Search Results (430)

Hybrid Split Federated Learning (HSFL for short) in emerging 6G-enabled UAV networks faces persistent challenges in data protection, device trust management, and long-term participation incentives. To address these issues, this study introduces S-HSFL, a security-enhanced framework that embeds verifiable federated learning mechanisms into HSFL and incorporates digital-signature-based authentication throughout the device selection process. This design effectively prevents model tampering and forgery attacks, achieving a defense success rate above 99%. To further strengthen collaborative training, we develop a MAB-GT device selection strategy that integrates multi-armed bandit exploration with multi-stage game-theoretic decision models, spanning non-cooperative, coalition, and repeated games, to encourage high-quality UAV nodes to provide reliable data and sustained computation. Experiments on the Modified National Institute of Standards and Technology (MNIST) dataset under both Independent and Identically Distributed (IID) and non-IID conditions demonstrate that S-HSFL maintains approximately 97% accuracy even in the presence of 30% adversarial UAVs. The MAB-GT strategy significantly improves convergence behavior and final model performance, while incurring only a 10–30% increase in communication overhead. The proposed S-HSFL framework establishes a secure, trustworthy, and efficient foundation for distributed intelligence in next-generation 6G UAV networks. Full article

Due to the critical role of channel coding, convolutional code recognition has attracted growing interest, particularly in non-cooperative communication scenarios such as spectrum surveillance. Deep learning-based approaches have emerged as promising techniques, offering improved classification performance. However, most existing works focus on single-parameter recognition and ignore the inherent correlations between code parameters. To address this, we propose a novel framework named Hybrid Multi-Task Network (HMT-Net), which adopts multi-task learning to simultaneously identify both the code rate and constraint length of convolutional codes. HMT-Net combines dilated convolutions with attention mechanisms and integrates a Transformer backbone to extract robust multi-scale sequence features. It also leverages a Channel-Wise Transformer to capture both local and global information efficiently. Meanwhile, we enhance the dataset by incorporating a comprehensive sequence dataset and further improve the recognition performance by extracting the statistical features of the sequences. Experimental results demonstrate that HMT-Net outperforms single-task models by an average recognition accuracy of 2.89%. Furthermore, HMT-Net exhibits even more remarkable performance, achieving enhancements of 4.57% in code rate recognition and 4.31% in constraint length recognition compared to other notable multi-tasking frameworks such as MAR-Net. These findings underscore the potential of HMT-Net as a robust solution for intelligent signal analysis, offering significant practical value for efficient spectrum management in next-generation communication systems. Full article

This paper studies delay-aware formation control for (unmanned aerial vehicle) UAV swarms operating under realistic air-to-air communication latency. An attractive–repulsive distance-based potential-field (ARD-PF) controller is used as an expert to generate demonstrations for imitation learning in multi-UAV cooperative systems. By augmenting the training data with communication delay, the learned policy implicitly compensates for outdated neighbor information and improves swarm coordination during autonomous flight. Extensive simulations across different swarm sizes, formation spacings, and delay levels show that delay-robust imitation learning significantly enlarges the probabilistic stability region compared with classical ARD-PF control and non-robust learning baselines. Formation control performance is evaluated using internal geometric error, global offset, and multi-run stability metrics. In addition, a predictive delay–stability model is introduced, linking the maximum admissible communication delay to swarm size and inter-agent spacing, with low fitting error against simulated stability boundaries. The results provide quantitative insights for designing communication-aware UAV swarm systems under latency constraints. Full article

This study investigates performance overheads and security threats in 6th Generation Mobile Communication (6G) roaming environments, which are expected to enable services such as autonomous driving, smart cities, and remote healthcare that demand ultra-low latency and high reliability. To bridge the gap between standardization and real-world deployment, we built a realistic roaming testbed by separating the home and visited public land mobile networks (H-PLMN and V-PLMN) and simulating user equipment (UE) interactions. In this environment, we defined and measured roaming cost by comparing non-roaming and roaming procedures, and reproduced two Subscription Concealed Identifier (SUCI)-based denial-of-service (DoS) attacks: random generation and replay. Our experiments showed that intermediary functions such as the Security Edge Protection Proxy (SEPP) and Service Communication Proxy (SCP) introduced CPU/memory overhead and latency, highlighting performance degradation unique to roaming. Moreover, random SUCI generation concentrated load on the Authentication Server Function (AUSF) in the H-PLMN, whereas replay attacks distributed it across both the H-PLMN and the V-PLMN, consistently identifying the AUSF as a bottleneck. These findings demonstrate that roaming enlarges the attack surface and exposes vulnerabilities not fully addressed in current standards. We conclude that secure and reliable 6G roaming requires multi-layered defense strategies with inter-operator cooperation, providing empirical evidence to guide standardization and operational practice. Full article

