Search Results (210)

Advanced metering infrastructure (AMI) is the sensing backbone of the smart grid, and its reliability underpins urban energy services such as state estimation, demand response, and distributed-energy integration. When AMI uses cellular spectrum leased through a cognitive mobile virtual network operator (C-MVNO), allocating channels to data aggregation points (DAPs) each frame is difficult because three uncertainties interact: imperfect spectrum sensing, time-varying and cross-channel-correlated primary-user activity, and stochastic urban propagation. Classical Hungarian assignment is optimal per frame but blind to primary-user dynamics, while cognitive-radio heuristics ignore queue state and cross-channel structure. We propose a two-timescale hierarchy that couples these established tools in a new way: a Proximal Policy Optimization (PPO) agent decides, once per epoch, which opportunistic channels to expose, and an exact Hungarian solver performs the per-frame DAP-to-channel assignment. To our knowledge this is the first coupling of a learned cognitive layer with exact Hungarian assignment for cognitive-radio resource allocation. On a 3GPP TR 38.901-compliant simulator, PPO significantly outperforms a Bayesian-belief baseline and the Hungarian-only configuration in delivery ratio, latency, and a strict per-meter satisfaction metric, and is robust across independent seeds and sensitivity sweeps. An architectural ablation shows the DAP tier is a precondition for viability, not merely an optimization. Full article

Dynamic spectrum access (DSA) is an effective technology to exploit spectrum for radio devices in complex electromagnetic environments. Systematic external jamming, such as swept and comb jamming, is a common form of jamming in anti-jamming communication scenarios. Deep reinforcement learning (DRL) is widely utilized to improve the performance of DSA. However, DRL-based DSA methods face challenges in generalizing across different jamming scenarios. In this paper, a DSA scheme based on a Bootstrap ensemble deep Q-network (BEDQN) integrated with an echo state network (ESN), termed ESN-BEDQN, is proposed to achieve fast and reliable access in scenarios where jamming patterns undergo sudden changes. The ESN provides low-complexity temporal memory to capture jamming patterns, while the BEDQN maintains multiple diverse readout heads to achieve fast exploration after ESN-BEDQN reset. Moreover, a jamming pattern change prediction method based on channel idle ratio detection using symmetric KL divergence is proposed to trigger network reset, i.e., Pred-Reset. Simulation results demonstrate that the ESN-BEDQN-based scheme achieves a near-zero collision rate under periodic jamming and recovers substantially faster than conventional deep Q-network (DQN)- and long short-term memory (LSTM)-DQN-based schemes in scenarios with abrupt jamming pattern changes. Furthermore, the Pred-Reset method can correctly capture jamming pattern changes and trigger network resets, achieving faster convergence than other baseline schemes across all tested scenarios. Full article

Topographic and geomorphic information provides an essential basis for human development and utilization of natural resources, disaster prevention and mitigation, ecological environment protection, and scientific research. Among spaceborne remote sensing approaches, Synthetic Aperture Radar (SAR) stands out due to its ability to actively transmit and receive microwave signals, enabling high spatial coverage, all–weather, and all–day observation. With the rapid development of miniaturized satellite constellations, high–revisit and high–resolution SAR data have become more accessible, offering unprecedented opportunities for dynamic ocean observation. However, existing SAR–based bathymetry methods based on power–spectrum analysis are susceptible to sea–spike noise and 180° directional ambiguity, limiting their accuracy in shallow coastal waters. To address these limitations, a Cross–Spectrum–based Wave Ray Tracking bathymetry retrieval algorithm (CS–WRT) is developed using high–resolution imagery from mini–SAR constellations including HiSea–1 and Chaohu–1. The method incorporates cross–spectrum analysis into a localized wave ray tracking framework to effectively suppress sea–spike noise and accurately extract shallow–water wave vectors. Applied to six SAR images over the Taiwan Strait, CS–WRT consistently outperformed the power–spectrum approach in coastal environments. In the Jinjiang coastal region, comparison with Electronic Navigational Chart (ENC) data yielded a root mean square error of 2.22 m, a mean absolute percentage error of 7.06%, and a Pearson correlation coefficient of 0.84. Analysis of the shoaling slope parameter k further revealed that stronger wave shoaling effects correlate with improved retrieval accuracy, suggesting its potential as a diagnostic indicator of retrieval reliability. Full article

