00978cam0 2200289 450 E60020006363920211020080517.020100507d1969 |||||ita|0103 bagerBEVersuch einer Rechtslehrevon Lazarus BendavidImpression anastaltiqueBruxellesCulture et Civilisation1969350 p.18 cmAetas kantiana26Ripr. facs. dell'ed.: Berlin, 1802001LAEC000284802001 *Aetas kantiana26Bendavid, LazarusA600200061085070220429ITUNISOB20211020RICAUNISOBUNISOB100|Coll|19|k18316E600200063639M 102 Monografia moderna SBNM100|Coll|19|k000026Si18316acquistocutoloUNISOBUNISOB20100507113715.020211020080507.0AlfanoVersuch einer Rechtslehre568895UNISOB04216nam 22005533 450 991102000530332120241216120649.0978139422794513942279499781394227938139422793097813942279521394227957(CKB)36976906000041(MiAaPQ)EBC31836407(Au-PeEL)EBL31836407(OCoLC)1481580740(Perlego)4740462(EXLCZ)993697690600004120241216d2025 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Future Networks1st ed.Newark :John Wiley & Sons, Incorporated,2025.©2025.1 online resource (414 pages)9781394227921 1394227922 Intelligent beam prediction and tracking -- Signal detection with machine learning -- AI-aided channel prediction -- Semantic communications -- Federated learning for wireless communications -- Federated learning in mesh networks -- Antenna design using artificial intelligence -- AI-driven approaches for solving electromagnetic inverse problems -- Reflectarray-based RIS-1 design using support vector machine to enhance mm-wave 5G coverage -- AI at the physical layer for wireless network security and privacy.An exploration of connected intelligent edge, artificial intelligence, and machine learning for B5G/6G architecture Artificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT). The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks. Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications. Some of the topics include: * Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systems * Channel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniques * Resource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MEC * Antenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modeling Identifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study. 5G mobile communication systemsTechnological innovationsArtificial intelligenceIndustrial applications5G mobile communication systemsTechnological innovations.Artificial intelligenceIndustrial applications.621.384Matin Mohammad A1064775Goudos Sotirios K1838902Karagiannidis George K1838903MiAaPQMiAaPQMiAaPQBOOK9911020005303321Artificial Intelligence for Future Networks4417996UNINA