Search Results (8,457)

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has become an effective architecture for supporting computation-intensive and latency-sensitive applications by enabling flexible deployment and enhanced wireless coverage. However, when non-orthogonal multiple access (NOMA) is incorporated, the joint optimization of computation offloading, wireless resource allocation, RIS phase configuration, and UAV trajectory design becomes highly challenging owing to the strong coupling among decision variables, problem non-convexity, and time-varying system dynamics. To address these challenges, this paper investigates the energy consumption minimization problem in a NOMA-based RIS-assisted UAV-MEC system by jointly optimizing user offloading ratios, transmit power, UAV computing resource allocation, and flight trajectory. A long short-term memory (LSTM)-embedded proximal policy optimization (PPO) algorithm is developed to capture the temporal dependencies of system states and enable adaptive decision-making in dynamic environments. In addition, a closed-form phase conjugation-based optimal RIS configuration is derived and incorporated into the environment model to reduce the action space and improve training efficiency. The simulation results show that the proposed LSTM-PPO method converges faster and achieves lower energy consumption than conventional PPO, deep deterministic policy gradient (DDPG), and fixed offloading schemes, while exhibiting improved stability and scalability in the tested multi-user scenarios. These results demonstrate the effectiveness of combining temporal learning and model-assisted RIS optimization for energy efficient resource management in RIS-assisted UAV-MEC systems. Full article

With the widespread adoption of multiple-input–multiple-output (MIMO) technology, channel state information (CSI) prediction has become a crucial technique for enhancing the performance of wireless communication systems. Traditional channel prediction methods face performance bottlenecks under high-speed mobility and complex channel conditions, making it difficult to meet the requirements of modern communication systems. To address this issue, this paper proposes a fully complex-valued cross-domain modeling framework, termed a complex-valued multi-scale transformer with time–frequency cross-attention network (CMTF-Net), for MIMO CSI prediction. CMTF-Net integrates a learnable multi-scale short-time Fourier transform (LMS-STFT), complex-valued multi-head self-attention (C-MHSA), and bidirectional cross-domain attention for complex-valued sequences (BCDA-CVS). These modules are designed to preserve amplitude–phase consistency, adapt time–frequency representations to CSI evolution, and enable information interaction between temporal and spectral features. On the simulated Overall Test set, CMTF-Net achieves the lowest MAE of 0.000032 and the highest Corr. ($\rho$) of 0.8230 among the compared methods, while maintaining competitive SE and BER values of 0.4240 and 0.2411 at SNR = 10 dB. On the DICHASUS measured datasets, CMTF-Net also shows favorable Test-ID and Test-OOD performance. For example, on DICHASUS-2186, it obtains Corr. ($\rho$)/SE/BER values of 0.8367/0.4935/0.2243 on Test-ID and 0.8061/0.4697/0.2351 on Test-OOD. These results indicate that CMTF-Net provides a balanced performance profile across prediction accuracy, spatial alignment, and communication-oriented evaluation. Full article

The sixth generation of mobile networks (6G) represents a paradigmatic shift in the conception of wireless communication systems, where Artificial Intelligence (AI) is not integrated as an additional feature but is conceived as a native and fundamental component of the physical layer (PHY). This paper presents a comprehensive survey of the state of the art in AI-native physical layer for 6G, synthesizing approximately 100 references from the period 1948–2025. The survey systematically covers 5 main PHY components (channel coding, channel estimation, signal detection, beamforming, and semantic communications) and analyzes 8 AI architectural families (autoencoders, CNN, RNN/LSTM, Transformers, GNN, GAN, Diffusion Models, and Foundation Models), addressing theoretical foundations, proposed architectures, learning algorithms, implementation challenges, and future research directions. A rigorous mathematical framework underpinning these developments is presented, including optimization formulations, convergence analysis, and theoretical performance characterization. Published results from the literature demonstrate that AI-native physical layer can improve conventional performance metrics and enable emerging capabilities essential to 6G, such as semantic communications, predictive environmental adaptation, and operation in previously inaccessible computational complexity regimes. However, such gains are conditional on adequate training resources, robust channel-matched data, and careful consideration of known limitations including generalization across channel distributions, sample inefficiency, model interpretability, and hardware implementation constraints—all of which are critically analyzed in this survey. A reproducible proof-of-concept benchmark further confirms that, under severe resource constraints, autoencoder-based codes currently underperform conventional schemes, highlighting the gap between theoretical potential and practical deployment readiness. Full article

