Intelligent Spectrum Management
Towards 6g
Herausgeber: Iyer, Sridhar; de Alwis, Chamitha; Lopez, Onel Alcaraz; Kalla, Anshuman
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Forward-thinking reference on spectrum sharing and resource management for 5G, B5G, and 6G wireless networks Intelligent Spectrum Management: Towards 6G explores various aspects of spectrum sharing and resource management in 5G, beyond 5G, and the envisaged 6G networks. The book offers an in-depth exploration of intelligent and secure sharing of spectrum and resource management in existing and future mobile networks. The book sets the stage by providing an insight to the evolution of mobile networks and highlights the importance of spectrum sharing and resource management in next-generation wi...
Forward-thinking reference on spectrum sharing and resource management for 5G, B5G, and 6G wireless networks Intelligent Spectrum Management: Towards 6G explores various aspects of spectrum sharing and resource management in 5G, beyond 5G, and the envisaged 6G networks. The book offers an in-depth exploration of intelligent and secure sharing of spectrum and resource management in existing and future mobile networks. The book sets the stage by providing an insight to the evolution of mobile networks and highlights the importance of spectrum sharing and resource management in next-generation wireless networks. At the core, the book explores various promising technologies such as cognitive radio, reinforcement learning, deep learning, reconfigurable intelligent surfaces, and blockchain technology towards efficient, intelligent, and secure sharing of spectrum and resource management. Moreover, the book presents dynamic and decentralized resource management techniques, including network slicing, game theory, and blockchain-enabled approaches. Topics covered include: * Spectrum, and why it must be utilized optimally and transparently * Future applications envisioned with 6G, such as digital twins, Industry 5.0, holographic telepresence, and Extended Reality (XR) * Challenges when Dynamic Spectrum Management (DSM) is enabled through Machine Learning (ML) techniques, including the complexity of received signals and the difficulty in obtaining accurate network data such as channel state information * Reinforcement learning and deep learning-assisted spectrum management * Synergy between Artificial Intelligence (AI) and blockchain technology for spectrum management * Private networks, including their prospects, architecture, enabling concepts, and techniques for efficient operation In essence, various innovative technologies and approaches that can be leveraged to enhance spectrum utilization and efficiently manage network resources are discussed. The book is a potential reference for researchers, academics, and professionals in the wireless service provider industry, as well as regulators and officials.
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