The intelligent transportation systems (ITS), including autonomous driving technologies, have increased the need for a capable communication system. The optical domain offers a promising spectrum for supporting multi-connection and high-data-rate applications. This paper proposes a dynamic resource allocation algorithm for vehicle-to-vehicle (V2V) 6G visible light communication (VLC) systems. A wavelength division multiple access (WDMA) method is utilized as a technique for supporting multiple connections. Two optimization objectives of the resource allocation are evaluated, which are referred to as Max SNR and Max spectral efficiency (SE) objectives. The best resource assignment for each vehicle is obtained by using the optimized resource allocation model. Five scenarios are examined where vehicles are moved in this work. The Max SE objective shows a fair allocation of resources based on the SNR compared to the Max SNR objective. In addition, a dynamic algorithm is developed for real-time solutions. The proposed dynamic algorithm can provide suboptimal resource allocation at 0.001 s, whereas the Max SE MILP model provides the optimal resource allocation in around 1 min. Thus, the proposed dynamic scheme can be used in real-time applications. Full article

The evolution of wireless networks toward 6G and Open Radio Access Network (O-RAN) architectures brings unprecedented demands for flexible and energy-efficient resource allocation mechanisms. A key challenge is to allocate radio resources effectively among heterogeneous units while satisfying diverse quality-of-service (QoS) requirements. Traditional allocation methods often fail to capture energy efficiency considerations or lack adaptability in highly dynamic and decentralized environments. To address this, we formulate the resource allocation problem as a non-cooperative game among SDN-enabled Central Units (CUs) and Distributed Units (DUs), where each player’s utility captures a trade-off between throughput gains and resource costs under threshold-based SINR QoS constraints. We show that the game admits an exact generalized potential function, guaranteeing the existence of a pure-strategy Nash equilibrium and convergence under sequential best response dynamics. The SDN controller supervises the network by adjusting system-level parameters, such as the resource price, to guide the network toward efficient and fair allocations. This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks. The proposed framework is evaluated against both classical resource allocation strategies (equal and greedy allocation) and advanced optimization-based and game-theoretic baselines, including convex optimization, proportional fairness, water-filling, and Stackelberg formulations, and shows competitive performance. Full article

Flying Ad-Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV) are critical enablers of intelligent transportation and smart city ecosystems. Their reliance on shared wireless channels, however, exposes them to diverse jamming attacks that threaten communication reliability, mission effectiveness, and safety. This paper presents a comprehensive study of jamming threats in integrated FANET–IoT–IoV environments and analyzes conventional and advanced anti-jamming techniques across physical, link/MAC, spectral, spatial, temporal, and hybrid domains. To address the challenges posed by heterogeneous and dynamic network conditions, we propose a cross-layer anti-jamming framework that integrates Cognitive Radio (CR) for dynamic spectrum access and Multi-Agent Reinforcement Learning (MARL) for cooperative, adaptive decision-making. The framework employs a Perception Engine for local anomaly detection, a Cognitive Engine for constructing a collaborative jamming map, and a Decision and Action Engine for multi-agent DRL-based mitigation. Simulation results demonstrate that the proposed CR-MARL framework significantly improves packet delivery ratio, reduces latency, and adapts efficiently to varying jamming strategies, while maintaining low energy and computational overhead, making it suitable for resource-constrained UAVs, vehicles, and IoT sensors. Full article

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT (B-IoT) as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. Using a structured narrative-review approach, this survey synthesizes relevant peer-reviewed and technical literature on B-IoT-enabled digital agriculture and organizes the evidence around communication key performance indicators (KPIs), deployment constraints, and four technology domains: sensing, connectivity, intelligence/compute, and control/application. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions. Full article

