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.
Emerging technologies including wireless power transfer (WPT), integrated sensing and communication (ISAC), and fluid antennas (FAs), have significantly advanced the capabilities and performance of modern satellite communication systems. This paper investigates an FA-assisted integrated sensing, communication, and power transfer (ISCPT) framework for low Earth orbit (LEO) satellite networks, which operates in two phases: (i) an energy-transfer and target-sensing phase (Phase I), and (ii) an information-transmission phase (Phase II). Specifically, in Phase I, a space solar power satellite (SSPS) transmits a dual-functional waveform to simultaneously charge multiple LEO satellites and illuminate a sensing target, while in Phase II, these LEO satellites coordinately serve multiple ground user equipments (UEs) leveraging the harvested energy. We formulate a sum-rate maximization problem subject to the SSPS’s transmit power constraint, LEO satellites’ energy harvesting and sensing requirements, UEs’ information rate demands, and the FAs’ movable regions. To tackle the highly-coupled and non-convex optimization problem, a three-stage alternating optimization (AO) algorithm is proposed, which decomposes it into resource allocation, SSPS-side FA placement, and LEO-side FA placement subproblems. In particular, the resource allocation subproblem is reformulated by adopting the Cauchy–Schwarz inequality and semidefinite relaxation (SDR), and is efficiently tackled via the successive convex approximation method. The two FA placement subproblems are addressed leveraging trust-region-based optimization. Simulation results validate the superior performance gains of the proposed algorithm over seven benchmarks and demonstrate that FAs can enhance multi-functional wireless services by adjusting inter-channel diversity according to service types. Notably, a non-trivial trade-off arises among FAs-enabled multi-functional services requiring distinct channel characteristics, as FAs cannot simultaneously provide optimal channel conditions for all services.
Satellite-terrestrial integrated networks with simultaneous wireless information and power transfer (SWIPT) provide wide-area connectivity and sustainable service support, but they also face serious security challenges due to the broadcast nature of satellite links and the possibility that an energy receiver may act as potential eavesdropper. To address this issue, this paper proposes a secure precoding design for a high-altitude platform (HAP)-assisted rate-splitting multiple access (RSMA) architecture under a quasi-static transmission model. Specifically, a cooperative direct and relay transmission (CDRT) framework is developed, in which the HAP assists the satellite transmission to improve the physical layer security for multi-user SWIPT services. By assuming the energy receiver near the target user as potential eavesdropper, we formulate a sum secrecy rate maximization problem subject to energy harvesting and transmit power constraints. To transform the original nonconvex optimization problem into a tractable convex problem, we employ techniques such as first-order Taylor expansion approximation, rank-one constraint relaxation, successive convex approximation, and semidefinite relaxation. Numerical results demonstrate that the proposed CDRT-RSMA scheme significantly outperforms conventional non-orthogonal and time-division multiple access schemes in terms of security performance.
The rapid development of uncrewed aerial vehicle (UAV) technology has brought security threats, which has motivated the development of integrated sensing and communication (ISAC) systems to address these issues. However, the power supply limitations of distributed ISAC nodes and the demand for covert signal transmission in adversarial scenarios remain unresolved. This paper focuses on covert transmission optimization for an integrated energy-harvesting, sensing and covert communication (IEHSCC) system, consisting of energy-harvesting (EH) nodes with switchable sensing/jamming modes and an ISAC base station (BS) to avoid detection by adversarial warden Willie. The minimum detection error probability (DEP) of Willie is derived, and the IEHSCC system’s sensing and covert transmission performance are calculated. Concerning the channel state information (CSI) estimation error, a joint optimization problem for EH node beamforming, BS beamforming, power allocation and EH node mode selection to maximize covert transmission rate is formulated. The problem is decomposed into subproblems and solved via S-Procedure and semi-definite relaxation (SDR) algorithm. An iterative water-filling algorithm is proposed to select EH modes. Simulation results show the proposed algorithm achieves fast convergence and effectively improves the covert transmission rate in the IEHSCC system. Increasing the number of antennas of EH node can increase covert rate, while excessive nodes or BS power degrades system performance, requiring rational configuration in practice.
