithms in the frequency domain for continuous phase modulations

Blind equalization/detection for OFDM signals over frequency-selective channels

Further results in carrier frequency estimation for transmissions over flat fading channels