2026· IEEE Transactions on Network Science and Engineering· Vol 13, pp. 10715-10731· 0 citations· 60 references
Computer Science
Abstract
For applications such as global broadband access, emergency communications, and integrated space-air-ground networking, 6G networks impose requirements on satellite communication systems. These requirements include high reliability, low latency, and proactive intelligent control. However, low Earth orbit (LEO) satellite networks have highly time-varying topologies. Wireless optical intersatellite links (WOISLs) are also vulnerable to space debris blockage and sun outage. Conventional reactive routing schemes rely on current network states and are difficult to cope with abrupt future changes in link risk. To address this issue, this paper proposes Digital-Twin and World-Model driven Risk-Aware Routing (DT-WM-RAR), a proactive risk-aware routing framework that fuses digital twins (DTs) and world models (WMs). First, a multi-source space risk model is developed for WOISLs. It converts space debris blockage and sun outage into unified link-level risk features. Then, a DT platform for 6G LEO satellite networks is constructed to synchronize constellation topology, link performance, traffic load, and space-environment risk states in real time. On this basis, a latent-space WM is introduced to infer short-term future changes in link risk, load, and connectivity. A Soft Actor-Critic (SAC) policy network is used to generate risk-aware link costs. The service forwarding path is finally obtained through shortest-path search. Simulation results show that the proposed DT-WM-RAR improves the service success rate, reduces rerouting frequency, and decreases end-to-end delay in both low-risk and high-risk scenarios. In particular, under high-risk and heavy-load conditions, it better avoids potentially failed links and improves the reliability and stability of 6G satellite networks in complex space environments.
The proposed Digital Twin Satellite Network (DTSN) framework connects the physical satellite network with a synchronized virtual twin and combines real-time telemetry, Integrated Sensing and Communication (ISAC), predictive intelligence, and resilience-oriented control and successfully isolates compromised nodes and triggers proactive network reconfiguration.
This study focuses on future Non-Terrestrial Networks (NTN) integrated with Terrestrial Networks (TN) for future 5G/6G systems. NTN envisions a 3D architecture, where Low Earth Orbit (LEO) satellite networks will play a key role in bridging the digital divide, complementing the gradual terrestrial 5G/6G rollout concentrated in high-density and high-traffic areas, by ensuring service continuity across broad geographic regions and providing coverage in case of emergencies or in remote areas. In this context, we address networking issues for the integration and federation of Terrestrial and Non-Terrestrial Network (T-NTN) in line with the IMT-2030 vision, focusing on interoperability, spectrum coexistence, unified control and management, and service continuity. Federation is a complementary approach to integration that enables distinct satellite systems to cooperate through agreements, potentially unified satellite terminals, and common resource management. We show that system federation significantly enhances both latency performance and connectivity robustness compared with non-federated LEO architectures. This paper also investigates the challenges and possible solutions for adopting the Open-RAN architecture for T-NTN, including routing options for mega-LEO systems, edge intelligence, and energy efficiency as critical elements for sustainability. Finally, we address the security, privacy, and resilience aspects of federated T-NTN architectures with emphasis on zero-trust, secure routing, trustworthy edge intelligence, Post-Quantum Cryptography (PQC), and Quantum Key Distribution (QKD).
Sarath Babu, Victor Baños-Gonzalez, Mario Cordina et al.· 0 citations
Recently, the large-scale Low Earth Orbit (LEO) satellite networks are emerging as a cornerstone of future 6G systems, promising global coverage and massive throughput. However, the complex space environments, such as solar outage and ionospheric scintillation, can lead to regional link impairments that severely undermine connectivity. These adverse conditions can significantly compromise end-to-end paths, which drastically diminishes the reliability of the whole connection and eventually degrade the overall transmission performance. To address these gaps, we propose a novel Agentic AI-driven multipath transmission approach to ensure robust and stable data delivery in LEO satellite networks. It features two key innovations: 1) Intent-based multipath routing scheme: Leveraging a hybrid domain-based architecture, distributed agents perceive regional network states to autonomously establish robust multipath routes aligned with specific intent objectives. 2) Fine-grained Multipath QUIC (MPQUIC) congestion control algorithm: Derived from a multipath fluid model, this algorithm performs fine-grained congestion balancing across all sub-paths, and ensures throughput and TCP-friendliness simultaneously in unstable LEO satellite environments. We evaluate the proposed approach through extensive experiments in the Kuiper K3 shell network simulated via UltraStar. Experimental results demonstrate that this approach significantly outperforms other benchmarks in large-scale LEO satellite networks.
Mengyang Zhang, Yu Sun, Xiaoyu Liu et al.· IEEE Transactions on Cogniti...· 0 citations
Due to cross-domain heterogeneity, unpredictable traffic patterns, and limited real-time visibility into network conditions, mobile backbone networks are increasingly experiencing performance degradation. This study presents a novel cross-domain AI-driven telemetry pipeline that facilitates intelligent, high-performance routing across the core network, transport, and radio access domains in order to address these issues. The suggested approach captures fine-grained network information, such as latency, link utilization, queue depth, and packet loss, in real time by combining streaming telemetry with a uniform cross-layer data aggregation paradigm. Using this telemetry, a lightweight AI-powered predictive routing engine forecasts congestion and uses adaptive path selection to dynamically improve routing choices. The suggested method greatly increases routing efficiency and network resilience by employing the Ant colony optimization (ACO) algorithm to provide improved proactive and context-aware traffic steering, in contrast to conventional reactive routing protocols. Within the mobile backbone, the innovation is found in the smooth integration of AI-driven decision intelligence and cross-domain telemetry. Experiments show significant gains in throughput stability, end-to-end latency, and resource usage, confirming the usefulness of the suggested framework for next-generation mobile networks.
: Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge, UAVs, and satellites; (2) a Reliability-Aware Multi-Objective Optimization Framework (RA-MOOF) that introduces explicit reliability guarantees through cross-layer link reliability modeling, node availability estimation, and smooth reliability proxy functions. Addressing the heterogeneous communication characteristics of the SAGIN architecture, this paper establishes a complete cross-layer delay model and composite reliability metrics. The reliability formulation is defined under explicitly stated conditional-independence assumptions, and the proposed smooth constraint terms are treated as surrogate CMDP costs rather than exact hard chance-constraint guarantees. Extensive experiments in a SAGIN simulation environment demonstrate that the proposed method improves the task completion rate by 3.8%, reduces average latency by 11.1%, and increases system reliability by 3.9% compared to state-of-the-art benchmarks. The optimization-only RA-Opt baseline is used as a non-real-time optimization reference for assessing reliability-aware offloading decision quality, while deployment-time decision-latency comparisons are interpreted primarily among learned inference policies. Comprehensive ablation studies and statistical validation across multiple random seeds confirm the contributions of each component, while cross-layer offloading decision analysis verifies the effectiveness of the method across different network layer selections.
Fei-Yan Bu, Zheng Wang, Yong Pan et al.· Computers, Materials & C...· 0 citations
An Adaptive Hybrid Routing Framework that integrates RPL and GPSR under a machine learning (ML)-driven decision engine that offers a resilient and energy-efficient routing solution for next-generation smart grid neighborhood area networks is proposed.
Teslim Komolafe, Enoch Owoeye, Samuel A. Adegbola et al.· Energy Science, Engineering,...· 0 citations
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