Synapses, abundant in the brain, are structures needed for life. Our Introduction, based on the forms of such structures published few decades ago, helped in developing recent concepts of health and diseases. Growing axons govern their growth by cell-to-cell communication, axon guidance, and synapse orientations. The assembly of synapses requires the organization and function of pre-synaptic and post-synaptic neuronal terminals with a liquid–liquid phase, governed by Ca²⁺ responses of thin astrocyte domains. Upon synapse stimulation, the clefts expand up to several folds while pre- and post-synaptic thickness remains unchanged. In additional responses, neurons co-operate with astrocytes and extracellular vesicles (EVs), the latter dependent on extracellular and intracellular spaces. Astrocyte and microglia cells and/or EV secretions induce neurons by various effects including traveling changes. Pre-synaptic responses are defined as canonical if based on neurotransmitter release; non-canonical if they are without release and are discharged by EVs, not neurotransmitters. Health and diseases depend on other general properties, such as those defined molecularly. Among neurodegenerative diseases, attention is specified by various properties of Alzheimer’s and other diagnoses. Critical identifications can be due to astrocyte and microglia cells or multiple effects induced by EVs. At present, the complexity of therapies, although of limited success, is developing innovative initiatives. Full article

Restoring soil microbial functioning in reclaimed coal gangue soils is critical for ecosystem recovery, yet how different organic amendments, particularly industrial by-products, regulate bacterial communities remains unclear. Here, we tested three organic inputs—the residue after evaporation (RAE) from vitamin C production, Trichoderma inoculation, and cattle manure—applied alone and in combination in a photovoltaic agroforestry system on coal gangue spoil. Our results indicate that the treatment based on manure increased bacterial α-diversity and favored taxa associated with organic matter transformation, including Actinobacteria and Acidobacteriota, suggesting expanded niche partitioning in response to heterogeneous substrates and nutrients. RAE alone supported communities closer to non-manure controls but, when co-applied with manure, further enhanced network connectivity and the prevalence of positive associations, indicating strengthened cooperative interactions and functional redundancy. In contrast, RAE combined with Trichoderma in the absence of manure reduced diversity, and simplified the co-occurrence network, suggesting resource monopolization and antagonism. Overall, RAE acted as a key driver of microbial cooperation and potential ecosystem resilience, and RAE-based amendments, particularly when integrated with manure, appear to be effective strategies for improving soil microbial functionality in degraded coal gangue soils. Full article

Automatic modulation recognition (AMR) is crucial for signal interception and analysis in non-cooperative communication scenarios. To address the challenges of low signal-to-noise ratio (SNR) and model generalizability, this paper proposes a lightweight parallel network architecture that integrates convolutional layers, a channel attention mechanism, residual connections, and long short-term memory (LSTM) units. The model takes in-phase and quadrature (IQ) components of signals as inputs to jointly learn features for modulation scheme identification. Experiments are conducted on the expanded RML dataset to evaluate the model’s performance. Results indicate that the proposed network achieves recognition accuracy comparable to that of deep neural networks while requiring significantly fewer parameters. Furthermore, it demonstrates favorable generalization performance on other datasets, demonstrating its potential for efficient deployment under resource constraints. Full article

Simultaneous Wireless Information and Power Transfer (SWIPT) integrated with non-orthogonal multiple access (NOMA) offers a promising solution for energy-efficient Internet of Things (IoT) applications in the context of increasingly scarce spectrum resources. This paper addresses the energy efficiency (EE) maximization problem in a downlink cooperative SWIPT-NOMA network, where user cooperation is employed to mitigate the near-far effect and enhance network performance. We formulate the EE optimization problem for a multi-user scenario by jointly optimizing the transmission time, the power allocation ratio, and the transmission power of the near user in the cooperative SWIPT-NOMA network, and we propose a cooperative SWIPT-NOMA energy efficiency allocation algorithm. Firstly, the fractional programming problem for EE maximization is transformed into a more tractable form using the Dinkelbach method. Subsequently, the resource allocation variables are iteratively updated via variable substitution, successive convex approximation, and the Lagrangian dual method until the algorithm converges. Extensive simulations are conducted to evaluate the performance of the proposed algorithm under various conditions and to compare it with existing schemes. The proposed algorithm enhances network energy efficiency while ensuring user throughput, providing a more efficient resource allocation solution for wireless communication networks. Full article

