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Performance Analysis Of Cognitive Radio Networks With Diversity And Faded Channels

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posted on 24.05.2021, 09:23 authored by Sattar J. Hussain
This dissertation presents new approaches for cognitive radio networks that combat fading effects and improve detection accuracy. We propose an advance framework for performance analysis of cooperative spectrum sensing over non-identical Nakagami- A detect-amplify-and-forward strategy is proposed to mitigate bandwidth requirements of relaying local observations to a fusion center. The end-to-end performance of a relay-based cooperative spectrum sensing over independent identically distributed Rayleigh fading channels is also investigated in this dissertation. Specifically, we aim to incorporate sensing time, end-to-end SNR, and end-to-end channel statistic into the performance analysis of cooperative CR networks. We also propose a cluster-based cooperative spectrum sensing approach to overcome the bandwidth limitations of the reporting links. The approach reduces the number of reporting terminals to a minimal reporting set and replaces the global fusion center by a local fusion center to mitigate the destructive channel conditions of global relaying channels. A new approach is proposed to select the location of the local fusion center using the general center scheme in graph theory. We aim to show that multipath fading on relaying channels yields similar performance degradations as multipath fading on sensing channels. With the detect-amplify-and forward strategy, refraining the heavily faded relays improves the detection accuracy. A gain of 3 dB is achieved by switching from amplify-and-forward strategy to detect-amplify-and-forward strategy with 3 cooperative users. Compared to the non-cooperative spectrum sensing, a gain of up to 8 dB is achieved with 4 cooperative users and equal gain combining receiver. Similar experimental set up but with selection combining receiver, a gain of 5 dB is achieved.





Doctor of Philosophy


Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type