IOT-inspired spectrum sharing in UAV-assisted NOMA networks with deep learning approach

Abstract

Energy and spectral efficiency (SE) of Internet of Things (IoT) networks can be improved by integrating energy harvesting, cognitive radio, and non-orthogonal multiple access (NOMA) techniques, while unmanned aerial vehicles (UAVs), on the other hand, are a quick and adaptable entity for improving the coverage performance. We conducted a study to assess the performance of an overlay cognitive radio- NOMA (OC-NOMA) system assisted by UAVs using an energy harvesting-based cooperative spectrum sharing transmission (I-CSST) scheme, inspired by the IoT. Herein, an energy-constrained UAV-borne secondary node harvests radio-frequency energy from the primary source (PS) and uses it to send both its own information signal and the primary information signal using the NOMA approach. We consider the impact of the imperfect successive interference cancellation (iSIC) in NOMA and the distortion noises caused by hardware impairments (HIs) in signal processing, which are unavoidable in real-world systems. We obtain the complicated expressions of outage probability (OP) for primary and secondary IoT networks using I-CSST scheme under heterogeneous Rician and Nakagami-m fading channels.