Dynamic Spectrum Management: From Cognitive Radio to Blockchain and Artificial IntelligenceКНИГИ » СЕТЕВЫЕ ТЕХНОЛОГИИ
Название: Dynamic Spectrum Management: From Cognitive Radio to Blockchain and Artificial Intelligence Автор: Ying-Chang Liang Издательство: Springer Год: 2019 (2020 Edition) Страниц: 180 Язык: английский Формат: pdf (true), epub Размер: 12.4 MB
Radio spectrum, as an indispensable enabler of wireless communications, is becoming a severely scarce resource with the explosive growth of wireless traffic and massive connections of devices. Because of the spectrum scarcity problem, millimeter-wave band and Tera-Hertz band are being explored for cellular mobile communications. However, measurements have shown that the radio spectrum is experiencing underutilization due to the adoption of static and exclusive spectrum allocation method. It is expected that the spectrum allocation policy will be evolved from the fixed manner to dynamic spectrum management (DSM), in order to make full use of the radio spectrum.
The success of DSM, however, attributes not only to the availability of technical methodologies, but also to the support from the spectrum policy. Cognitive radio (CR) is the state-of-the-art enabling technique for DSM. With CR, an unlicensed/secondary user is able to opportunistically or concurrently access spectrum bands owned by the licensed/primary users. On the other hand, blockchain, as an essentially open and distributed ledger, incentivizes the formulation and secures the execution of the policies for DSM. Finally, artificial intelligence (AI) techniques help the users observe and interact with the dynamic radio environment, thereby improving the efficiency and robustness of CR and blockchain for DSM.
In the past decade, a significant advancement has been made in Artificial Intelligence (AI) research from both theoretical and application perspectives. Researchers have also applied AI techniques, particularly Machine Learning (ML) algorithms, to DSM, the results of which have shown superior performance as compared to traditional ones. In this book, we first provide a brief review on ML techniques. Then we introduce recent applications of ML algorithms to enablers of DSM, which include spectrum sensing, signal classification and dynamic spectrum access.
This book provides a systematic overview of the above three technologies for DSM, and reviews several communication systems that use DSM. It is intended for a broad range of readers, including the students and the researchers in wireless communications, as well as the radio spectrum policymakers. We hope the concepts, theories and methodologies presented in this book could offer useful references and guidance to the readers.
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