Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

This letter proposes to integrate multibeam satellite with terrestrial network for traffic offloading, and investigates the problem of joint resource allocation of satellite backhaul links and terrestrial user links, aiming to maximize the offloading energy efficiency. To address the mixed-integer and nonconvex problem, we decompose it into subproblems and propose an effective two-step algorithm. In the first step, the user links allocation for terrestrial BSs is solved by extended water-filling to maximize the BS sum rate while ensuring users’ QoS requirements. In the second step, the satellite subproblem under capacity constraint is converted into a convex subproblem iteratively by quadratic transform to obtain the near-optimal satellite beam power. Numerical simulations verify that our proposed algorithm outperforms benchmark algorithms in offloading energy efficiency by at least 12.1%.

This letter addresses the wireless resource scheduling challenge for high-density low-earth orbit (LEO) satellite constellations, catering to the demands of wide-area User Equipments (UEs). Aiming to maximize system capacity, we conceptualize the issue as a multi-branch bipartite graph matching problem with specially designed vertices, and propose an ‘access-right’ based two-step matching mechanism for satellite channel allocation (ARSMM). ARSMM runs in two steps. Firstly, the mechanism employs a Hungarian algorithm based on determining ‘access-right’ to obtain satellite-UE scheduling. Secondly, it introduces a matching adjustment algorithm based on three criteria, respectively, to accommodate the varying channel demands of the UEs. Numerical simulation results indicate that ARSMM markedly improves resource utilization efficiency and system capacity compared to the baseline algorithm. Moreover, it achieves near-parity with the Genetic Algorithm while significantly improving processing speed to millisecond-level efficiency.

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions $F_1\left(w\right)$ and $F_3\left(w\right)$ to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.