Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems
Public Deposited- Resource Type
- Creator
- Abstract
Cognitive radio (CR) can be seen as a solution to spectrum scarcity caused by Internet of Things (IoT), where multi-band cooperative spectrum sensing (CSS) is the key. However, conventional CSS techniques have to be improved to fulfill IoT requirements. One of the main challenges is cooperative secondary users (SUs) scheduling to sense channels. Hence, we propose a novel heterogeneous multi-band multi-user CSS (HM2CSS) scheme. HM2CSS allows heterogeneous SUs to sense multiple channels and selects cooperative SUs in two stages. Then, diffusion learning is used to exchange locally sensed information among cooperative SUs and decision on channels availability is made. Simulation results illustrate that HM2CSS improves detection performance and CRN throughput compared to existing schemes. It provides fair energy consumption for CSS for all channels. Nevertheless, HM2CSS has its own drawbacks yet to overcome. Hence, future research insights are given to overcome the drawbacks of HM2CSS and existing multi-band CSS schemes.
- Subject
- Language
- Publisher
- Thesis Degree Level
- Thesis Degree Name
- Thesis Degree Discipline
- Identifier
- Rights Notes
Copyright © 2018 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.
- Date Created
- 2018
Relations
- In Collection:
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
gharib-distributedlearningbasedcooperativespectrum.pdf | 2023-05-05 | Public | Download |