04414nam a22006015i 4500 978-981-97-7644-3 DE-He213 20251030113133.0 cr nn 008mamaa 241008s2024 si | s |||| 0|eng d 9789819776443 978-981-97-7644-3 10.1007/978-981-97-7644-3 doi TK5105.5-5105.9 UKN bicssc COM043000 bisacsh UKN thema Zhang, Lin. author. aut http://id.loc.gov/vocabulary/relators/aut AI-enabled Spectrum Sharing [electronic resource] : Recent Advances In Wireless Edge Networks / by Lin Zhang, Ming Xiao, Zicun Wang, Wanbin Tang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. X, 84 p. 36 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computer Science, 2191-5776 Chapter 1 Introductions and Preliminaries -- Chapter 2 AI-Enabled opportunistic Spectrum Sharing -- Chapter 3 AI-Enabled Centralized Spectrum Sharing -- Chapter 4 AI-Enabled Distributed Spectrum Sharing -- Chapter 5: Conclusions. Wireless edge networks aim to provide last-mile wireless connections between access points and diversified wireless end devices. Recent years witness the rapid development of wireless communication ecosystems including fundamental theory breakthroughs, manufacture capability improvements, as well as the explosively increasing wireless end devices and service demands. It is known that spectrum is the irreplaceable resource for wireless transmissions in edge networks. Nevertheless, it is quite challenging and inefficient to allocate dedicated spectrum for each single transmission link due to the severe shortage of spectrum resource. Alternatively, by enabling different links to use the same spectrum, spectrum sharing is envisioned to be a promising paradigm to properly accommodate the conflict between the scarce spectrum resource and substantial spectrum demands. Conventionally, model-driven optimization methods are widely adopted to optimize the spectrum sharing policy in the edge network and achieve friendly coexistence among different transmission links. However, future wireless edge network is predicted to be large-scale and heterogeneous, model-driven optimization methods will be problematic such as imperfect modelling and unacceptable overheads. Different from the existing related books on spectrum sharing or spectrum management for wireless edge networks, our book leverages the artificial intelligence (AI) to achieve smart spectrum sharing for wireless edge networks and elaborates AI-enabled spectrum sharing technique in typical scenarios, which can guide the development of next-generation spectrum sharing standards, as well as provide innovative spectrum sharing methods for related practitioners, including research fellow, lecturers, and students. Computer networks . 60157 Wireless communication systems. 65781 Mobile communication systems. 65782 Multiagent systems. Internet of things. 58845 Computer Communication Networks. 65689 Wireless and Mobile Communication. 65786 Multiagent Systems. Internet of Things. 58845 Xiao, Ming. author. aut http://id.loc.gov/vocabulary/relators/aut 85785 Wang, Zicun. author. aut http://id.loc.gov/vocabulary/relators/aut 85786 Tang, Wanbin. author. aut http://id.loc.gov/vocabulary/relators/aut 85787 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819776436 Printed edition: 9789819776450 SpringerBriefs in Computer Science, 2191-5776 66578 https://doi.org/10.1007/978-981-97-7644-3 ZDB-2-CWD ZDB-2-SXPC 64079 64079