Orthogonal frequency division multiplexing (OFDM) is, with substantial progress in digital signal processing, becoming an important part of the telecommunications arena. The most appealing feature of OFDM is the simplicity of the receiver design due to the efficiency with which OFDM can cope with the effects of frequency-selective multipath channels. Here, we address the problem of channel estimation for OFDM systems. Exploiting receive antenna diversity, a second-order statistics-based (SOS) blind technique is proposed. Our method differs from the existing SOS-based techniques in that channel estimation is carried out using the frequency domain (i.e., post-FFT) signals whereas existing methods use the time domain signals (i.e., pre-FFT). In the proposed method, channel identifiability is guaranteed regardless of the channels zeros locations, so long as any roots common to all the diversity channels are on the unit circle. Only short data records are required to achieve good performance. For PSK transmission, this method enables channel estimation even from a single OFDM symbol at high SNR. Further, when only a small number of subcarriers can be used for channel estimation because of computational complexity, we determine the optimal set of subcarriers in terms of estimation accuracy.

A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors – noted experts on the topic – offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal Data Processing brings together in one volume the range of multi-modal data processing.

This SpringerBrief investigates the performance of semi-blind independent component analysis (ICA) based equalization and carrier frequency offset estimation approaches (CFO) for a number of orthogonal frequency division multiplexing (OFDM) based wireless communication systems. It provides a comprehensive overview of the challenges of channel equalization and frequency synchronization for different wireless systems. The authors present the wireless communication channel and system models. Key existing CFO estimation methods are reviewed, along with a number of the training based and non-training based (blind) channel estimation methods. This is followed by a study of ICA and its applications to OFDM-based wireless communication systems. Later chapters provide a detailed description of recent research on semi-blind CFO estimation and ICA based equalization approaches for various wireless communication systems including multiple-input multiple-output (MIMO) OFDM and coordinated multipoint (CoMP) systems. Semi-blind CFO estimation and equalization structures provide a spectrum-efficient and high-performance solution for high speed wireless communications. This book is suitable for postgraduate students, researchers or professionals in the area of wireless communications.

Modern telecommunication systems offer services demanding very high transmission rates. Channel identification appears as a major concern in this context. Looking forward better trade-offs between the quality of information recovery and suitable bit-rates, the use of blind techniques is of great interest. Making use of the special properties of the 4th-order output cumulants, this thesis introduces new statistical signal processing tools with applications in radio-mobile communication systems. Exploiting the highly symmetrical structure of the output cumulants, we address the blind channel identification problem by introducing a multilinear model for the 4th-order output cumulant tensor, based on the Parallel Factor (Parafac) analysis. The components of the new tensor model have a Hankel structure, in the SISO case. For (memoryless) MIMO channels, redundant tensor factors are exploited in the estimation of the channel coefficients. In this context, we develop blind identification algorithms based on a single-step least squares (SSLS) minimization problem. The proposed methods fully exploit the multilinear structure of the cumulant tensor as well as its symmetries and redundancies, thus enabling us to avoid any kind of pre-processing. Indeed, the SS-LS approach induces a solution based on a sole optimisation procedure, without intermediate stages, contrary to the vast majority of methods found in the literature. Using only the 4th-order cumulants, and exploiting the Virtual Array concept, we treat the source localization problem in multiuser sensor array processing. Exploiting a particular arrangement of the cumulant tensor, an original contribution consists in providing additional virtual sensors by improving the array resolution by means of an enhanced array structure that commonly arises when using 6th-order statistics. We also consider the problem of estimating the physical parameters of a multipath MIMO communication channel. Using a fully blind approach, we first treat the multipath channel as a convolutive MIMO model and propose a new technique to estimate its coefficients. This non-parametric technique generalizes the methods formerly proposed for the SISO and (memoryless) MIMO cases. Using a tensor formalism to represent the multipath MIMO channel, we estimate the physical multipath parameters by means of a combined ALS-MUSIC technique based on subspace algorithms. Finally, we turn our attention to the problem of determining the order of FIR channels in the context of MISO systems. We introduce a complete combined procedure for the detection and estimation of frequency-selective MISO communication channels. The new algorithm successively detects the signal sources, determines the order of their individual transmission channels and estimates the associated channel coefficients using a deflationary approach.

The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems *Provides the latest research on System Identification *Contains contributions written by experts in the field *Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.