Dynamic Power Allocation in NOMA-Based Satellite-Terrestrial Networks: A Customized Particle Swarm Optimization Approach

Nonorthogonal multiple access (NOMA)-based satellite-terrestrial systems can offer significant improvements in spectral efficiency. However, improper power allocation within these systems can result in resource waste, user unfairness, and performance degradation. This article presents an innovative power allocation strategy for NOMA-based satellite-terrestrial communication systems, achieved through the development of an advanced optimization algorithm. Specifically, based on a representative multiuser system model that incorporates various real-world factors, such as polarization conversion loss, hardware imperfections, channel estimation errors, and imperfect successive interference, we derive the expressions for the signal-to-interference-plus-noise ratio and outage probability. Then, we propose a customized particle swarm optimization (PSO) algorithm, termed Dirichlet-initiated, sorted, and heuristically constrained PSO (DISH-PSO) algorithm, that integrates Dirichlet-based initialization, decoding-priority-aware sorting, heuristic constraint handling, and exponentially decaying inertia weight into a unified framework. We design this customized PSO algorithm to optimize dynamic power allocation for minimizing outage probability. We demonstrate its enhanced convergence and robustness under complex communication conditions. The simulation results demonstrate that the proposed DISH-PSO algorithm consistently achieves a lower system outage probability and faster convergence than representative population-based, structured nonconvex, and alternating optimization baselines, enabling the system to reach high-quality solutions with fewer iterations.