AVs are also bound to capitalize on 5G networks, which creates crucial challenges in the adaptable management of resources because they need very low latency, a high-speed connection, and energy-efficient functionality. Older approaches to optimizing resource allocation in the high-frequency changing vehicle environment fail to deliver as mobility trends and network status constantly adapt and change. To overcome these problems, we suggest a new Deep Reinforcement Learning (DRL)-based algorithm, which is aimed at optimizing the allocation of resources to AVs. This model combines a Spatiotemporal Graph Convolution Network (ST-GCN), Gated Recurrent Units (GRU), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to create a unified model. The ST-GCN is successful at both capturing the dynamic space relationship between vehicles and between vehicles and roadside infrastructure, and also gives a complete picture of network topology. GRU uses traffic and communication information to forecast future mobility patterns and bandwidth demand of each agent and therefore allocate resources proactively. The MADDPG algorithm is used to enable decentralized but coordinated decision-making among AVs, which enables the realization of dynamic policies of bandwidth allocation in real-time. Simulations using such aspects as a realistic Rayleigh fading channel model, a node density of 100 vehicles/km², and 100 MHz of bandwidth prove the effectiveness of the framework extensively. We find that the end-to-end latency increase is reduced by up to 30%, and the system throughput is increased by up to 28, and the energy efficiency is increased by an average of 40 percent in comparison with the baseline techniques. Such results confirm our framework to be a plausible solution to building effective and sustainable communication systems to enable AVs to cooperate in the information exchange of important data. Full article

With the advancement in electronic attack technologies, intelligent jamming poses a significant challenge to the reliable transmission of wireless communications. Traditional anti-jamming methods often fail to adapt to dynamic nonlinear jamming environments. This paper addresses nonlinear swept-frequency jamming by modeling anti-jamming communication as a sequential decision-making problem and proposes an intelligent anti-jamming method based on proximal policy optimization (PPO) to optimize dynamic channel selection. Firstly, the channel selection problem is formalized as a Markov decision process (MDP), where a state space integrating jamming patterns and communication status is designed, the channel set is defined as the action space, and a multi-objective reward function trades off jamming avoidance against switching overhead. A dual-network architecture comprising a policy network and a value network is constructed, and the PPO algorithm is employed for policy updates, where a clipping mechanism is used to enhance training stability. The system optimizes the anti-jamming strategy online through a closed-loop process of “sensing–decision–learning–communication”. Simulation results demonstrate that compared to conventional methods, the proposed method significantly improves key performance indicators such as packet success rate and throughput. It can rapidly track changes in jamming, exhibiting excellent real-time performance and environmental robustness, and thus provides an effective solution for reliable communication in dynamic jamming environments. Full article

Background/Objectives: Cochlear implantation (CI) represents a well-established intervention for the management of severe to profound sensorineural hearing loss. The co-occurrence of severe hearing loss and Autism Spectrum Disorder (ASD) presents unique diagnostic and therapeutic challenges that significantly impact post-implantation outcomes. This review aims to synthesize the current literature on cochlear implantation in children with Autism Spectrum Disorder (ASD), including diagnostic, audiological, rehabilitative, and functional outcome considerations. Methods: A structured search of PubMed and Scopus was performed for English-language articles published between January 2000 and January 2026, focusing on audiological assessment, rehabilitation challenges, multidisciplinary management, and post-implant functional outcomes in this population. Results: The findings synthesized in this review suggest that cochlear implantation in children with Autism Spectrum Disorder must be interpreted within a broader communicative-ecological framework rather than through auditory metrics alone. These findings highlight a multidimensional model of post-implant outcomes, shaped by the dynamic interplay between auditory access, social engagement, family context, and language-learning environments. Conclusions: Most children with ASD and severe-to-profound hearing loss show improvements in speech perception and production after cochlear implantation, although outcomes are highly variable. A multidisciplinary approach, through coordinated collaboration among specialists, enhances family engagement, optimizes compliance with care plans, and ultimately contributes to improved clinical and developmental outcomes. ASD should not be considered a contraindication for CI; however, careful individual assessment, realistic parental counseling, and a multidisciplinary approach availability to evaluation and rehabilitation are essential. Full article

