Reconfigurable-Intelligence-Surface-Assisted Opportunistic Multiple Access in UAV-IoT Networks

Abstract

Due to the advantages of flexible deployment and strong environmental adaptability of an unmanned aerial vehicle (UAV), UAVs serve as aerial base stations (BSs) to meet Quality of Services (QoSs) of ground users in Internet of Things (IoT) networks. Nonorthogonal multiple access (NOMA) is a potential technique in wireless communications area, which can significantly improve sum spectrum efficiency (SE) of systems. To avoid the limitation of perfect channel state information (CSI), opportunistic beamforming (OBF) is proposed, where a set of randomly generated weights is used to preprocess transmitted signals. Due to multiuser diversity gain introduced by OBF, OBF-NOMA systems can achieve approximate sum SE to conventional NOMA systems. Additionally, reconfigurable intelligent surfaces (RISs) are involved to overcome obstruction and obtain further improvements of SE. Therefore, this article proposes an RIS-aid OBF-NOMA system in UAV-IoT networks, where random weights and opportunistic phase matrix are, respectively, applied in a UAV and RIS. Statistical characteristics of equivalent channels are derived in Nakagami- $m~(m \geq 1)$ fading channels. Theoretical asymptotic analyses of SE and bit error rate (BER) are then presented. Furthermore, a nonconvex optimization problem is formulated to maximize SE. To obtain the optimal solution, we divide the problem into two suboptimization problems and apply a joint iterative algorithm. Numerical results show that the proposed method achieves a satisfactory SE without complex channel estimation and perfect CSI.