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020 _a9789819776443
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024 7 _a10.1007/978-981-97-7644-3
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072 7 _aUKN
_2bicssc
072 7 _aCOM043000
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072 7 _aUKN
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100 1 _aZhang, Lin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAI-enabled Spectrum Sharing
_h[electronic resource] :
_bRecent Advances In Wireless Edge Networks /
_cby Lin Zhang, Ming Xiao, Zicun Wang, Wanbin Tang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aX, 84 p. 36 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _aChapter 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.
520 _aWireless 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.
650 0 _aComputer networks .
_960157
650 0 _aWireless communication systems.
_965781
650 0 _aMobile communication systems.
_965782
650 0 _aMultiagent systems.
650 0 _aInternet of things.
_958845
650 1 4 _aComputer Communication Networks.
_965689
650 2 4 _aWireless and Mobile Communication.
_965786
650 2 4 _aMultiagent Systems.
650 2 4 _aInternet of Things.
_958845
700 1 _aXiao, Ming.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985785
700 1 _aWang, Zicun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985786
700 1 _aTang, Wanbin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985787
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819776436
776 0 8 _iPrinted edition:
_z9789819776450
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
_966578
856 4 0 _uhttps://doi.org/10.1007/978-981-97-7644-3
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
999 _c64079
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