John J. Shynk

Bio: John J. Shynk is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Adaptive filter & Adaptive beamformer. The author has an hindex of 20, co-authored 131 publications receiving 1459 citations.

The constant modulus array for cochannel signal copy and direction finding

Abstract: The constant modulus (CM) array is a blind adaptive beamformer capable of recovering a narrowband signal among several cochannel sources without using a pilot or training signal. It is a conventional weight-and-sum adaptive beamformer whose weights are updated by the constant modulus algorithm. An adaptive signal canceller follows the beamformer to remove the captured signal from the array input and to provide an estimate of its direction vector. Based on a Wiener model, we investigate the steady-state properties of the CM array and the signal canceller. For mutually uncorrelated sources and noise, it is shown that the signal canceller exactly removes the source captured by the array. Thus, identical stages of the CM array and signal canceller may be used in a multistage system to recover several cochannel sources. Computer simulations are presented to verify the analytical results and to illustrate the transient behavior of the system.

Nonlinear techniques for the joint estimation of cochannel signals

Abstract: Cochannel interference occurs when two or more signals overlap in frequency and are present concurrently. Unlike in spread-spectrum multiple-access systems where the different users necessarily share the same channel, cochannel interference is a severe hindrance to frequency- and time-division multiple-access communications, and is typically minimized by interference rejection/suppression techniques. Rather than using interference suppression, we are interested in the joint estimation of the information-bearing narrow-band cochannel signals. Novel joint estimators are proposed that employ a single-input demodulator with oversampling to compensate for timing uncertainties. Assuming finite impulse-response channel characteristics, maximum likelihood (ML) and maximum a posteriori (MAP) criteria are used to derive cochannel detectors of varying complexities and degrees of performance. In particular, a (suboptimal) two-stage joint MAP symbol detector (JMAPSD) is introduced that has a lower complexity than the single-stage estimators while accruing only a marginal loss in error-rate performance at high signal-to-interference ratios. Assuming only reliable estimates of the primary and secondary signal powers, a blind adaptive JMAPSD algorithm for a priori unknown channels is also derived. The performance of these nonlinear joint estimation algorithms is studied through example computer simulations for two cochannel sources.

Bayesian algorithms for blind equalization using parallel adaptive filtering

Abstract: A new blind equalization algorithm based on a suboptimum Bayesian symbol-by-symbol detector is presented. It is first shown that the maximum a posteriori (MAP) sequence probabilities can be approximated using the innovations likelihoods generated by a parallel bank of Kalman filters. These filters generate a set of channel estimates conditioned on the possible symbol subsequences contributing to the intersymbol interference. The conditional estimates and MAP symbol metrics are then combined using a suboptimum Bayesian formula. Two methods are considered to reduce the computational complexity of the algorithm. First, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the channel estimation process and hence the number of parallel filters required. Second, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. A computational complexity analysis of the LMS Bayesian equalizer demonstrates that its implementation in parallel programmable digital signal processing devices is feasible at 16 kbps. The performance of the resulting algorithms is evaluated through bit-error-rate simulations, which are compared to the performance bounds of the maximum-likelihood sequence estimator. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (typically within 200 iterations) for both BPSK and QPSK modulation formats. >

Comparative performance study of several blind equalization algorithms

Abstract: This paper examines the transient and steady-state characteristics of several Bussgang-type blind equalization algorithms. A combination of computer simulations and analysis is used to assess the relative performance of the various algorithms. The computer simulations involve channel characteristics typical of those found in an urban multipath environment, and they include the effects of frequency offset. The equalizer structures considered in this paper are comprised of a T/2 fractionally-spaced linear finite impulse response filter. The analysis of misadjustment is based on an approximate Gaussian model of the data.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Steady-state analysis of the multistage constant modulus array

Abstract: The multistage constant modulus (CM) array is a cascade adaptive beamforming system that can recover several narrowband cochannel signals without training. We examine its steady-state properties at convergence using a stochastic analysis and computer simulations. Based on a Wiener model of convergence for the gradient adaptive algorithms, closed-form expressions are derived for the CM array and canceller weight vectors, as well as the effective source direction vectors at all stages along the cascade system. The signal-capture and direction-finding capabilities of the system are also discussed. Computer simulations for stationary and fading sources are presented to confirm the theoretical results and to illustrate the rapid convergence behavior of the adaptive algorithms.

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

Abstract: 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.

Handbook of Blind Source Separation: Independent Component Analysis and Applications

Abstract: 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

Space-time processing for wireless communications

Abstract: 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.

Blind equalization using the constant modulus criterion: a review

Abstract: 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.

Recurrent Neural Networks for Prediction

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