In this paper, a distributed quasi-generalized Reed–Solomon (Q-GRS)-coded cooperative split labeling diversity (DQ-GRSCC-SLD) scheme is proposed to support reliable cooperative transmission of small-volume information in typical scenarios such as device-to-device (D2D) communication, vehicular ad hoc networks (VANETs) and wireless sensor networks. The system employs distinct labeling mappers at the source and the relay, enabling single-antenna transmission while constructing equivalently a dual-antenna labeling diversity model at the destination, which enhances interference resistance and reduces transmission costs. In addition, an ingenious design is proposed to ensure that the destination obtains the joint Q-GRS code. To optimize the weight distribution of the joint code, a traversal search (TS) algorithm is developed. Furthermore, a low-complexity joint decoding algorithm for Q-GRS codes, namely bracketing decoding, is presented by leveraging the efficient decoding algorithm of generalized Reed–Solomon (GRS) codes. Compared to the conventional maximum likelihood (ML) decoding, its complexity has been reduced from comparing $q^k$ codewords to evaluating q or $q+1$ promising codewords. A theoretical performance analysis of the DQ-GRSCC-SLD scheme is provided. Simulation results reveal that the proposed DQ-GRSCC-SLD scheme demonstrates its superior performance under practical scenarios. Full article

Electromagnetic spectrum planning is a critical challenge in modern wireless communication systems, characterized by multiple conflicting objectives including spectrum utilization efficiency, interference minimization, and fairness among users. This paper proposes an Enhanced Discrete Multi-Objective Particle Swarm Optimization (EDMOPSO) algorithm specifically designed for spectrum assignment problems. The proposed method introduces a novel probabilistic discrete velocity update mechanism with adaptive dynamic bounds, an adaptive inertia weight strategy based on normalized population diversity, and an improved archiving technique with enhanced diversity preservation. To handle the discrete nature of spectrum allocation, we develop a binary encoding scheme combined with a problem-specific repair mechanism for constraint satisfaction. The algorithm is evaluated on both synthetic benchmark problems and real-world spectrum planning scenarios. Experimental results demonstrate that EDMOPSO achieves competitive performance advantages over seven established multi-objective evolutionary algorithms, with Hypervolume improvements of 18.7% and Inverted Generational Distance reductions of 23.4% compared to the second-best-performing algorithm. A comprehensive ablation study with 15 configurations validates the synergistic interaction between components. The proposed method provides an effective solution for macro-level periodic spectrum management in complex electromagnetic environments. Full article

High-performance electromagnetic wave absorption materials are desperately needed due to the growing serious electromagnetic interference and pollution issues brought on by the quick growth of modern electronic technology and wireless communication. This work uses the organic–inorganic hybrid perovskite MAPbBr_xI_3−x as a model system to address the problem of restricted loss mechanisms and the challenges in changing the absorption bandwidth of single-component wave-absorbing materials. It achieves systematic tuning of electromagnetic wave absorption performance, especially within the effective working frequency spectrum, through accurate halogen site engineering. According to the study, MAPbI₃ (MPI), MAPbBr_1.5I_1.5 (MPIB), and MAPbBr₃ (MPB), which were synthesized using the anti-solvent approach, all demonstrated exceptional microwave absorption capability, with maximum reflection loss values exceeding −37 dB, among which MPB achieves a remarkable value of −42.41 dB at 16.60 GHz. More significantly, this work shows a distinct structure-property relationship between the effective absorption peak frequency range of this series of materials and their band structure: the strongest absorption peak shows a regular blue shift as the material bandgap widens and the bromine content rises. This finding suggests that focused tailoring of the operating frequency band in wave-absorbing materials can be achieved by manipulating the band structure of perovskites by varying the halogen concentration. In addition to confirming the significant application potential of organic–inorganic hybrid perovskites in the field of microwave absorption, this study offers a novel research perspective and material template for precisely and programmably controlling the absorption frequency band of wave-absorbing materials based on their basic electronic structures. Full article