The oral cavity acts as the anatomical gateway to the respiratory tract, sharing both microbiological and pathophysiological links with the lower airways. Although radiotherapy is a cornerstone treatment for lung cancer, reliable oral microbiome biomarkers for predicting patient outcomes remain lacking. We analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls across discovery and two validation cohorts via 16S rRNA sequencing. Healthy controls exhibited a significantly higher abundance of Streptococcus compared to patients (p = 0.049, p < 0.001, p < 0.001, respectively). The structure of the microbial community exhibited substantial dynamic changes during treatment. Responders showed enrichment of Rothia aeria (p = 0.027) and Prevotella salivae (p = 0.043), associated with prolonged overall survival (OS) and progression-free survival (PFS), whereas non-responders exhibited elevated Porphyromonas endodontalis (p = 0.037) correlating with shorter OS and PFS. According to Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) analysis, Akkermansia and Alistipes were nearly absent in non-responders, while Desulfovibrio and Moraxella were virtually absent in responders. A diagnostic model based on Streptococcus achieved area under the curve (AUC) values of 0.85 (95% CI: 0.78–0.91) and 0.99 (95% CI: 0.98–1) in the validation cohorts, and a response prediction model incorporating Prevotella salivae and Neisseria oralis yielded an AUC of 0.74 (95% CI: 0.58–0.90). Furthermore, in small cell lung cancer, microbiota richness and diversity were inversely correlated with Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.008, p < 0.001, respectively) and pro-gastrin-releasing peptide (ProGRP) levels (p = 0.065, p = 0.084, respectively). These results demonstrate that lung cancer-associated oral microbiota signatures dynamically reflect therapeutic response and survival outcomes, supporting their potential role as non-invasive biomarkers for diagnosis and prognosis. Full article

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

Multi-UAV search missions for prioritized targets under constrained communications suffer from weak communication-decision integration, limited global perception synchronization, and delayed mission response. This paper formulates multi-UAV collaboration search as a multi-objective optimization problem to balance communication overhead and search performance. A Cooperative Hierarchical Target Search under Constrained Communications (CHTS-CC) algorithm is proposed to address the problem. The algorithm incorporates a Cluster-Consistent Information Fusion with Event Trigger (CCIF-ET) method, which enables intra-cluster information fusion. When clusters connect, a single merge that applies joint weighting by cluster scale and uncertainty reduces communication overhead. Furthermore, a Dynamic Preemptive Task Allocation (DPTA) mechanism reallocates UAV resources based on target priority and estimated time of arrival (ETA), enhancing responsiveness to high-priority targets. Simulation results show that when all UAVs and communication links operate normally, CCIF-ET reduces total confirmation time by 8.73% compared to the uncoordinated baseline and maintains a 24.43% advantage during single-UAV failures. In scenarios with obstacles, failures, and dynamic targets, CHTS-CC reduced mission completion steps by 34.78%, 32.35%, and 55.45% compared to the non-allocation baseline. The average detection time for high-priority targets decreased by 28.48%, 29.41%, and 58.82%, respectively, demonstrating the effectiveness of the proposed algorithm. Full article

Nanosatellites in Low Earth Orbit (LEO) are an attractive platform for commercial and scientific missions, but their downlink capacity is constrained by bandwidth and by low ground station duty cycles (often under 5%). These limitations are particularly acute in heterogeneous cooperative networks, where operators seek to maximize “good-put”: the number of unique messages successfully delivered to the ground. In this paper, we present and evaluate three complementary algorithms for scheduling nanosatellite passes to maximize good-put under realistic traffic and link variability. First, a Cooperative Reception Algorithm uses Shapley value analysis from cooperative game theory to estimate each station’s marginal contribution (considering signal quality, geography, and historical transmission patterns) and prioritize the most valuable upcoming satellite passes. Second, a pair-utility optimization algorithm refines these assignments through local, pairwise comparisons of reception probabilities between neighboring stations, correcting selection biases and adapting to changing link conditions. Third, a weighted bidding algorithm, inspired by the Helium reward model, assigns a price per message and allocates passes to maximize expected rewards in non-commercial networks such as SatNOGS and TinyGS. Simulation results show that all three approaches significantly outperform conventional scheduling strategies, with the Shapley-based method providing the largest gains in good-put. Collectively, these algorithms offer a practical toolkit to improve throughput, fairness, and resilience in next-generation nanosatellite communication systems. Full article

