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AI-enabled spectrum sharingrecent advances in wireless edge networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI-enabled spectrum sharingby Lin Zhang ... [et al.].
Reminder of title:	recent advances in wireless edge networks /
other author:	Zhang, Lin.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	x, 84 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Wireless communication systems.
Online resource:	https://doi.org/10.1007/978-981-97-7644-3
ISBN:	9789819776443$q(electronic bk.)

AI-enabled spectrum sharingrecent advances in wireless edge networks /
AI-enabled spectrum sharingrecent advances in wireless edge networks /[electronic resource] :by Lin Zhang ... [et al.]. - Singapore :Springer Nature Singapore :2024. - x, 84 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

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.

ISBN: 9789819776443$q(electronic bk.)

Standard No.: 10.1007/978-981-97-7644-3doiSubjects--Topical Terms:

182744
Wireless communication systems.

LC Class. No.: TK5103.2

Dewey Class. No.: 621.382

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