Spatial Clustering in Slotted ALOHA Two-Hop Random Access for Machine Type Communications
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The LTE random access procedures proposed in 3GPP for Machine Type Communication (MTC) in current cellular systems may become overwhelmed when too many machine devices attempt to upload their data. In this thesis, we propose a two-hop slotted ALOHA-based cluster random access method to improve the performance of MTC. We introduce a clustering geometry model for machine locations, and define a clustering metric to measure the clustering of the locations. We examine the impact of different parameters on the system performance. Our results show that as the machine locations become more clustered, the overall performance metrics are improved. It can also be seen that the energy consumption can be dramatically decreased using the proposed scheme due to the simplified access procedure proposed.
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Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used 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.
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zhao-spatialclusteringinslottedalohatwohoprandom.pdf | 2023-05-05 | Public | Download |