Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.

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Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations

Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, RD algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communicationsShows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

https://ie.technion.ac.il/~mcib/Gribonval_Zibulevsky_SCA_chapter.pdf

Handbook of Blind Source Separation: Independent Component Analysis and Applications

Space-time processing can improve network capacity, coverage, and quality by reducing co-channel interference (CCI) while enhancing diversity and array gain. This article focuses largely on the receive (mobile-to-base station) time-division multiple access (TDMA) (nonspread modulation) application for high-mobility networks. We describe a large (macro) cell propagation channel and discuss different physical effects such as path loss, fading delay spread, angle spread, and Doppler spread. We also develop a signal model incorporating channel effects. Both forward-link (transmit) and reverse-link (receive) channels are considered and the relationship between the two is discussed. Single- and multiuser models are treated for four important space-time processing problems, and the underlying spatial and temporal structure are discussed as are different algorithmic approaches to reverse link space-time professing with blind and nonblind methods for single- and multiple-user cases. We cover forward-link space-time algorithms and we outline methods for estimation of multipath parameters. We also discuss applications of space-time processing to CDMA, applications of space-time techniques to current cellular systems, and industry trends.

Space-time processing for wireless communications

This paper provides a tutorial introduction to the constant modulus (CM) criterion for blind fractionally spaced equalizer (FSE) design via a (stochastic) gradient descent algorithm such as the constant modulus algorithm (CMA). The topical decisions utilized in this tutorial can be used to help catalog the emerging literature on the CM criterion and on the behavior of (stochastic) gradient descent algorithms used to minimize it.

/pdf/blind-equalization-using-the-constant-modulus-criterion-a-2gf351pnrk.pdf

Blind equalization using the constant modulus criterion: a review

Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.

Recurrent Neural Networks for Prediction

A stochastic analysis is presented of the convergence properties of a second-order adaptive infinite-impulse-response (IIR) notch filter with a gradient-descent coefficient update algorithm. The analysis is based on a linearization of the gradient function in the vicinity of the optimal solution. Expressions for the stability bounds of the algorithm step size and for the steady-state variance of the adaptive parameter are derived. These analytical results are verified by computer simulations for a variety of signal and noise conditions. >

http://ieeexplore.ieee.org/iel5/143/3356/00112101.pdf

Stability bounds for an adaptive IIR notch filter

We describe an adaptive algorithm that uses a subspace method for separating cochannel time-division multiple-access (TDMA) signals impinging on an antenna array. A frame synchronization algorithm is initially employed to identify the cochannel scenario. The adaptive algorithm uses a subspace decomposition of the array signals to constrain the beamformer weights for the signal of interest to be in a specific subspace. This subspace corresponds to the orthogonal complement of the subspace spanned by the direction vectors of the interferers. A follow-on linear equalizer removes the intersymbol interference (ISI) introduced by the transmit filter.

A subspace method for separating cochannel TDMA signals

The convergence behavior of perceptron learning algorithms has been difficult to analyze because of their inherent nonlinearity and the lack of an appropriate model for the training signals. In many cases, extensive computer simulations have been the only way of quantifying their performance. Previously we introduced a stochastic convergence model based on a system identification formulation of the training data that allows one to derive closed-form expressions for the stationary points and cost functions, as well as deterministic recursions for the transient learning behavior. We provide an overview of this approach and describe how it is applied to single- and two-layer perceptron configurations. >

http://ieeexplore.ieee.org/iel2/3310/9942/00471587.pdf

On the system identification convergence model for perceptron learning algorithms

The authors present a computationally efficient blind equalization architecture that is based on multirate and subband adaptive filtering techniques. The received signal is first partitioned by a filter bank into a set of narrowband frequency bins that are approximately nonoverlapping. The adaptive processing is then performed at a lower sampling rate, thus achieving a considerable savings in complexity. Furthermore, the signal power in each frequency bin is normalized to obtain a more uniform convergence rate across the adaptive filter coefficients. The overall convergence rate of the filter is thus dramatically improved compared to that of conventional time- and frequency-domain implementations. Computer simulations of this filter bank equalizer in a multipath environment are presented to illustrate its transient and steady-state convergence properties. >

Adaptive equalization using multirate filtering techniques

In this paper we describe an adaptive algorithm for separating fetal and maternal heart beats from data containing both fetal and maternal QRS complexes. The algorithm classifies the combined heart-rate data as a series of fetal maternal, and noise events using a technique of template matching. Peak detection is first employed to locate the potential fetal and maternal QRS complexes (referred to as candidate events). Fetal and maternal templates are generated automatically from the candidate events in the initialization period, and are used to classify the remaining candidate events based on certain similarity criteria. Once the fetal and maternal complexes are successfully detected and separated, a counting mechanism can be utilized to derive the corresponding heart rates. Computer simulations using real data demonstrate the effectiveness of the algorithm.

John J. Shynk

Papers

Stability bounds for an adaptive IIR notch filter

A subspace method for separating cochannel TDMA signals

On the system identification convergence model for perceptron learning algorithms

Adaptive equalization using multirate filtering techniques

A signal separation algorithm for fetal heart-rate estimation