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A student who opens a spectrum analyser for the first time in a crowded city usually expects to see a wall of signals. What appears instead is a surprise: a few tall, permanently occupied peaks, and between them long stretches of nothing at all. Measurement campaigns on four continents have quantified that impression. Below 3 GHz, average occupancy over a day is frequently in the range of 5–20 per cent, and in many licensed bands it is far lower. At the same time, every national regulator maintains a waiting list of operators asking for spectrum that no longer exists on paper.
This is the contradiction that gave rise to cognitive radio. The scarcity is not physical; it is administrative. Spectrum has been allocated exclusively, in perpetuity, on the assumption that a licensee's transmitter is always on and always everywhere. Neither assumption survives contact with data. If a radio could determine, reliably and in real time, that a licensed band is idle here and now, it could use that band and vacate it the moment the licensee returned. That single idea — proposed by Joseph Mitola III in 1998–2000 and given its engineering shape by Simon Haykin's 2005 formulation — reorganised an entire research field and, eventually, the rules of several regulators.
What this book covers
This book is a complete undergraduate-to-postgraduate course in cognitive radio. It begins with the measurements and the regulatory background that motivate the field, develops the software-defined radio platform on which cognitive radio is built, and then treats spectrum sensing as what it really is: a problem in statistical detection theory. Energy detection, matched filtering, cyclostationary feature detection, eigenvalue-based blind sensing, cooperative fusion, wideband and compressive sensing each receive a dedicated chapter with derivations, worked numerical examples and simulation code.
The second half moves upward through the protocol stack. Spectrum characterisation and prediction, spectrum decision, sharing and game theory, auctions, mobility and handoff, power control, MAC design, spectrum-aware routing, transport behaviour and cross-layer architecture are each developed to the point where a reader can implement and evaluate them. Two chapters are devoted to machine learning and reinforcement learning, which have moved in a decade from a research curiosity to the default approach for spectrum prediction and access policy. The final part addresses standards and regulation (IEEE 802.22, 802.11af, 802.19.1, ETSI RRS, LSA and CBRS), security, hardware platforms, energy efficiency, and the role of cognitive techniques in 5G, 6G, the Internet of Things and satellite systems.
Who it is for
The intended reader is a final-year undergraduate or first-year postgraduate student in electronics, communication or computer engineering, or a practising engineer moving into dynamic spectrum access. The prerequisites are modest and are recalled where needed: signals and systems, a first course in probability and random processes, and the basics of digital communication. Appendix A summarises the probability results used throughout; Appendix B tabulates the Q-function and threshold values that the sensing chapters need.
How the material is organised
Each chapter opens with a short list of learning objectives and closes with a summary, review questions, numerical problems and annotated references. Worked examples are set in shaded boxes and are numbered by chapter. Every major algorithm is accompanied by executable code — Python with NumPy for simulation, and GNU Radio or MATLAB where a real signal chain is involved — so that every curve in the book can be reproduced by the reader. The final chapter contains twelve laboratory experiments and six extended design projects suitable for a semester of coursework or a capstone.
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