Joint optimization of HBS 3D trajectory and power allocation transmission for energy- efficient NOMA downlink in 6G networks

Abstract

The rapid growth of devices in the Internet of Everything (IoE) poses significant challenges in achieving high-capacity and energy-efficient connectivity in 6G wireless networks. Hovered base stations (HBSs) provide a promising solution for enhancing physical-layer performance; however, their mobility and inefficient transmit power allocation (PA) may increase interference and energy consumption. In this paper, a multi-HBS-based NOMA transmission framework is proposed for downlink 6G networks, where each HBS serves multiple IoE devices. The proposed framework jointly optimizes HBS three-dimensional (3D) trajectory, transmit PA, and dynamic decoding order execution to maximize total sum rate (TSR) and total energy efficiency (TEE) under minimum data-rate constraints. The resulting optimization problem is non-convex due to the coupling among optimization variables and constraints. To efficiently solve this problem, a low-complexity and fast-converging hybrid optimization framework integrating a developed genetic algorithm and modified gray wolf optimization is adopted. Simulation results demonstrate that the proposed framework significantly outperforms existing benchmark schemes, achieving up to 23.6% improvement in TSR and 35.8% improvement in TEE. These results confirm the effectiveness of the proposed joint optimization framework for improving overall network performance.