Antenna Selection and Device Grouping for Spectrum-Efficient UAV-Assisted IoT Systems

Abstract

Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) systems have been implemented for over a decade, from transportation to military surveillance, and is proven worthy of integration in the next generation of wireless protocols. Though UAVs have immense potential, they have major drawbacks when it comes to real-world implementation, such as energy capacity, loss of signal quality, and spectrum limitations. To overcome these challenges, integration of UAVs with spectrum-efficient techniques including cognitive radio (CR) and non-orthogonal multiple access (NOMA) has been proposed. In this paper, we incorporate transmit-antenna selection (TAS) into an underlay CRNOMA network, which provides additional benefits through employing multiple-antenna-selection approach at the UAV with the goal of better serving the ground NOMA devices. The links associated with the multi-antenna UAV are theoretically assumed to experience Nakagami-m fading distribution. We also emphasize the degraded performance caused by imperfect successive interference cancellation (SIC) when decoding signals at the ground NOMA devices. The closed-form expressions for the proposed model are derived to evaluate two main performance metrics, namely the outage probability and the ergodic capacity. Monte-Carlo simulations are performed to analyze the performance of the system in different scenarios. We observe that the power allocation factors for the devices in a group and the altitude of UAV have a noticeable impact on the performance of the system. Furthermore, the increase in the number of antennas at the UAV can complement these effects and further improve the system performance.