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.
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.
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.
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 (1−25Gbps), and environmental conditions demonstrate: (1)71%spectrum efficiency improvement (reaching6.2bps/Hz), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to1000+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.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
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.
In the recent year, quantum computing and communications (QCC) represented two revolutionary technologies that are rapidly developing and integrating to interconnect heterogeneous quantum devices within the so-called Quantum Internet (QI). Despite several investigations and developments have been performed on terrestrial quantum communications over typical optical fiber links, there are some limitations in terms of extremely high losses that can be faced by deploying several repeaters, which in turns involves impractical costs for network management, especially in terms of forwarding procedures. Quantum Satellite Networks (QSNs) is capable to overcome the constraints of terrestrial optical networks, i.e., the remarkable attenuations over long distances which makes the intercontinental communications unaffordable. The recent technological developments in terms of quantum satellite communications motivated our investigation on an ad hoc quantum satellite backbone interconnecting quantum on Earth Servers in order to achieve an unprecedented computational capacity. Our aim is to investigate existing constellations, spanning from Low Earth Orbit (LEO) up to micro-satellites, to properly derive some guidelines for designing an efficient backbone. Specifically, the focus is on evaluating the impact of forwarding approaches on the end-to-end capacity (i.e., the entanglement generation rate) to achieve a trade-off between performance and cost.
The rapid advancement of quantum computing provides unprecedented opportunities for the optimization of multiple-input, multiple-output (MIMO) systems. This paper investigates the efficacy of harnessing quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, for the optimization of MIMO systems. The paper establishes a theoretical framework that maps MIMO optimization problems onto quantum circuits, ensuring a reduction in computational complexity compared to classical algorithms. The proposed methodology is validated through a series of numerical simulations. Notable improvements are observed in both convergence speed and the quality of the optimized solutions. Furthermore, the synergy of quantum computing and MIMO presents new frontiers for reducing interference, enhancing throughput, and ensuring robustness against environmental uncertainties. These results affirm the potential of integrating quantum computing techniques in MIMO system optimization, heralding a transformative era for wireless communication paradigms.