With great potential in providing global coverage and real-time service, recently, direct satellite-to-device (DS2D) communication has attracted considerable attention. However, how to effectively utilize the costly satellite resources to satisfy the on-demand massive connectivity remains a huge challenge. This paper proposes a cost-effective hetero-granular resource allocation framework that combines the advantages of ground-based scheduling and spaceborne scheduling. In specific, we aim to optimize both the cell-level and user-level scheduling in a beam-hopping system. The formulated optimization problem is first decomposed into a coarse-grained scheduling problem among cells using historical demand information on the ground station, and a fine-grained scheduling problem among users using real-time service demands on satellite. We solve the mixed-integer non-linear programming problem of coarse-grained scheduling with cross-entropy and quantum particle swarm optimization algorithms to find the global optimum, exploiting the adequate ground-based computational resources. The fine-grained scheduling problem is solved with a generalized-benders-decomposition-based algorithm to accommodate the limited spaceborne resources, which decouples power and bandwidth allocation based on a closed-form solution of optimal dual variables in the primal power allocation problem. Simulation results demonstrate that the proposed method effectively reduces the length of the waiting queue by up to 25.05% compared to the existing methods.

Space-air-ground integrated networks (SAGIN) combine the wide-area coverage of satellites with airborne platforms acting as relays, enabling efficient data delivery for large-scale Internet of Things (IoT) sensing applications. To further enhance transmission efficiency, this paper incorporates airborne onboard processing to reduce communication workloads and proposes an intelligent beam hopping strategy tailored to spatially uneven traffic demands. Specifically, we design a multi-agent deep reinforcement learning (MADRL)-based beam hopping framework, where satellite agents coordinate beam scheduling while considering spatial service heterogeneity and the diverse computational capacities of airborne platforms. To reduce the complexity introduced by individual task requirements, we integrate a summarized statistical profile of the computation tasks into the agent’s observation space, including average and maximum computation efficiencies across tasks. Simulation results demonstrate that the proposed scheme significantly accelerates convergence and reduces the data backlog by up to 80%, especially under scenarios with considerable heterogeneity in service demands and airborne platform computational capabilities.

This paper proposes an efficient backup routing scheme based on potential-minimized path first for Low Earth orbit mega-constellation network to solve the service interruption problem caused by satellite node failure, in which the potential-minimized path first strategy adopts the artificial potential field model to consider the network node congestion level and shortest path delay. This backup routing scheme optimizes both the primary and backup paths by weighting and summing the potentials of both paths. This weighting takes into account congestion and possible broken network nodes. It retains the property of distributed routing using potential functions to simplify complexity and signaling interactions. Simulation results show that the proposed backup routing scheme significantly reduces the outage time and data transmission delay, enhancing the reliability and stability compared to existing backup routing schemes such as intelligent backup multi-path ant colony routing algorithm, redundant multi-path routing algorithm and traffic-load-aware multipath routing algorithm.

Integrating feeder- and user-links in multi-beam satellite communications significantly enhances system flexibility but requires effective resource allocation to fully realize its potential. Multi-agent deep reinforcement learning (MADRL) has emerged as a scalable solution for beam hopping, by allowing each agent to optimize the transmission parameters for one beam. However, integrating feeder- and user-links introduces complicated dependencies, including resource competition between feeder- and user-links and data-flow coupling between uplinks and downlinks, dramatically deteriorating agent cooperation. To approach the performance limit, this paper introduces a dependency-elimination MADRL framework incorporating model decomposition, link decoupling, and novel agent-level collaboration mechanisms to allocate beams, power, and bandwidth with reduced complexity. Specifically, to facilitate beam-level agent reuse for complexity reduction under the heterogeneity of feeder- and user-links, characterized by data-flow aggregation and division, we decouple bandwidth allocation from the learning model. The uplink-downlink dependencies in the bandwidth allocation is then resolved using a generalized water-filling strategy based on the performance upper bounds. Furthermore, we improve agent cooperation efficiency through state and reward decomposition and a novel non-cooperation penalty. Evaluations show that our method improves the system performance by up to 57.7% compared to sota MADRL methods while reducing training complexity by more than 50%.

