The emerging vision of sixth-generation (6G) wireless networks is to achieve seamless and sustainable global connectivity. Low Earth Orbit (LEO) satellites provide a cost-effective means to realize this goal; however, large Doppler shifts, severe propagation loss, and multipath interference pose significant challenges to 6G LEO satellite communications. To address these issues, this paper proposes an energy-efficient Affine Frequency Division Multiplexing with Index Modulation (AFDM-IM) based communication scheme for 6G LEO satellite systems, which enhances spectral efficiency while minimizing additional power consumption. The proposed framework encompasses both up-link and down-link models together with their corresponding receiver designs. Specifically, beamforming is introduced, for the first time, to mitigate multipath effects in both transmission links. Furthermore, estimation algorithms for the complex gain are developed, and minimum mean square error criterion and maximum likelihood criterion are respectively employed for information symbol detection in the up-link and down-link. Simulation results demonstrate that the proposed scheme achieves a 6.25% improvement in spectral efficiency and a 31.25% reduction in energy consumption. Furthermore, the mean square error in the up-link and the bit error rate in the down-link reach approximately -45 dB and 10−4, respectively, under specific propagation conditions. These results confirm the effectiveness and superiority of the proposed AFDM-IM-based design compared to conventional methods, enabling high spectral and energy efficiency for 6G LEO satellite systems.
Quantum key distribution and post-quantum cryptography represent the backbone for tackling security challenges in the quantum era. Although each of them has its own shortcomings, their practical implementation raises the question of their possible combination and synergy. In this context, the paper considers mixing established cryptographic keys at key management layer, using both approaches to optimize the available key resources in a quantum network, also balancing the provided security. We provide analytical model, security analysis, and optimal contribution of keys in the mixed cryptographic key. Extensive simulation results demonstrate the validity of this approach in point-to-point and network environments.
With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks. This paper proposes an energy-scheduling approach that jointly optimizes energy efficiency, task completion rate, and task waiting time for power transfer from low Earth orbit satellites to terrestrial mobile user devices (UDs). To address scheduling challenges caused by satellite and UD mobility and channel uncertainty from stochastic propagation effects, we decompose the problem into three subproblems within a three-layer predictive framework: 1) a state prediction layer forecasts UD and satellite states; 2) an interaction mapping layer uses a graph neural network (GNN) to model energy transfer efficiency; and 3) a decision-making layer determines the energy allocation plan. Distinct machine learning (ML) methods are tailored to each layer. To balance the competing objectives, we adopt a multi-objective reinforcement learning (MORL) technique that scalarizes them into a weighted-sum reward, transforming the multi-objective problem into a tractable single-objective problem. We further introduce a multi-agent deep learning model integrating self-attention with multi-agent proximal policy optimization (MAPPO) to improve objective balancing. Simulation results show that the proposed approach achieves a better overall trade-off than baseline methods, maintaining competitive task completion rates and energy efficiency while reducing task waiting times, and remains robust under highly variable conditions.
Variational quantum circuits (VQCs) are central to near-term quantum computing, yet their practical deployment is severely hindered by noise. While existing error mitigation methods, such as zero-noise extrapolation (ZNE), typically assume static noise, real noisy intermediate-scale quantum (NISQ) systems exhibit dynamic, time-varying noise that remains largely unaddressed. To overcome this critical gap, our work introduces a novel adaptive noise mitigation framework for VQCs that integrates ZNE with contextual multi-armed bandits (CMAB), enabling dynamic, context-aware selection of circuit-folding levels based on ansatz parameters (e.g., depth, parameter count) and the evolving noise environment. Unlike fixed-fold or heuristic ZNE, our approach uses online adaptation to improve the accuracy of ZNE and reduce redundant quantum circuit executions. Our extensive simulations and experiments on real quantum hardware reveal the following important properties: (i) deeper VQCs accumulate noise, degrading accuracy and increasing the number of quantum circuit executions; (ii) ZNE restores estimator fidelity when the folding level is chosen appropriately; and (iii) CMAB-guided folding cuts quantum circuit execution round trips by up to 40%, bytes exchanged by up to 35%, and end-to-end cost by up to 30% under a 10 Mbps budget, with up to 6.9% higher estimator fidelity (CIFAR-10, depth 3, noise band η = 0.05), versus fixed-fold and grid-search ZNE. These results demonstrate substantial performance gains over existing noise mitigation methods, underscoring the effectiveness of our design in supporting robust noise mitigation for VQCs. The source code is also publicly released to support reproducibility.