To meet the demand for high-capacity indoor wireless access in future 6G systems, we propose and experimentally demonstrate a photonics-aided D-band wireless transmission scheme operating at 138 GHz. At the transmitter, two external-cavity lasers together with an I/Q modulator are used to generate a modulated D-band carrier. At the receiver, homodyne down-conversion is employed to directly recover the received signal to baseband, thereby relaxing the requirements on ultra-wideband analog components and high-speed sampling hardware. A 20 m indoor line-of-sight wireless link is established to transmit a 56-Gbaud-rate OFDM-QPSK signal. The transmitted and received spectra, received constellations and bit-error-rate (BER) performance are functions of optical power at different symbol rates, and the channel amplitude and phase responses are systematically analyzed. The results show that broadband D-band signal generation, transmission, and recovery can be stably achieved in the proposed system. After receiver-side digital signal processing (DSP), clear QPSK constellations are obtained. BER measurements reveal an optimal optical-power operating range, and the 32-GBaud OFDM signal outperforms the 56-Gbaud-rate signal because its narrower occupied bandwidth makes it less sensitive to frequency-selective distortion. For 56-Gbaud-rate OFDM transmission, the BER approaches the 20% low-density parity-check forward-error-correction threshold at an optical power of approximately −1 dBm. Further analysis indicates that the current link performance is mainly limited by frequency-selective amplitude and phase distortions under bandwidth-constrained conditions, together with slight nonlinear effects at high power. These results verify the feasibility of a photonics-aided D-band wireless architecture with homodyne reception for medium-range, high-symbol-rate indoor transmission and provide an experimental basis for future 6G sub-THz wireless links. Full article

Federated learning is a distributed machine learning paradigm that enables multiple devices to collaboratively train a shared model while keeping their raw data localized. Federated learning has become an attractive solution for intrusion detection in Internet of Things (IoT)-based wireless sensor networks because it enables collaborative model training without transferring raw traffic data. However, real deployments rarely satisfy the common assumption that client data are independent and identically distributed (IID). In practical wireless sensor networks, data heterogeneity naturally arises from spatial variation, uneven attack exposure, traffic imbalance, and differences in sensing conditions, which can substantially affect detection reliability and deployment feasibility. This study presents an application-oriented evaluation of federated intrusion detection under controlled non-IID conditions using three representative datasets: WSN-DS, CIC-IDS-2017, and UNSW-NB15. An LSTM-based intrusion detection model is trained in a federated setting and assessed using three aggregation strategies, namely, FedAvg, FedProx, and SCAFFOLD, under label skew, quantity skew, and feature skew scenarios. The results show that standard FedAvg degrades markedly as heterogeneity increases, with accuracy reductions of up to 23.4 percentage points and substantially higher communication cost under extreme non-IID settings. In contrast, FedProx and SCAFFOLD improve convergence stability and reduce the impact of client drift, with SCAFFOLD showing the strongest overall robustness and up to 45% lower communication cost than FedAvg due to faster convergence. These results demonstrate that non-IID awareness is essential for building deployable privacy-preserving intrusion detection systems for resource-constrained IoT environments. The study provides practical guidance for selecting federated aggregation strategies in wireless sensor network security applications where robustness, bandwidth efficiency, and real-world data heterogeneity must be jointly considered. Full article

Continuous intraocular pressure (IOP) monitoring is crucial for glaucoma management. Currently, traditional static IOP measurements often fail to detect circadian fluctuations, leading to a clinical dilemma where “normal IOP” is observed despite persistent visual field deterioration. This study presents a wireless, passive localized surface plasmon resonance (LSPR) sensing platform integrated into flexible silicone hydrogel contact lenses. Gold nanoparticles (AuNPs), synthesized via the sodium citrate reduction method, were incorporated into the lens periphery using a “swelling-induced nano-doping” technique to transduce IOP-induced corneal strain into detectable spectral shifts. Ex vivo porcine eye investigations established a physical mapping model, confirming significant LSPR peak wavelength response trends in correlation with IOP variations (10–50 mmHg) and corneal curvature changes. Subsequent 21-day in vivo rabbit studies demonstrated excellent ocular surface biocompatibility; quantitative histopathological analysis (HE, PAS, and Ki67 staining) revealed no significant adverse alterations in corneal endothelial cell density or conjunctival goblet cell function compared to control groups (p > 0.05). Furthermore, the platform maintained high structural integrity and anterior segment tolerance under transient high-IOP conditions. While currently a proof-of-concept, these results indicate that the LSPR-active hybrid system effectively captures dynamic IOP fluctuation patterns as an optical response to acute interventions, providing a foundational engineering path for next-generation, battery-free wearable diagnostics in personalized glaucoma care without the need for built-in electronics. Full article

