Utilizing Partial Non-Orthogonal Multiple Access (P-NOMA) in Drone-Enabled Internet-of-Things Wireless Networks

Future drone-enabled Internet-of-Things (IoT) wireless networks have attracted considerable attention from industry and academia. Future drone-enabled IoT wireless networks are expected to enable the Internet of Everything and provide services with massive connectivity, heterogeneous quality of service, ultra-reliability, and higher throughput. Therefore, future drone-enabled IoT wireless networks necessitate more effective use of wireless resources and efficient interference management approaches. As a result, the multiple access techniques and the physical layer for wireless communication systems have been rethought and redesigned. This paper proposes utilizing the partial non-orthogonal multiple access (P-NOMA) in drone-enabled IoT wireless networks, where a single drone provides wireless coverage for a set of IoT devices. In P-NOMA, a portion of the channel is orthogonal, while the other is non-orthogonal for each IoT device. When using a non-orthogonal channel portion, an IoT device that receives high transmit power from the drone treats a signal of another IoT device as noise and quickly recovers its signal without using a successive interference cancellation (SIC) process. However, an IoT device that receives low transmit power from that drone must perform the SIC process on a non-orthogonal channel portion to recover its signal. The optimization problem in this research aims to find the maximum sum data rate of all IoT devices, considering the 3D placement of the drone, device pairing, and the parameters of P-NOMA. Finding the optimal solution to the optimization problem is challenging because of the NP-completeness of the formulated problem. Therefore, a decomposition framework is proposed to aid in solving it. Particularly, the optimization problem is decomposed into three subproblems: the 3D placement for the drone, device pairing, and P-NOMA parameters. Then efficient techniques are proposed to solve these subproblems. Simulation results verify the efficacy of utilizing P-NOMA in drone-enabled IoT wireless networks. Specifically, our results demonstrate that P-NOMA can boost the sum rate by 22%–28% compared with NOMA and by 83%–104% compared with OMA.