Nearest Neighbour Methods and their Applications in Design of 5G & Beyond Wireless Networks

Abstract

In this paper, we present an overview of Nearest neighbour (NN) methods, which are frequently employed for solving classification problems using supervised learning. The article concisely introduces the theoretical background, algorithmic, and implementation aspects along with the key applications. From an application standpoint, this article explores the challenges related to the 5G and beyond wireless networks which can be solved using NN classification techniques.

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Authors/Creators:	Zaidi, S https://orcid.org/0000-0003-1969-3727
Copyright, Publisher and Additional Information:	© 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:	Nearest neighbour search; Nearest neighbour classification; k-NN; 5G; Localisation; Beamforming; MIMO; Anomaly; SDN; Network Slicing; NFV; Energy efficiency
Dates:	Accepted: 13 January 2021 Published (online): 5 February 2021 Published: December 2021
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Depositing User:	Symplectic Publications
Date Deposited:	15 Jan 2021 16:08
Last Modified:	25 Jun 2023 22:33
Status:	Published
Publisher:	Elsevier
Identification Number:	https://doi.org/10.1016/j.icte.2021.01.003

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Nearest Neighbour Methods and their Applications in Design of 5G & Beyond Wireless Networks

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