Frequency Modulation (FM) broadcasting signals, as widely available signals of opportunity, hold significant potential for positioning in satellite-denied environments. However, existing FM-based positioning methods predominantly rely on either knowledge-based traditional machine learning or purely data-driven deep learning approaches, which often fail to balance model complexity and positioning accuracy. Moreover, interference from high-power Wireless Power Transfer (WPT) systems further degrades positioning performance, thereby constraining the application of WPT technology in satellite-denied scenarios. To address these challenges, this paper proposes the lightweight FM-based positioning method (LFMPM), a hybrid knowledge-and-data-driven positioning framework specifically designed for efficient positioning in WPT scenarios. The LFMPM leverages knowledge to guide the design of a data preprocessing strategy, a lightweight convolutional neural network, and a position-aware loss function. This design effectively suppresses WPT interference and efficiently learns signal-position patterns, achieving superior positioning performance with lower complexity. To validate the practical performance of the proposed method, extensive experiments were conducted against baseline models. Experimental results demonstrate that LFMPM achieves higher positioning accuracy than baseline models and exhibits excellent robustness under WPT interference scenarios. Moreover, the complexity of LFMPM is approximately two orders of magnitude lower than that of purely data-driven approaches, which enables efficient deployment in resource-constrained WPT systems.
Quantum key distribution (QKD) enables information-theoretically secure key exchange between two remote parties. However, practical implementations often are vulnerable to device imperfections, particularly in the state preparation. In this paper, we propose a reconfigurable QKD scheme based on arbitrary pure states. By reducing the number and accuracy of required quantum states, our approach simplifies implementation while enhancing robustness against practical imperfections. Within this framework, the communicating parties can dynamically reconfigure their system, adaptively selecting a subset of states based on the raw key, to implement various QKD protocols. We prove that the security of the proposed scheme can be reduced to that of standard QKD protocols with mutually unbiased bases and full quantum state preparation, thereby inheriting the established security proofs of existing QKD protocols. To the best of our knowledge, the proposed scheme imposes the loosest and most straightforward requirements on state preparation, making it the most simplified QKD scheme to date. Notably, this reconfiguration provides greater resilience against state preparation flaws and can be seamlessly integrated into current systems without requiring any hardware modifications. Our work bridges the gap between theoretical security and practical implementation, paving the way for more reliable QKD deployments in real-world scenarios.
Integrating the ocean segment is essential to realizing the sixth-generation (6G) vision of space-air-ground-ocean (SAGO) networks, yet progress remains constrained by limited connectivity and poor energy sustainability. To address these challenges, we develop an underwater omnidirectional simultaneous lightwave information and power transfer (SLIPT) architecture with two coupled components. First, we propose a harmonic-roundness framework for omnidirectional SLIPT cellular architecture, linking array geometry to coverage uniformity, capacity, and fairness. Second, using a photovoltaic-receiver model that characterizes energy harvesting and small-signal communication, we derive the energy-harvesting and communication Pareto frontier and its achievability proof. Together, these two components enable a practical underwater SLIPT cellular architecture for alignment-free simultaneous energy and data transfer. Experimentally, a hexagonal omnidirectional transmitter used as a cellular base station attains 98.15% roundness. With the omnidirectional base station and a photomultiplier tube receiver, a 5-m underwater link delivers 142.3 Mbps. With a 450-nm laser and a photovoltaic panel, a 5-m link achieves 100 Mbps while simultaneously harvesting 67.75 mW, an order-of-magnitude improvement over prior work.With the omnidirectional base station and a photovoltaic panel over a 0.5-m underwater channel, the link simultaneously sustains 13 Mbps and harvests 0.02 mW. An orthogonal frequency division multiple access (OFDMA) demon-stration with two users yields a Jain’s index of 0.85, providing initial experimental support for equitable multiuser performance. These results support harmonic-roundness-driven SLIPT with photovoltaic receivers as a promising cellular architecture for self-power underwater networking and as a potential building block for future SAGO 6G networks.
