Problem
Research questions and friction points this paper is trying to address.
Age of Incorrect Information
Satellite-Ground Integrated Networks
Edge Computing
Robust Minimization
Mission-Critical Applications
Distributed Quantum-Assisted Robust AoII Minimization in Satellite-Ground Integrated Edge Networks
🤖 AI Summary
This study addresses the inaccuracy of edge node state estimation caused by link outages in Space-Air-Ground Integrated Networks (SAGIN), where traditional Age of Information (AoI) fails to distinguish benign delays from hazardous errors. We propose SENTINEL, a distributed hybrid quantum-classical framework that introduces the Age of Incorrect Information (AoII) metric and formulates the first network-level AoII minimization model for multi-node SAGIN. By decomposing update intervals for closed-form cost computation, the framework jointly optimizes update rates, satellite-ground associations, and bandwidth allocation via QUBO modeling, Ising mapping, distributed QAOA, and ADMM mechanisms. Simulations demonstrate that SENTINEL outperforms learning-based baselines, matches optimal policies in small-scale networks while approximating exact minimax references, and provides rigorous worst-case AoII safety guarantees under complete outage scenarios.
📝 Abstract
Mission-critical edge applications in 6G-and-beyond networks, such as autonomous systems, disaster response, and infrastructure monitoring, require that the edge decision-maker's estimate of a monitored process remain correct, not merely up to date. Satellite-ground integrated networks (SAGIN) often provide the only connectivity in infrastructure-limited or disaster-affected regions, yet satellite handover and shadowing interrupt links, during which the process may change state several times, leaving the edge node's estimate substantially wrong. Age of information (AoI) tracks only elapsed time and cannot distinguish a harmless delay from a dangerous error. We instead adopt the age of incorrect information (AoII), which penalizes both the duration and magnitude of estimation error, and formulate, to our knowledge, the first network-level, multi-node AoII minimization problem over SAGIN under stochastic handover and shadowing. We propose SENTINEL, a distributed hybrid quantum-classical framework that jointly schedules update rates, satellite-to-base-station associations, and bandwidth allocation to minimize the worst-case time-average AoII. Because AoII is history dependent, it resists per-slot optimization; a renewal-interval decomposition that separates source dynamics from channel disruption yields a closed-form AoII cost per inter-delivery interval. The resulting robust scheduling problem, VANGUARD, is formulated as a QUBO, mapped to an Ising Hamiltonian, and solved via distributed QAOA with ADMM-based coordination across satellite and ground domains. Every returned schedule carries a certified worst-case AoII over all disruption scenarios. Simulations show that SENTINEL outperforms learning-based and random baselines, matches a state-aware threshold policy in small networks while additionally providing a worst-case guarantee, and remains close to an exact minimax reference.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Age of Incorrect Information (AoII)
Quantum-Classical Hybrid Framework
Distributed QAOA
QUBO
Satellite-Ground Integrated Networks (SAGIN)
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