This paper investigates a UAV-assisted wireless communication system that integrates optical wireless communication (LiFi) with conventional RF links to enhance network capacity in crowd-gathering scenarios. While the unmanned aerial vehicle (UAV) serves as a flying base station providing downlink transmission to mobile ground users, the study places particular emphasis on the role of LiFi as a complementary physical layer technology within heterogeneous networks—an aspect closely connected to optical and photonics advancements. The proposed system is designed for environments such as theme parks and public events, where user groups move collectively toward points of interest (PoIs). To maintain quality of service (QoS) under dynamic mobility, we develop a joint optimization framework that simultaneously designs the UAV’s flight path and resource allocation over time. Given the problem’s non-convexity, a block coordinate descent (BCD) based approach is introduced, which decomposes the problem into power allocation and path planning subproblems. The power allocation step is solved using convex optimization techniques, while the path planning subproblem is handled via successive convex approximation (SCA). Simulation results demonstrate that the proposed algorithm achieves rapid convergence within 3–5 iterations while guaranteeing 100% heterogeneous QoS satisfaction, ultimately yielding nearly 15.00 bps/Hz system capacity enhancement over baseline approaches. These findings motivate the integration of coordinated three-dimensional trajectory planning for multi-UAV cooperation as a promising direction for further enhancement. Although LiFi is implemented in free-space optics rather than fiber-based sensing, this work highlights a relevant optical technology that may inspire future cross-domain applications, including those in optical sensing, where UAVs and reconfigurable optical links play a role. Full article

Unmanned aerial vehicle (UAV) swarms, equipped for distributed sensing and rapid response, can form coalitions to undertake complex missions such as post-disaster relief, communication support, and payload delivery. However, typical coalition formation methods assign each UAV to a single task, limiting cross-task resource sharing. To address this, we investigate overlapping coalition formation (OCF) for UAV swarms, where a single UAV is permitted to participate in multiple coalitions, enabling resource reuse and reducing idleness. We formulate OCF as a multi-objective combinatorial optimization problem that jointly balances task fulfillment ratio, coalition synchronization deviation, and operational cost, while explicitly accounting for inter-coalition resource contention and execution precedence. Specifically, we first construct a hypergraph representation of UAVs and tasks and employ a hypergraph attention network to capture their high-order interactions. Next, we propose a structure-aware hierarchical value decomposition method for policy learning, which progressively aggregates individual- and coalition-level information, models member complementarity and inter-coalition cooperative–competitive relations, and generates a global value estimate that is sensitive to changes in coalition structure. Furthermore, we integrate Monte Carlo Tree Search, utilizing the learned value as a heuristic to efficiently explore the feasible region, and close the loop with candidate-structure demonstration replay and policy distillation, enabling search to refine the learned policy. In multi-scale rescue simulations, the proposed approach improves task utility by up to 11.4% over the best-performing baseline and increases energy efficiency by more than 228% compared to a non-overlapping coalition variant. Full article

Phased antenna arrays have revolutionized modern wireless systems by enabling dynamic beamforming, multibeam synthesis, and user tracking to enhance data rates and reduce interferences, yet their reliance on expensive active components (e.g., phase shifters, amplifiers) embedded in antenna array elements limits adoption in cost-sensitive sub-GHz applications. Therefore, the active phased antenna arrays are still considered as high-end technology and primarily designed only for high-frequency bands and demanding applications such as radars and mobile base stations in microwave bands. In contrast, various important radio communication services still operate in sub-GHz bands with no adequate solution for modern antenna systems with beamforming capability. This paper introduces a 3D antenna array with switched-beam or multibeam capability, designed to eliminate mechanical rotators and active circuitry while maintaining all-sky coverage. By integrating collinear radiating elements with a Butler matrix feed network, the proposed 3D array achieves transmit/receive multibeam operation in the 435 MHz amateur satellite band and adjacent 433 MHz ISM band. Simulations demonstrate a design that provides selectable eight beams, enabling horizontal 360° coverage with only one radio connected to the Butler matrix. If eight noncoherent radios are used simultaneously, the proposed antenna array acts as a multibeam all-sky coverage antenna. Innovations in our design include a 3D circular collinear topology combining the broad and adjustable elevation coverage of collinear antennas with azimuthal beam steering, a passive Butler matrix enabling bidirectional transmit/receive multibeam operation, and scalability across sub-GHz bands where collinear antennas dominate (e.g., Lora WAN, trunked radio). Results show sufficient gain, confirming feasibility for low-earth-orbit satellite tracking or long-range IoT backhaul, and maintenance-free beamforming solutions in sub-GHz bands. Given the absence of practical beamforming or multibeam-capable solutions in this frequency band, our novel concept—featuring non-coherent cooperation across multiple ground stations and/or beams—has the potential to fundamentally transform how the growing number of CubeSats in low Earth orbit can be efficiently supported from the ground segment perspective. Full article