Intelligent Reflecting Surface Enhanced Multi-UAV NOMA Networks
Xidong Mu, Yuanwei Liu, Li Guo, Jiaru Lin, H. Vincent Poor

TL;DR
This paper introduces an IRS-enhanced multi-UAV NOMA network framework, jointly optimizing UAV placement, IRS reflection, and NOMA decoding to significantly improve network sum rate performance.
Contribution
It proposes a novel joint optimization framework for multi-UAV NOMA networks with IRS, including a BCD-based algorithm to solve the complex problem efficiently.
Findings
IRS enhances channel quality and reduces interference in multi-UAV NOMA networks.
The proposed scheme outperforms OMA-IRS and NOMA without IRS in sum rate.
Optimized UAV placement amplifies the benefits of NOMA through flexible decoding orders.
Abstract
Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternatingly solved by exploiting the penalty method and the successive convex approximation technique. The proposed…
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