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International Conference on Communications and Broadband Networking (ICCBN)

Quantum-Enhanced Attention for Federated Resource Allocation in LEO Satellite Networks

Muhammad Ahsan Nazir, Ming Chen, Abdul Aziz, Muhammad Aurangzeb, Umar Farooq

Published Nov 27, 2025• 0 citations
Publisher

Abstract

Dynamic Radio Resource Allocation (RRA) in Low Earth Orbit-Space Integrated Ground Networks (LEOSIGNs) presents unprecedented challenges due to extreme dynamism, sparse resource availability, and heterogeneous quality-of-service requirements across satellite constellations like Starlink, OneWeb, and Kuiper. This paper introduces a paradigmshifting Quantum-Optimized Deep Learning with Federated Edge Intelligence (QO-FEDI) framework that synergistically integrates: (1) Variational Quantum Circuits (VQC) for decomposing the NP-hard RRA problem into parametric quantum optimization, (2) federated attention-based predictive models across satellite clusters for privacy-preserving demand forecasting, and (3) adaptive edge-compute resource coordination for sub- 100 ms allocation latency. Novel contributions include: mathematical formulation of quantum cost Hamiltonians capturing interference constraints as entanglement patterns, federated learning topology with Byzantine-robust aggregation, and spatio-temporal attention mechanisms for orbital trajectory-aware predictions. Extensive evaluations across 15 realistic LEO scenarios with varying satellite configurations (32-256 satellites), traffic patterns ($1-25 \text{Gbps}$), and environmental conditions demonstrate: (1$) 71 \%$spectrum efficiency improvement (reaching$6.2 \text{bps} / \text{Hz}$), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to$\mathbf{1 0 0 0 +}$satellite constellations. Comparative analysis with 8 state-of-the-art methods validates near-optimal solution quality (96.1 % vs. theoretical bounds). Real-world testbed deployment on constellation simulator validates practical applicability and deployment readiness for commercial satellite operators.

DOI
10.1109/iccbn67217.2025.11447817

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Deep-space medical surveillance necessitates decentralized AI frameworks that are resilient to communication latency and resource scarcity. This study introduces QFedAstro, which integrates variational quantum circuits with federated learning for astronaut health anomaly detection across heterogeneous mission profiles. The architecture employs five synergistic modules: quantum-classical hybrid optimization (QCHO) navigating non-convex loss landscapes via superposition-inspired parameter updates; adaptive mission intelligence networks (AMIN) reconfiguring layer weights based on gravity-radiation-isolation vectors; temporal-spatial health embedding (TSHE) capturing physiological interdependencies through graph attention; privacy-preserving biometric fusion (PPBF) applying mission-calibrated differential noise (ϵ = 0.5, δ = 10 -5 ); and cross-mission knowledge crystallization (CMKC) enabling inter-environment transfer learning. Validation across 150-astronaut simulations spanning ISS, Lunar, Martian, and Deep Space contexts yields 96.73% accuracy—2.61% above centralized baselines—while achieving 147x privacy amplification and 68% bandwidth reduction. Paired t-tests confirmed significance (p < 0.001, Cohen’s d = 2.41). The framework challenges conventional privacy-utility tradeoffs, demonstrating that quantum augmentation enables the concurrent optimization of fidelity and sovereignty in mission-critical federated systems.
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Recent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by stringent communication and coordination constraints. In this study, we propose orbital QFL, termed orb-QFL, a novel quantum-assisted Federated Learning framework tailored for Low Earth Orbit (LEO) satellite constellations. Distinct from conventional FL paradigms, termed orb-QFL operates without centralized servers or global aggregation mechanisms (e.g., FedAvg), instead leveraging quantum entanglement and local quantum processing to facilitate decentralized, inter-satellite collaboration. This design inherently addresses the challenges of orbital dynamics, such as intermittent connectivity, high propagation delays, and coverage variability. The framework enables continuous model refinement through direct quantum-based synchronization between neighboring satellites, thereby enhancing resilience and preserving data locality. To validate our approach, we integrate the Qiskit quantum machine learning toolkit with Poliastro-based orbital simulations and conduct experiments using Statlog dataset.
As Low Earth Orbit (LEO) satellite mega constellations continue to be deployed for satellite internet and recent successful experiments in satellite-based quantum entanglement distribution emerge, a natural question arises: How should we coordinate transmissions and design scalable scheduling policies for a quantum satellite internet? In this work, we consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations. We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard. We then propose four heuristic algorithms and evaluate their performance for Starlink mega constellation under various amount of resources and placements of the ground stations. We find that the maximum number of receivers necessary per ground station grows very slowly with the total number of deployed ground stations. Our proposed algorithms, leveraging optimal weighted b-matching and the global greedy heuristic, outperform others in entanglement distribution rate, entanglement fidelity, and handover cost metrics. While we develop these scheduling algorithms, we have also designed a software system to simulate, visualize, and evaluate satellite mega-constellations for entanglement distribution.
Low Earth orbit (LEO) satellite constellations are in the spotlight during this pivotal time of developing high-speed communications not only for provisioning non-terrestrial connectivity but also for establishing in-space backhauling solutions. However, the technological evolution in this direction faces many challenges especially in the framework of coexisting with other communication systems due to the spectrum scarcity and the aggregated interference. Recently, the concept of cell-free massive multiple-input multiple-output (MIMO) has been applied to the LEO constellations for ameliorating both energy and spectral efficiencies as well as improving interference mitigation. In this paper, we propose a quantum-based activation strategy for LEO satellites based on user demands using the cell-free massive MIMO communication scheme. Specifically, a binary integer linear optimization problem is formulated and solved using the quantum approximate optimization algorithm (QAOA), which provides tangible quantum advantages through swiftly finding the optimal solutions. The performance of the proposed technique is evaluated via simulations and compared to an adaptive power control scheme. It has been shown that approximately 45%, in average, of the LEO satellite nodes can be switched-off during various traffic load instances. Deactivating the redundant nodes would not only enhance the system energy efficiency but also reduce the computational workload at the constellation and minimize the inter-satellite interference.