Learning-Assisted Secure Transmission Design for Low-Earth Orbit Satellites With High-Altitude Platform Jamming

Abstract

Low-earth orbit (LEO) satellite systems provide global coverage and low latency, yet their rapid motion induces severe channel variations, rendering full-service-period physical layer security design prohibitively costly and inefficient. To address this issue, we investigate a high-altitude platform (HAP)-assisted secure transmission scheme, where the HAP transmits artificial noise (AN) to enhance satellite communication security and improve user fairness. Specifically, we formulate a minimum secrecy rate maximization problem with joint power allocation and the HAP beamforming vector as optimization variables. Due to the non-convexity nature of the problem, solving it for a single time slot is computationally intensive, and directly extending the optimization across all time slots incurs prohibitive overhead. Therefore, we propose a learning-based framework that first optimizes key time slots and then trains a kernel ridge regression (KRR) model. In particular, the number of training samples is adaptively adjusted based on the minimum distance between eavesdroppers and legitimate users, while a warm-start strategy is used to accelerate optimization over the sampled slots. The trained KRR model learns the mapping from satellite angular positions to secure transmission parameters and predicts suitable solutions for the remaining time slots. Simulation results show that the proposed method achieves secrecy and user fairness performance comparable to per-slot optimization with much lower computational complexity, offering a scalable and efficient solution for secure LEO satellite communications.