Indoor Radio Dot Placement Optimization using UE Positioning and K-Means Clustering

This research evaluates the performance impact from dynamic information, mainly user density and distribution, to quantitatively evaluate radio dot adjustment algorithms that can better accommodate for cost-effective performance solutions. The number of UEs and their distribution are simulated, with the Machine Learning (ML) cluster algorithm of K-means being used to evaluate the ideal scenario where all the RD unit locations are adjusted. Further thesis specific algorithms are used to improve network performance for a cost-efficient solution is implemented. Results have proved that dynamic information is one of the key factors with major impact to the network performance. Adjusting RD unit placements by taking the dynamic information into account could provide a cost-efficient solution to optimize the Indoor network performance.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2024

Author dc:creator

• Bousfield, John Liu

Statement dc:rights

• Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2024-16222

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/43200