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.

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Cognitive radio: brain-empowered wireless communications

1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra. 4. Parametric Methods for Line Spectra. 5. Filter Bank Methods. 6. Spatial Methods. Appendix A: Linear Algebra and Matrix Analysis Tools. Appendix B: Cramer-Rao Bound Tools. Appendix C: Model Order Selection Tools. Appendix D: Answers to Selected Exercises. Bibliography. References Grouped by Subject. Subject Index.

/pdf/spectral-analysis-of-signals-4dbupf9m4n.pdf

Spectral analysis of signals

This study investigated using Monte Carlo simulation the interaction between a linear trend and a lag-one autoregressive (AR(1)) process when both exist in a time series. Simulation experiments demonstrated that the existence of serial correlation alters the variance of the estimate of the Mann–Kendall (MK) statistic; and the presence of a trend alters the estimate of the magnitude of serial correlation. Furthermore, it was shown that removal of a positive serial correlation component from time series by pre-whitening resulted in a reduction in the magnitude of the existing trend; and the removal of a trend component from a time series as a first step prior to pre-whitening eliminates the influence of the trend on the serial correlation and does not seriously affect the estimate of the true AR(1). These results indicate that the commonly used pre-whitening procedure for eliminating the effect of serial correlation on the MK test leads to potentially inaccurate assessments of the significance of a trend; and certain procedures will be more appropriate for eliminating the impact of serial correlation on the MK test. In essence, it was advocated that a trend first be removed in a series prior to ascertaining the magnitude of serial correlation. This alternative approach and the previously existing approaches were employed to assess the significance of a trend in serially correlated annual mean and annual minimum streamflow data of some pristine river basins in Ontario, Canada. Results indicate that, with the previously existing procedures, researchers and practitioners may have incorrectly identified the possibility of significant trends. Copyright  2002 Environment Canada. Published by John Wiley & Sons, Ltd.

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The influence of autocorrelation on the ability to detect trend in hydrological series

[1] Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and nonstationary processes in nature, the analysis method would have to be adaptive. Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research. In this review, we will briefly introduce the method, list some recent developments, demonstrate the usefulness of the method, summarize some applications in various geophysical research areas, and finally, discuss the outstanding open problems. We hope this review will serve as an introduction of the method for those new to the concepts, as well as a summary of the present frontiers of its applications for experienced research scientists.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2007RG000228

A review on Hilbert‐Huang transform: Method and its applications to geophysical studies

When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.

/pdf/micro-doppler-effect-in-radar-phenomenon-model-and-1o8h9ojzmb.pdf

Micro-Doppler effect in radar: phenomenon, model, and simulation study

Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e. the value of the signal at each instant in time is well defined. However, the time representation of a signal is poorly localized in frequency, i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain. On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.

Time-Frequency Analysis

A modified Mann-Kendall trend test for autocorrelated data

Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis

INTRODUCTION Hydrologic Frequency Analysis General Aspects and Approaches Other Models Return Period, Probability, and Plotting Positions Flood Frequency Models Hydrologic Risk Regionalization Tests on Hydrologic Data SELECTION AND EVALUATION OF PARENT DISTRIBUTION: CONVENTIONAL MOMENTS Moments of Distributions and Their Sample Estimates Moment Ratio Diagrams (MRDs) Probability Plots Selection of Distributions Regional Homogeneity and Regionalization SELECTION AND EVALUATION OF PARENT DISTRIBUTIONS: PROBABILITY WEIGHTED MOMENTS AND L-MOMENTS Moments of Distributions and Their Sample Estimates L-Moment Ratio Diagrams Goodness of Fit Tests A Case Study PARAMETER AND QUANTILE ESTIMATION Introduction Parameter Estimation Quantile Estimation Confidence Intervals NORMAL AND RELATED DISTRIBUTIONS Normal Distribution Two-Parameter Lognormal (LN(2)) Distribution Three-Parameter Lognormal (LM(3)) Distribution GAMMA FAMILY Exponential Distribution Two-Parameter Gamma (G(2)) Distribution Pearson (2) Distribution Log-Pearson (3) Distribution U.S. Water Resources Council Method (WRCM) EXTREME VALUE DISTRIBUTIONS Generalized Extreme Value (GEV) Distribution The Extreme Value Type (EV(1) Distribution Weibul Distribution WAKEBY FAMILY The 5-Parameter Wakeby Distribution (WAK(5)) The 4-Parameter Wakeby Distribution (WAK(4)) The Generalized Pareto Distribution LOGISTIC DISTRIBUTIONS Logistic Distribution Generalized Logistic Distribution COMPUTER PROGRAM Introduction Description of Program REFERENCES

Khaled H. Hamed

Papers

Time-Frequency Analysis

A modified Mann-Kendall trend test for autocorrelated data

Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis

Flood Frequency Analysis

Exact Distribution of the Mann-Kendall Trend Test Statistic for Persistent Data