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. Full article

Metastatic triple-negative breast cancer (mTNBC) remains one of the most challenging therapeutic settings in oncology. Although it has traditionally been defined by the absence of hormone receptor expression—estrogen receptor (ER) and progesterone receptor (PR)—and HER2 amplification or overexpression, this simplified definition fails to capture the biological complexity that drives its marked clinical heterogeneity, therapeutic resistance, and prognostic variability. Over the past decade, multiple studies have challenged the notion of TNBC as a single disease entity, identifying distinct molecular subtypes, including Basal-like 1 (BL1), Basal-like 2 (BL2), Mesenchymal (M), Mesenchymal Stem-like (MSL), Immunomodulatory (IM), and Luminal Androgen Receptor (LAR), each characterized by specific biological programs and therapeutic vulnerabilities. In parallel, clinically oriented systems such as the Fudan classification have enabled the prospective evaluation of subtype-guided therapeutic strategies in metastatic disease, as illustrated by the FUTURE and FUTURE-SUPER trials. In this review, we examine the molecular classification and clinical behavior of mTNBC subtypes, integrating genomic, transcriptomic, epigenetic, immunologic, stromal, and biomechanical dimensions of tumor heterogeneity. We also discuss emerging tools, including single-cell RNA sequencing, spatial transcriptomics, circulating tumor DNA analysis, long non-coding RNA profiling, and surrogate immunohistochemistry-based classifiers, as well as their potential role in refining patient stratification. From a therapeutic perspective, we review subtype-guided strategies involving chemotherapy, platinum agents, PARP inhibitors, immunotherapy, antiandrogen therapy, PI3K/AKT/mTOR pathway inhibition, antiangiogenic approaches, and antibody–drug conjugates. Redefining mTNBC through biologically driven stratification represents a rational strategy to optimize treatment selection, support clinical trial design, and accelerate the development of precision oncology approaches. However, clinical implementation requires greater methodological standardization, validated predictive biomarkers, accessible diagnostic platforms, and dynamic monitoring strategies capable of capturing subtype evolution under therapeutic pressure. TNBC should therefore not be regarded as a single disease, but as a spectrum of biologically distinct and clinically evolving entities whose integrated characterization may be essential to improving outcomes in this historically poor-prognosis population. Full article

Exposure to blast waves without kinetic, penetrating, thermal, or toxic components causes a distinct form of traumatic brain injury, termed primary blast-induced TBI (pbTBI). Clinical manifestations of pbTBI span a wide spectrum, ranging from life-threatening intracranial hemorrhage, hyperemia, and delayed cerebral edema to mild and transient neurological symptoms without detectable structural abnormalities on routine imaging. At the mild end of the spectrum, symptoms after a single exposure may resolve quickly, yet repeated exposures—even at very low levels, termed “subconcussive”—can develop into post-concussive syndrome (PCS) or persistent post-concussive symptoms (PPCS) in a subset of individuals. Despite extensive studies, the molecular pathobiology linking primary blast exposure to delayed and sometimes chronic neurobehavioral deficits remains incompletely understood. A mechanistic framework connecting blast-wave physics to biomechanics to biological vulnerability may therefore help define exposure hazards, interpret clinical symptomatology, and guide diagnostic and therapeutic development. This review summarizes the physics of primary blast waves, the resulting biomechanical responses, and candidate biological substrates, emphasizing structures and interfaces with distinct acoustic impedances across anatomical, tissue, cellular, and molecular scales. We synthesize evidence supporting the hypothesis that the cerebral vasculature and endothelial cells represent critically vulnerable substrates of primary blast-wave injury, in part because the vascular tree constitutes the brain’s largest and most widely distributed interface between compartments with different acoustic impedances. Across experimental and human studies, endothelial stress, vascular injury, and downstream neuroinflammation emerge as convergent molecular responses to primary blast exposure. Temporal dynamics are central to understanding pbTBI because many blast-induced processes unfold in sequential phases. These observations support conceptualizing pbTBI as a condition characterized by prominent cerebrovascular injury of varying severity with secondary consequences for neuronal signaling, network function, and behavior. Within this framework, cerebrovascular and neurovascular unit (NVU) dysfunction provides a parsimonious bridge between primary blast-wave exposure and chronic symptom trajectories, where vascular pathology may offer more accessible therapeutic targets than neuronal injury. Key knowledge gaps include identifying which physical component(s) of the blast are most injurious, establishing biologically meaningful dose–response relationships at molecular and physiological levels, and defining windows of vulnerability during recovery that are relevant to repeated exposures. Addressing these gaps is essential for refining safety protocols, improving diagnostic specificity through mechanism-informed biomarkers, and developing evidence-based molecular and vascular therapeutic targets for pbTBI-associated conditions. Progress will require integrating waveform-aware dosimetry with longitudinal physiological and molecular monitoring across both preclinical and human cohorts. Such integration offers a practical path toward translating blast physics into actionable medical guidance for prevention, triage, and recovery management. Full article