Satellite-aided airborne systems can enable data acquisition in remote areas for the intelligent transportation systems (ITS), leveraging the satellite coverage alongside the mobility and multifunctional capabilities of autonomous aerial vehicles (AAVs). However, due to the harsh environment, ensuring secure and timely task execution is complicated by uncertainties related to both channel conditions and eavesdropping threats. This paper proposes a two-stage optimization method to fully exploit AAVs’ flying, sensing, compressing, and transmitting capabilities for secure data acquisition under dual uncertainties. In the first stage, a deep reinforcement learning strategy is employed to optimize sensing and trajectory planning to explore the eavesdropping environment and balance computational and transmission demands. Building on the sensed information about the eavesdropping environment, the second stage focuses on minimizing task completion time through optimal resource allocation and hierarchical A* path planning, with the channel uncertainty addressed by incorporating an outage probability constraint. Simulations demonstrate that the proposed method can reduce data completion time by 35.3%, validating its effectiveness in uncertain environments.

Assessing the vulnerability of mega-constellation networks (MCNs) to large-scale failure is challenging, due to the time-varying three-dimensional constellation. In this paper, we propose a geometrical method to assess the vulnerability of the MCNs under large-scale geographical failure based on the standard +Gridinter-satellite connectivity pattern, using hop count as the metric. We first equivalently map the original constellation to a two-dimensional flat torus, offering a simplified and time-invariant representation of the geographical failure. The failure’s properties are then revealed on the torus to identify the blockage on the inter-satellite paths that might increase the hop count between end users. Under the blockage, a closed-form hop count expression is then obtained by deriving the explicit expressions of the optimal detours in the scaling limit. Finally, we propose a low-complexity hop count estimation algorithm that achieves a maximum relative error of 1.5% compared with the network simulation while being$10^{5}$times faster. Evaluations using traffic sourced from the 100 most populous cities show that the geographical failure covering the latitude corresponding to the MCNs’ inclination has the greatest impact on the hop count, whereas the MCNs are generally robust even under extreme scenarios.

Space-air-ground integrated network presents a promising solution to the challenge of accommodating large-scale device access while confronting sophisticated interference threats. Existing random access techniques neglect the dynamic interference environment and thus often struggle to realize anti-intelligent interference effectively. This paper proposes a novel approach to address this issue. By employing deep reinforcement learning algorithms, we utilize real-time feedback to adapt to the dynamic environment resulting from the time-varying interference strategy, as well as the involvement of various types of entities. Moreover, we propose a hierarchical reward function to improve the access efficiency. Simulation results show that our method reduces the congestion between users by up to 47% and enhances access efficiency is about 3.2 times compared with random access under malicious jammer intro conclusion finding.

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

The densely deployment of satellites enables the realization of direct-to-satellite Internet-of-things system with tremendous terminals through multi-satellite cooperation. Multi-user detection (MUD) based on cooperative satellite network can dramatically increase the detection performance. However, it is chained by the limited number of inter-satellite link (ISL) bandwidth resources. To cope with the stringent constraints on ISLs, we propose a novel auxiliary node (AN)-aided factor graph and the corresponding multi-user detection (MUD) algorithm named auxiliary node-cooperative message passing algorithm (AN-CMPA). Simulation results show that our proposed algorithm achieves only 0.5dB loss with 75% decreased information cost under effectively designed information filter criterion.

The reliability of low Earth orbit (LEO) mega constellation networks (MCNs) under large-scale geographical failure of satellites remains unrevealed. In this paper, we propose an algorithm to assess the connectivity, average latency and hop count by considering topology changes resulting from geographical failure, under different topology management. Numerical simulations are conducted based on the traffics source from end users distributed among the 100 most populous cities. The results show that the MCNs are generally reliable against geographical failure, as a geographical failure with a radius of 3000 km can at most disconnect 8% of the end users, while increasing the average hop count and latency of the remaining users by less than 10%. Also, topology reconfiguration after failure have a greater impact on the hop count than latency.