Simon Haykin

Cognitive radio: brain-empowered wireless communications

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

Kalman Filtering and Neural Networks

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. The book deals with important applications in such fields as control, financial forecasting, and idle speed control.

Cubature Kalman Filters

In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a third-degree spherical-radial cubature rule that provides a set of cubature points scaling linearly with the state-vector dimension. The CKF may therefore provide a systematic solution for high-dimensional nonlinear filtering problems. The paper also includes the derivation of a square-root version of the CKF for improved numerical stability. The CKF is tested experimentally in two nonlinear state estimation problems. In the first problem, the proposed cubature rule is used to compute the second-order statistics of a nonlinearly transformed Gaussian random variable. The second problem addresses the use of the CKF for tracking a maneuvering aircraft. The results of both experiments demonstrate the improved performance of the CKF over conventional nonlinear filters.

Spectrum Sensing for Cognitive Radio

Spectrum sensing is the very task upon which the entire operation of cognitive radio rests. For cognitive radio to fulfill the potential it offers to solve the spectrum underutilization problem and do so in a reliable and computationally feasible manner, we require a spectrum sensor that detects spectrum holes (i.e., underutilized subbands of the radio spectrum), provides high spectral-resolution capability, estimates the average power in each subband of the spectrum, and identifies the unknown directions of interfering signals. Cyclostationarity is another desirable property that could be used for signal detection and classification. The multitaper method (MTM) for nonparametric spectral estimation accomplishes these tasks accurately, effectively, robustly, and in a computationally feasible manner. The objectives of this paper are to present: 1) tutorial exposition of the MTM, which is expandable to perform space-time processing and time-frequency analysis; 2) cyclostationarity, viewed from the Loeve and Fourier perspectives; and 3) experimental results, using Advanced Television Systems Committee digital television and generic land mobile radio signals, followed by a discussion of the effects of Rayleigh fading.

Cognitive radar: a way of the future

This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echo-location system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remote-sensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.

Turbo-BLAST for wireless communications: theory and experiments

Turbo-BLAST is a novel multitransmit multireceive (MTMR) antenna scheme for high-throughput wireless communications. It exploits the following ideas: the Bell Labs layered space time (BLAST) architecture; random layered space-time (RLST) coding scheme by using independent block codes and random space-time interleaving; sub-optimal turbo-like receiver that performs iterative decoding of the RLST codes and estimation of the channel matrix in an iterative and, most important, simple fashion. The net result is a new transceiver that is not only computationally efficient compared with the optimal maximum likelihood decoder, but it also yields a probability of error performance that is orders of magnitude smaller than traditional BLAST schemes for the same operating conditions. This paper also presents experimental results using real-life indoor channel measurements demonstrating the high-spectral efficiency of turbo-BLAST.

Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature

In this paper, a new version of the quadrature Kalman filter (QKF) is developed theoretically and tested experimentally. We first derive the new QKF for nonlinear systems with additive Gaussian noise by linearizing the process and measurement functions using statistical linear regression (SLR) through a set of Gauss-Hermite quadrature points that parameterize the Gaussian density. Moreover, we discuss how the new QKF can be extended and modified to take into account specific details of a given application. We then go on to extend the use of the new QKF to discrete-time, nonlinear systems with additive, possibly non-Gaussian noise. A bank of parallel QKFs, called the Gaussian sum-quadrature Kalman filter (GS-QKF) approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities. The weights are obtained from the residuals of the QKFs. Three different Gaussian mixture reduction techniques are presented to alleviate the growing number of the Gaussian sum terms inherent to the GS-QKFs. Simulation results exhibit a significant improvement of the GS-QKFs over other nonlinear filtering approaches, namely, the basic bootstrap (particle) filters and Gaussian-sum extended Kalman filters, to solve nonlinear non- Gaussian filtering problems.

The complex backpropagation algorithm

The backpropagation (BP) algorithm that provides a popular method for the design of a multilayer neural network to include complex coefficients and complex signals so that it can be applied to general radar signal processing and communications problems. It is shown that the network can classify complex signals. The generalization of the BP to deal with complex signals should make it possible to expand the line of applications of this powerful nonlinear signal processing algorithm. >

Adaptive tracking of linear time-variant systems by extended RLS algorithms

We exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applications, experiments are presented that demonstrate the tracking superiority of the extended RLS algorithms compared with the standard RLS and least-mean-squares (LMS) algorithms.

Nonlinear Methods of Spectral Analysis

Prediction-error filtering and maximum-entropy spectral estimation.- Autoregressive and mixed autoregressive-moving average models and spectra.- Iterative least-squares procedure for ARMA spectral estimation.- Maximum-likelihood spectral estimation.- Application of the maximum-likelihood method and the maximum-entropy method to array processing.- Recent advances in spectral estimation.Prediction-error filtering and maximum-entropy spectral estimation.- Autoregressive and mixed autoregressive-moving average models and spectra.- Iterative least-squares procedure for ARMA spectral estimation.- Maximum-likelihood spectral estimation.- Application of the maximum-likelihood method and the maximum-entropy method to array processing.- Recent advances in spectral estimation.