Jayaweera S.K. Signal Processing for Cognitive Radios

John Wiley, 2015. — 802.Arguably, it is signal processing that makes a cognitive radio cognitive. Its predecessor, software-defined radio (SDR) technology, already provides a software-reconfigurable device platform by implementing most baseband radio operations in software instead of in hardware. Cognitive radios are meant to be SDRs that are cognitive and intelligent. Acquisition of knowledge through learning is a common aspect of both cognition and intelligence, while self-awareness and reasoning (application of acquired knowledge) are perhaps distinctive features of cognition and intelligence, respectively. A cognitive radio, thus, is supposed to possess all these features: self-awareness, learning, and reasoning. Clearly, these are attributes that a radio can possess mostly through signal processing. It is the signal processing algorithms, implemented on an SDR platform, that will endow a radio with self-awareness, learning, and reasoning abilities.
There are many books devoted to cognitive radios. However, none are devoted to signal processing in cognitive radios. This book is an attempt to highlight the fundamental role of signal processing in cognitive radios. One may identify two types of signal processing within a cognitive radio: signal processing for gaining spectrum awareness and signal processing for achieving efficient communications. Many processing algorithms that fall under the latter are already present in all wireless communications systems and devices. However, signal processing for spectrum awareness is unique to cognitive radios. These are aimed at providing the cognitive radio with self-awareness, a necessary ingredient of cognition. Attempting to comprehensively cover signal processing of both these types in a single book is perhaps an unrealistic goal. Moreover, it is unnecessary given the fact that there are many excellent books devoted to signal processing in wireless communications systems. Hence, the focus of this book is on signal processing that is unique to a cognitive radio. Not surprisingly, signal processing for gaining spectrum awareness constitutes a bulk of this focus. Still though, there are certain advanced signal processing techniques aimed at achieving efficient communications that are realistically well suited for implementations on sophisticated devices such as cognitive radios, thus deserving to be included in a book on signal processing for cognitive radios. An example is the advanced cooperative communications and processing techniques.
Any radio that is simply cognitive and intelligent can be considered as a cognitive radio. Hence, the notion of cognitive radios is a broad concept. Depending on the application context and the type of communications network in question, cognition and intelligence in a cognitive radio may be directed at achieving different objectives. One such widely pursued objective is the development of radios that may coexist with licensed spectrum users through dynamic spectrum sharing (DSS). However, there are other useful objectives as well, including, among others, multiband/multimode operation and antijamming. In general, such cognitive radios may be taken to be wideband radios in the sense that they may be able to operate over a wide span of non-contiguous spectrum. By default, the cognitive radios in this book are such wideband radios whose cognition and intelligence are aimed at arbitrary performance objectives. The DSS cognitive radios are treated as an important special case.[b Introduction to Cognitive Radios[/b]The Cognitive Radio
Cognitive Radios and Dynamic Spectrum Sharing
Theoretical Foundations
Introduction to Detection Theory
Introduction to Estimation Theory
Power Spectrum Estimation
Markov Decision Processes
Bayesian Nonparametric Classification
Signal Processing in Cognitive Radios
Wideband Spectrum Sensing
Spectral Activity Detection in Wideband Cognitive Radios
Signal Classification in Wideband Cognitive Radios
Primary Signal Detection in DSA Cognitive Networks
Spectrum Decision-Making in DSA Cognitive Networks
Dynamic Spectrum Leasing in Cognitive Radio Networks
Cooperative Cognitive Communications
Machine Learning in Cognitive Radios
A: Nyquist Sampling Theorem
B: A Collection of Useful Probability Distributions
C: Conjugate Priors