Deployment for NOMA-UAV Base Stations Based on Hybrid Sparrow Search Algorithm

Abstract

Auxiliary UAV base stations are expected to be the most promising schemes to fill the resource shortage and the blind spot of coverage of the ground base stations in the future. This paper proposes a hybrid sparrow search algorithm (SSA) to optimize the three-dimensional (3D) deployment of multi-UAV base stations with the objective of maximizing the sum log-rate utility. First, the air-to-ground channel model is established using the non-orthogonal multiple access (NOMA) technology to improve the spectrum utilization by considering the complex interference forms in multi-UAV scenarios, which is more compatible with the actual applications. Then, the multi-UAV deployment problem is described as an optimization problem to maximize the sum log-rate utility of all users. Finally, a hybrid SSA is proposed to implement the optimal 3D deployment of multi-UAV base stations. Specifically, in order to overcome the local optimization defects of the traditional SSA, the opposition-based learning (OBL) strategy is utilized to modify the initial position of the sparrows to promote particle diversity, and the sine-cosine algorithm is developed to optimize the update formula of the beggar sparrows by integrating the disturbance into the update process. Consequently, a hybrid SSA with higher search accuracy, stability and robustness is formed. The simulation results demonstrate that the proposed method outperforms the traditional methods in terms of the sum log-rate utility and throughput.