Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

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Gharib, Anastassia




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.


Engineering - Electronics and Electrical




Carleton University

Thesis Degree Name: 

Master of Applied Science: 

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Engineering, Electrical and Computer

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Theses and Dissertations

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