Scholarlink

<p>Network-based Global Navigation Satellite Systems (GNSS) support critical infrastructure and autonomous systems, but centralized processing hubs limit scalability, resilience, and latency. We present a global-scale decentralized GNSS architecture that jointly estimates receiver states and produces network-wide satellite-correction products. Modeling the receiver network as a time-varying graph, we formulate information-mixing schedule design as training a deep linear neural network whose layers are doubly stochastic matrices constrained by graph topology. Masked Sinkhorn-Knopp projections are integrated with backpropagation to preserve topology-awareness and feasibility during training. The learned schedule supports an online gradient-tracking diffusion strategy, allowing receivers to perform local inference from their own observations while exchanging compact messages to reach consensus on satellite corrections and self-localization. When receivers are reference stations, the resulting consensus products can be broadcast for precise point positioning (PPP) and precise point positioning–real-time kinematic (PPP–RTK) services. Experiments using hundreds of globally distributed IGS stations show that the proposed method matches centralized baselines while converging faster and reducing communication overhead relative to existing decentralized approaches. Overall, by reframing decentralized GNSS as a networked signal processing problem, this work highlights the potential of decentralized optimization, consensus-based inference, and graph-aware learning as effective tools for operational satellite navigation.</p>

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Decentralized GNSS at global scale via graph-aware diffusion adaptation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Xue Xian Zheng,
  • Xing Liu,
  • Tareq Y. Al-Naffouri

摘要

Network-based Global Navigation Satellite Systems (GNSS) support critical infrastructure and autonomous systems, but centralized processing hubs limit scalability, resilience, and latency. We present a global-scale decentralized GNSS architecture that jointly estimates receiver states and produces network-wide satellite-correction products. Modeling the receiver network as a time-varying graph, we formulate information-mixing schedule design as training a deep linear neural network whose layers are doubly stochastic matrices constrained by graph topology. Masked Sinkhorn-Knopp projections are integrated with backpropagation to preserve topology-awareness and feasibility during training. The learned schedule supports an online gradient-tracking diffusion strategy, allowing receivers to perform local inference from their own observations while exchanging compact messages to reach consensus on satellite corrections and self-localization. When receivers are reference stations, the resulting consensus products can be broadcast for precise point positioning (PPP) and precise point positioning–real-time kinematic (PPP–RTK) services. Experiments using hundreds of globally distributed IGS stations show that the proposed method matches centralized baselines while converging faster and reducing communication overhead relative to existing decentralized approaches. Overall, by reframing decentralized GNSS as a networked signal processing problem, this work highlights the potential of decentralized optimization, consensus-based inference, and graph-aware learning as effective tools for operational satellite navigation.