Satellite-terrestrial integrated networks with simultaneous wireless information and power transfer (SWIPT) provide wide-area connectivity and sustainable service support, but they also face serious security challenges due to the broadcast nature of satellite links and the possibility that an energy receiver may act as potential eavesdropper. To address this issue, this paper proposes a secure precoding design for a high-altitude platform (HAP)-assisted rate-splitting multiple access (RSMA) architecture under a quasi-static transmission model. Specifically, a cooperative direct and relay transmission (CDRT) framework is developed, in which the HAP assists the satellite transmission to improve the physical layer security for multi-user SWIPT services. By assuming the energy receiver near the target user as potential eavesdropper, we formulate a sum secrecy rate maximization problem subject to energy harvesting and transmit power constraints. To transform the original nonconvex optimization problem into a tractable convex problem, we employ techniques such as first-order Taylor expansion approximation, rank-one constraint relaxation, successive convex approximation, and semidefinite relaxation. Numerical results demonstrate that the proposed CDRT-RSMA scheme significantly outperforms conventional non-orthogonal and time-division multiple access schemes in terms of security performance.
Mengyan Huang, Xingwang Li, Chengjun Jiang et al.· IEEE Journal on Selected Are...· 0 citations
Low earth orbit (LEO) satellite networks have emerged as a key enabler for delivering real-time and global services to distributed terrestrial nodes, particularly in remote regions. To preserve data privacy, federated learning (FL) provides a decentralized framework for advancing artificial intelligence (AI) in complex tasks. However, the efficiency of FL is constrained by high and imbalanced energy consumption, which limits its practical deployment. To address these challenges, an energy-aware FL framework that integrates knowledge distillation (KD) with task offloading is proposed, where KD is performed at both the FL server and client devices or direct-connected satellites using public datasets. The energy consumption balancing problem is formulated as a quadratic unconstrained binary optimization (QUBO) model. To achieve computational efficiency and parallelism, the quantum approximate optimization algorithm (QAOA) is employed to solve the problem with both the mixing and cost Hamiltonians derived and the corresponding quantum circuit designed. In a FL framework over a LEO satellite network comprising 40 satellites and 10 FL clients, the proposed method reduces energy consumption by approximately 26.4%, achieves improved energy balance with a weighted variance of approximately 4.93 and maintains high accuracy of 0.95 in a vehicle classification task, compared with the traditional FL method.
Pengxiang Qin, Dongyang Xu, Lei Liu et al.· IEEE Transactions on Cogniti...· 0 citations
