Advancing 5G Coverage: Novel Methods for Metasurface Characterization and Leaky-Wave Antenna Reconfigurability

This thesis introduces novel approaches to enhance 5G coverage through accurate metasurface (MS) characterization using incident field reconstruction and pattern reconfigurability in leaky wave antennas (LWAs). Metasurfaces offer potential coverage enhancement by signal manipulation. The incident field reconstruction method efficiently analyzes and optimizes MSs, addressing traditional limitations. Using numerical superposition and mechanical displacement, it creates user-defined incident fields, reducing design-measurement discrepancies. This facilitates improved MS deployment for 5G coverage. Inspired by source displacement, a pattern reconfigurable LWA design with mechanical switching is proposed. In Ka-band, a contactless excitation method enables beam-steering, benefiting communication systems with cost-effective flexibility. This approach also facilitates beam scanning, bandwidth, and polarization reconfiguration. This thesis provides innovative solutions to wireless communication challenges, demonstrated through MS incident field reconstruction and LWA contactless excitation methods, promising more reliable and efficient 5G networks.

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

2023

Author dc:creator

• Wang, Kan

Statement dc:rights

• Copyright © 2023 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/2023-15712

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/40990