Anastassia Gharib, Waleed Ejaz, and Mohamed Ibnkahla
Spectrum sensing is believed to be a prominent solution to spectrum scarcity caused by the presence of a large number of devices, particularly in Internet of Things (IoT) applications. Providing spectrum access to all of these devices is one of the paramount issues for I systems. Nevertheless, IoT poses several challenges for spectrum sensing that have yet to be overcome. Conventional spectrum sensing techniques have to be carefully modified to be applied to sophisticated and scalable IoT systems. In this paper, an analysis of spectrum sensing for IoT and its possible architecture configurations are presented. We provide an extensive list of challenges associated with spectrum sensing for IoT systems. Focus is given to distributed learning approaches known as incremental, consensus, and diffusion learning in the context of IoT. We further present a case study on cooperative spectrum sensing for IoT systems, where we propose an optimized distributed solution based on diffusion learning. Finally, simulation results demonstrate that the proposed solution improves detection performance and aggregate secondary IoT network throughput, and can minimize hardware complexity for secondary IoT users.
A. Gharib, W. Ejaz and M. Ibnkahla, “Distributed Spectrum Sensing for IoT Networks: Architecture, Challenges, and Learning,” IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 66-73, June 2021.
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