Wireless Power Transfer (WPT) enables sustainable voice services in Non-Terrestrial Networks (NTN), yet it introduces a critical interdependence between information and energy. High-power charging could degrade communication quality, while high-quality semantic codecs increase energy consumption. To navigate this trade-off, we propose an online joint adaptation mechanism for NTN voice services that adjusts WPT power levels and semantic codec bitrates dynamically. We formulate an optimization objective to maximize long-term perceptual voice quality under queueing stability, energy sustainability, and receiver-side protection constraints. Our approach uses the Lyapunov optimization to decompose this stochastic problem into per-slot decision-making. These decisions remain nonlinear because the voice-quality response is unknown and safety constrained. To address this, our safe low-rank bilinear upper confidence bound algorithm, termed SR-BiUCB, integrates a conservative safe-action filter and frequent-directions sketching, decoupling the information-energy interdependence in a computationally efficient manner. We implement a multi-rate semantic codec on a live Tiantong narrow-band voice link and emulate the WPT-induced impairment. Prototype traces and trace-driven simulations show that SR-BiUCB achieves over 22% higher lower-tail perceptual voice quality than representative state-of-the-art benchmarks, while maintaining bounded reported backlogs and near-zero receiver-side protection exceedances. These results support safe, high-quality, energy-sustainable voice services in future NTN.
Affine frequency division multiplexing (AFDM) has emerged as a robust multi-carrier modulation candidate for high-mobility communications. This paper investigates an AFDM-based integrated sensing and communications (ISAC) framework for unmanned aerial vehicle (UAV) links within space-air-ground integrated networks (SAGINs). A key contribution of this work is the novel design of the cyclic prefix and postfix (CPP) for AFDM, which is specifically tailored to accommodate wireless power transfer (WPT) requirements, thereby supporting simultaneous information and energy transmission. Specifically, the base station exploits the reflected echoes of AFDM signals to estimate sensing parameters, including the position, velocity, and angle of mobile users. To optimize the communication link, we propose an intelligent adaptive modulation and coding (AMC) decision-making process. A specialized dataset is established, integrating physically interpretable metrics—such as distance, velocity, and angle—with historical AFDM channel state information characterized by its unique chirp-domain representation. Subsequently, a hybrid deep learning architecture, designated as CNN-LSTM, is developed to establish a unified evaluation framework. This framework leverages the feature extraction capabilities of convolutional neural networks (CNNs) to process the spatial-temporal correlations of the AFDM channel, while utilizing Long Short-Term Memory (LSTM) networks to capture the long-term temporal dependencies of UAV trajectories. Simulation results demonstrate that the proposed modeling approach achieves superior separability and robustness, aligning closely with the ideal adaptive envelope while exhibiting enhanced cross-trajectory generalization capabilities compared to conventional methodologies.
Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computing-control (SC3) closed loops. However, the limited battery capacities of devices within these SC3 loops constrain operational duration and degrade control efficiency, particularly in remote or post-disaster scenarios. To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC3 closed loops. In this paper, we investigate a wireless-powered SC3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC3 closed loops. By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance. Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem. The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized. We recast the problem into a more tractable form and develop an iterative algorithm to solve it. For the special case of a single loop, we further analyze the properties of optimal solutions in energy-limited scenarios to provide insights for practical parameter configuration. Simulation results demonstrate the performance gains of the proposed scheme.
We consider a private hypothesis testing scenario, including both symmetric and asymmetric testing, based on classical data samples. The utility is measured by the error exponents, namely the Chernoff information and the relative entropy, while privacy is measured in terms of classical or quantum local differential privacy. In this scenario, we show a quantum advantage with respect to the optimal privacy-utility trade-off (PUT) in certain cases. Specifically, we focus on distributions referred to as smoothed point mass distributions, along with the uniform distribution, as hypotheses. We then derive upper bounds on the optimal PUTs achievable by classical privacy mechanisms, which are tight in specific instances. To show the quantum advantage, we propose a particular quantum privacy mechanism that achieves better PUTs than these upper bounds in both symmetric and asymmetric testing, specifically under stringent privacy constraints and small discrete data alphabet sizes ranging from 3 to 9. The proposed mechanism consists of a classical-quantum channel that prepares symmetric informationally complete (SIC) states, followed by a depolarizing channel.
