A broader coverage of low earth orbit (LEO) satellite networks has been an object of research interest in recent years and will remain to be of keen interest to both start-ups and established companies in the future. However, achieving optimal coverage remains a challenge today, as the satellites dynamically move along their orbit, requiring frequent constellation size and beamsteering adjustments. Such frequent adjustments and the number of variables that need to be optimized result in a high computational complexity. Aiming to attend to these concerns, the present study proposes a quantum adaptive learning (QAL) as a potential solution for coverage optimization of stochastic geometry-based LEO satellite networks with low computational complexity by taking advantage of quantum computing and adaptive learning. The QAL scheme utilizes quantum computing and a feedback mechanism to improve the learning network design and parameters for higher accuracy with low computational complexity. As a study case, considering the binomial point process (BPP) distribution of contact distance between satellites and user terminals, we use the QAL scheme to optimize the satellites' constellation size and their corresponding beamsteering at each time step to achieve maximum coverage probability. To evaluate performance, the simulation results of the QAL scheme are compared with those of quantum machine learning (QML), which lacks feedback and adaptive mechanisms. The results show that the proposed QAL outperforms QML by achieving higher coverage probability, higher accuracy, and faster convergence.
A broader coverage of low earth orbit (LEO) satellite networks has been an object of research interest in recent years and will remain to be of keen interest to both start-ups and established companies in the future. However, achieving optimal coverage remains a challenge today, as the satellites dynamically move along their orbit, requiring frequent constellation size and beamsteering adjustments. Such frequent adjustments and the number of variables that need to be optimized result in a high computational complexity. Aiming to attend to these concerns, the present study proposes a quantum adaptive learning (QAL) as a potential solution for coverage optimization of stochastic geometry-based LEO satellite networks with low computational complexity by taking advantage of quantum computing and adaptive learning. The QAL scheme utilizes quantum computing and a feedback mechanism to improve the learning network design and parameters for higher accuracy with low computational complexity. As a study case, considering the binomial point process (BPP) distribution of contact distance between satellites and user terminals, we use the QAL scheme to optimize the satellites' constellation size and their corresponding beamsteering at each time step to achieve maximum coverage probability. To evaluate performance, the simulation results of the QAL scheme are compared with those of quantum machine learning (QML), which lacks feedback and adaptive mechanisms. The results show that the proposed QAL outperforms QML by achieving higher coverage probability, higher accuracy, and faster convergence.
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.
Satellite-based quantum key distribution (QKD), especially relying on low Earth orbit (LEO) satellites, overcomes the geographical limitations of fiber-based QKD and enables global secure communications. However, existing QKD link establishment schemes for LEO quantum satellite networks fail to fully consider practical scenarios (e.g., multi-layer constellations as well as heterogeneous satellite–ground and inter-satellite QKD links) along with realistic constraints (e.g., different illumination conditions and the need to avoid frequent link switching). To address this gap, this work focuses on the adaptive establishment of satellite–ground and inter-satellite QKD links in single- and dual-layer LEO quantum satellite networks. We describe the network structures of single- and dual-layer constellations, as well as formulate the corresponding network model, secret key rate model, and secret key volume model. To realize adaptive QKD link establishment, we develop two integer linear programming (ILP) models and design three heuristic algorithms. The two ILP models are denoted as ILP-G and ILP-R, respectively, where ILP-G aims to maximize the generated key volume, and ILP-R is dedicated to optimizing global key resource balance by prioritizing QKD links with lower remaining key volume. The three heuristic algorithms are designed based on maximum key rate (MaxKR), maximum generated key volume (MaxGKV), and minimum remaining key volume (MinRKV). Simulation results verify the applicability of the proposed approaches across single- and dual-layer LEO quantum satellite networks. Comparative evaluations against two benchmarks, i.e., random fit (RF) and first fit (FF), show that ILP-G and MaxGKV achieve the largest cumulative key volume, while ILP-R and MinRKV outperform the other approaches in key volume distribution, quantum satellite availability, and total number of established QKD links. Specifically, the cumulative key volume of ILP-G is 44.0% and 33.8% higher than that of RF in single- and dual-layer LEO quantum satellite networks, respectively. Furthermore, the dual-layer constellation generates more key resources, with the cumulative key volume reaching approximately five times that of the single-layer constellation. It also exhibits a more balanced global key resource distribution, where key resources are distributed across a broader set of node pairs rather than concentrated on a limited subset of nodes.
