Lifetime Maximization for UAV-Enabled Cognitive-NOMA IoT Networks: Joint Location, Power, and Decoding Order Optimization

TL;DR

This paper proposes a joint optimization framework for UAV location, power, and decoding order to maximize the minimum lifetime of IoT devices in a cognitive NOMA network, addressing interference constraints and imperfect CSI.

Contribution

It introduces a novel joint optimization approach for UAV-enabled IoT networks with NOMA, including a globally optimal solution for small-scale and a low-complexity sub-optimal algorithm for large-scale scenarios.

Findings

Proposed method outperforms benchmark schemes in lifetime maximization.

Joint optimization improves interference management and energy efficiency.

Effective solutions for both small-scale and large-scale scenarios.

Abstract

This paper investigates a cognitive unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) network, where secondary/cognitive IoT devices upload their data to the UAV hub following a non-orthogonal multiple access (NOMA) protocol in the spectrum of the primary network. We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, transmit power, and decoding order subject to interference-power constraints in presence of the imperfect channel state information (CSI). To solve the formulated non-convex mixed-integer programming problem, we first jointly optimize the UAV location and transmit power for a given decoding order and obtain the globally optimal solution with the assistance of Lagrange duality and then obtain the best decoding order by exhaustive search, which is applicable to relatively small-scale scenarios. For large-scale scenarios, we propose a low-complexity sub-optimal algorithm by transforming the original problem into a more tractable equivalent form and applying the successive convex approximation (SCA) technique and penalty function method. Numerical results demonstrate that the proposed design significantly outperforms the benchmark schemes.