We propose QCLight (Quantum-Classical Light), a unified quantum-classical optical communication system that simultaneously supports classical data transmission and quantum key distribution (QKD) over a shared free-space optical (FSO) link. The system employs a novel Bennett–Brassard 1984 (BB84) with pulse position modulation scheme that embeds polarization-encoded qubits and time-bin-encoded classical symbols into a common symbol frame, enabling physical-layer integration and hardware reuse. A full transceiver architecture is developed, combining polarization-resolved encoding with time-multiplexed photon detection to jointly decode quantum and classical using a shared single-photon detection chain. To assess system performance under realistic conditions, we develop a comprehensive analytical framework that captures key impairments including photon-counting noise, detector dead time, atmospheric turbulence (modeled via Gamma-Gamma fading), and pointing errors. Closed-form expressions are derived for critical metrics such as the symbol error rate (SER), achievable mutual information, quantum bit error rate (QBER), and secret key rate (SKR), incorporating dead-time-limited Poisson statistics and fading distributions via Meijer-G functions. Theoretical predictions are validated through Monte Carlo simulations, confirming that QCLight enables secure and efficient dual-purpose communication in photon-starved UAV and satellite-ground FSO scenarios.
This paper investigates an unmanned aerial vehicle (UAV)-assisted holographic integrated data and energy transfer (IDET) system operating under the finite blocklength (FBL) regime. A comprehensive analytical framework is developed, in which a UAV equipped with a reconfigurable holographic surface (RHS) hovers above the users to enable simultaneous data and energy transfer. To better capture practical system characteristics, the data transfer link is modeled as an RHS-induced spatially correlated fading channel, whereas the energy transfer link is characterized by a deterministic spherical-wave line-of-sight channel together with a non-linear energy harvesting model. In the considered FBL setting, two beamforming strategies, namely the data-centric (D-C) and energy-centric (E-C) designs, are investigated to respectively favor data transmission and energy transfer. Under these two designs, closed-form or tractable approximate closed-form expressions are derived for the average decoding error probability and average achievable rate of the data user (DU), as well as the energy outage probability and average harvested energy of the energy user (EU). Moreover, the achievable rate-energy (R-E) region is characterized through a Pareto-optimal beamforming formulation. By exploiting the structure of the considered problem, a low-complexity reduced-dimensional beamforming algorithm is developed to efficiently construct the Pareto boundary. The obtained results reveal the fundamental performance limits of UAV-assisted holographic IDET under short-packet transmission and provide useful physical and algorithmic insights into the achievable R-E region. In particular, increasing the blocklength significantly improves the harvested energy and enhances the achievable rate and decoding reliability, whereas the marginal rate gain gradually diminishes once the blocklength is sufficiently large, revealing an important blocklength-rate-energy-latency tradeoff. Finally, Monte Carlo simulations validate the accuracy of the theoretical analysis.
We study a two-phase, sensing-enhanced simultaneous wireless information and power transfer (SWIPT) framework for a multi-user system under position uncertainty. Phase-I dedicates a fraction of the frame to cooperative sensing that refines user positions; Phase-II performs SWIPT using the refined statistics. We define the information-energy (I-E) region to capture the fundamental tradeoff between sensing-induced enhancement and SWIPT duration, and then optimize it via bisection-based phase scheduling and robust beamforming. The sensing-induced enhancement is quantified by the derived closed-form Cram & eacute;r-Rao Lower Bound (CRLB), and then optimized via a direction alignment water-filling procedure. For the SWIPT phase, we derive the ergodic capacity upper bound (ECUB) and the ergodic power havest (EPH) to characterize, respectively, the communication and power transfer efficiencies that harness the sensing enhancement. Accordingly, we devise a closed-form eigenbeamforming for power transfer; a quadratically constrained quadratic program (QCQP)-based beamforming for communication; and a QCQP-based beamforming with successive convex approximation (SCA) for SWIPT. Numerical results demonstrate substantial robustness and efficiency gains over conventional beamforming across a wide range of uncertainty levels.