An increase in earth observation missions has increased the demand of efficient design and optimization of satellite constellations. Maximizing coverage of the target while effectively utilizing the limited orbital resources is one of the critical design challenges for complex combinatorial optimization problems. The maximal covering location problem (MCLP), serves as a base for orbital coverage modeling, is NP-hard and computationally intractable for large-constellation instances. Using heuristics, metaheuristics, and mixed-integer linear programming, classical solvers have achieved optimal or near-optimal results, yet their scalability is limited as the problem size increases. Quantum computing advancements, including the quantum approximate optimization algorithms, offer a potential solution to NP-hard combinatorial optimization problems. Current quantum hardware limitations, such as low qubit counts and circuit depth, restrict solutions for small-scale instance problems. To address this challenge, this paper proposes a scalable quantum optimization framework for MCLP in satellite constellation design. A decomposition-based quantum methodology is proposed, in which large MCLP instances are partitioned into subgraphs by classical decomposition, optimized independently via quantum optimization circuits, and combined using quantum reconstruction strategies. Computational results across different constellation sizes reveal better scalability in less time while maintaining competitive coverage performance compared to classical solvers.
Satellite communications (SatCom) are envisioned as a critical enabler of 6G networks, enabling seamless global coverage by integrating terrestrial infrastructures with multi-layered satellite constellations. Among these, the integration between geostationary (GEO) and low Earth orbit (LEO) satellite networks is particularly attractive, as they combine the broad coverage of GEO satellites with the low latency and high capacity of LEO systems. Within this context, we address the resource allocation problem for LEO satellite through a joint design of beam size and transmit power, while accounting for GEO interference constraints, residual Doppler frequency offsets, and frequency reuse strategies. The objective is to maximize the spectral efficiency of LEO system operating in multi-beam GEO-LEO networks. Motivated by the limitations of classical deep reinforcement learning (RL) in such dynamic orbital settings and the potential of quantum RL for accelerated convergence, we propose a hybrid solution that exploits quantum acceleration during offline training and subsequently exports the learned policy into a classical representational format for onboard LEO satellite deployment. A fully quantum deep deterministic policy gradient framework with variational quantum circuit-based actor and critic is developed, along with a neural network-based policy translator for classical inference. To the best of our knowledge, this is the first deployment-ready quantum RL framework in SatCom, offering efficient offline training, reduced retraining latency, and practical deployment compatibility with existing LEO satellite hardware.
The expansion of satellite-based quantum networks requires adaptive routing mechanisms that can sustain entanglement under dynamic orbital and atmospheric conditions. Conventional schemes, often tailored to static or idealised topologies, fail to capture the combined effects of orbital motion, fading, and trust variability in inter-satellite links. This work proposes an \textit{adaptive entanglement-aware routing framework} that jointly accounts for orbital geometry, atmospheric attenuation, and multi-parameter link evaluation. The routing metric integrates fidelity, trust, and key-rate weights to maintain connectivity and mitigate loss from turbulence and fading. Monte Carlo simulations across multiple orbital densities (ρ=10−6~km−3) and environmental regimes, standard atmosphere, strong turbulence, and clear-sky LEO show up to a 275\% improvement in key generation rate and a 15\% increase in effective entanglement fidelity over existing adaptive methods. The framework achieves sub-linear path-length scaling with network size and remains robust for fading variances up to σfade=0.1, demonstrating strong potential for future global quantum constellations.
Low Earth Orbit (LEO) satellite communications are pivotal for next-generation 6G wireless systems but suffer from severe channel impairments, including large Doppler shifts and time-varying Shadowed-Rician fading. These dynamic conditions, coupled with channel aging, significantly degrade the reliability of classical receivers. While deep learning-based solutions offer improved performance, their high computational complexity poses a challenge for resource-constrained satellite payloads. This paper proposes a parameter-efficient hybrid quantum-classical receiver based on a Quantum Convolutional Neural Network. In the remainder of this paper, the receiver is referred to as the Hybrid QCNN. The model is enhanced with an iterative data re-uploading mechanism to maximize the expressivity of limited qubits. To improve optimization stability in hybrid quantum-classical training, this paper introduces a Dual Learning Rate optimization strategy. In this setting, gradient magnitudes can be small and can differ across parameter groups, so the strategy assigns distinct learning rates to the quantum and classical parameters. This paper validates the proposed model using a high-fidelity channel simulator that integrates the 3GPP TR 38.811 elevation-dependent K-factor model with Loo's Shadowed-Rician fading model, dynamically capturing realistic fading characteristics across different elevation angles. Simulation results demonstrate that the proposed Hybrid QCNN achieves bit error rate performance that is nearly identical to that of the classical CNN baseline. The performance gap remains below 1% across varying channel conditions, while the number of parameters is reduced by approximately 89%. Furthermore, in an ablation study, the iterative re-processing mechanism achieves up to a 26.9% relative BER reduction at an SNR of 10 dB compared with a standard quantum pooling baseline on the considered test set. This result indicates improved feature preservation during quantum pooling.