3-D Fast Adaptive Beamforming Method for Satellite Uniform Planar Array Based on Improved Transformers

Low Earth Orbit (LEO) satellite communication constellations provide crucial support for the future of ubiquitous connectivity. However, the high dynamic nature of LEO satellites’ relative positions affects the stability and reliability of inter-satellite communication links while imposing stricter latency requirements. Fast adaptive beamforming technology offers a viable solution to address these challenges. To achieve fast adaptive beamforming, this paper proposes a two-step beamforming solution based on a Transformer neural network model and explores the feasibility of applying meta-heuristic algorithms for hyperparameter optimization in neural network (NN) models. Experimental results demonstrate that the angle-of-arrival predictor optimized using the Polar Lights Optimizer (PLO), a meta-heuristic algorithm inspired by the aurora phenomenon, significantly outperforms the unoptimized model, with its error consistently remaining within the half-power beamwidth of the proposed planar antenna array. Meanwhile, the beamforming accuracy improves by 15.2% compared to other NN-based models, and in all test scenarios, our algorithm reduced the average response time by approximately 82.1% compared to the null-steering beamforming algorithms. This study provides a novel solution for achieving low-latency, high-reliability 3D beamforming in LEO inter-satellite communication, integrating both speed and accuracy, thereby contributing to the advancement of 6G and future ubiquitous connectivity.