Musculoskeletal ultrasound (MSUS) is a well-established and reliable tool for the evaluation of entheses and enthesitis, particularly at larger and accessible sites. Recent technological advances, including high- and ultra-high-frequency transducers, have expanded its potential, enabling detailed assessment of distal extremity entheses. This narrative review provides a focused and updated perspective on this evolving field, highlighting three key advances. First, the identification and characterization of previously underrecognized entheseal sites in the distal extremities, such as pulley systems, retinacula, novel tendon insertions, and collateral ligaments, broadening the morphological spectrum of entheseal imaging. This is complemented by improved evaluation of vascularization through microvascular imaging and contrast-enhanced US (CEUS). Second, the emergence of interventional approaches, particularly US-guided entheseal biopsy, offers a novel means to investigate entheseal tissue in vivo and may establish a link between imaging and histopathology. Third, the integration of advanced functional imaging modalities, including elastography and multispectral optoacoustic tomography (MSOT), provides preliminary additional insights into the biomechanical and molecular properties of the enthesis beyond conventional structural assessment. Collectively, these developments support new investigational perspectives, positioning MSUS as a dynamic and integrative modality capable of exploring new anatomical territories and biological dimensions, with the potential to reshape the understanding and evaluation of entheseal involvement in rheumatology. Full article

The utilization of drone swarms for cooperative missions is becoming increasingly prevalent. However, establishing high-concurrency and highly reliable communication links in complex environments remains a significant challenge. Existing methods based on traditional Medium Access Control (MAC) protocols struggle to cope with high-density collisions, while conventional deep reinforcement learning (DRL) approaches often encounter convergence difficulties in non-stationary interference environments, leading to notable limitations in anti-jamming robustness and algorithmic efficiency. To tackle this problem, this paper proposes a dynamic access algorithm based on Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO). Specifically, we adopt a Centralized Training with Decentralized Execution (CTDE) architecture to enable implicit spectrum cooperation within the swarm. Notably, we design a three-stage progressive curriculum learning mechanism—basic collision avoidance, load balancing, and dynamic anti-jamming—coupled with a phased reward reshaping strategy, guiding the agents to progressively master intelligent frequency-hopping decisions in complex environments. Experimental results demonstrate that in simulated scenarios involving dynamic sweep jamming and high-load multi-drone communication, the proposed method significantly outperforms baseline models such as Carrier Sense Multiple Access (CSMA), random frequency hopping, and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in terms of normalized throughput, channel collision rate, and convergence speed. This research provides theoretical support and an algorithmic foundation for achieving highly reliable access in large-scale swarm data links under harsh environmental conditions. Full article