Telemetric systems are particularly valuable in applications where remote data acquisition and automatic transmission allow effective monitoring of local characteristics. Among the different telemetric approaches, passive wireless systems based on inductive coupling are particularly attractive because they enable sensor interrogation without onboard power storage. Printed electronics (PE) offer several advantages in the realization of such systems, including a wide selection of functional materials, reduced production costs, possibility of rapid prototyping and complete customization. This allows for the development of smart products by embedding sensors and electronics directly into existing objects without significantly altering their geometry or weight. In light of this, the aim of this scoping review is to explore key factors in implementing chipless passive inductively coupled LC telemetric systems via PE. Given the growing interest in smart products, this scoping review serves as a starting point for the design and implementation of smart products specifically on printed passive inductively coupled LC telemetric systems, addressing their development. To better understand the identified solutions, we first outlined the requirements and characteristics of ideal chipless passive LC-based inductively coupled telemetric systems. Then, we provided a comprehensive analysis of conductive materials and substrates, manufacturing technologies, and the design and performance of printed inductors and associated readout architectures. Full article

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article

Understanding the interaction forces between climbers and climbing holds is important for motion analysis and performance evaluation in sport climbing. In particular, force measurement during speed climbing can provide valuable insights into explosive movements and athlete performance. However, many existing measurement systems require modifications to the climbing wall structure or sensors installed behind the wall, which limits their applicability to existing speed climbing facilities. This study proposes a wireless instrumented climbing hold for speed climbing that enables force-related measurement without modifying the wall structure. The proposed system integrates a six-axis force sensor, a microcomputer, a wireless communication module, and a battery inside the climbing hold. This self-contained configuration allows the hold to wirelessly transmit force and moment data during climbing while maintaining compatibility with standard speed climbing walls and competition environments. In addition, the system enables an estimation of the point of force application on the hold surface by combining measured force and moment data with the three-dimensional hold geometry. Experimental evaluations were conducted to verify the feasibility and performance of the system. External load tests using a digital force gauge confirmed that the embedded sensor can measure static loads and respond to rapidly changing loads with sufficient temporal responsiveness, and the estimated point of force application corresponded closely to the actual loading point. Furthermore, measurements on an actual speed climbing wall demonstrated that the proposed system can successfully capture interaction forces during climbing movements. These results indicate that the proposed system is a practical tool for force-based motion analysis in speed climbing. Full article

Late spring frost poses a serious threat to orchard production, especially in mountainous orchards where timely frost monitoring and adaptive protection are difficult to implement. To address this problem, this study developed an intelligent smoke-based anti-frost machine integrating a LoRa-based wireless temperature monitoring system, a smoke actuation unit, and a closed-loop control terminal. To overcome the slow response and large overshoot of conventional PID control under nonlinear field conditions, a fuzzy PID control strategy optimized by the Hippopotamus Optimization Algorithm (HOA) was proposed to regulate smoke release in real time. Comparative simulations were conducted using conventional PID, fuzzy PID, and HOA-fuzzy PID controllers, and field experiments were performed in an apple orchard. The results showed that the HOA-fuzzy PID controller achieved the best dynamic performance. Compared with conventional PID, the overshoot, rise time, and settling time were reduced by 60.12%, 33.21%, and 61.94%, respectively; compared with fuzzy PID, they were reduced by 22.85%, 50.45%, and 60.11%, respectively. Disturbance simulation further indicated improved control robustness. Field experiments showed that the prototype increased the orchard canopy temperature by 0.9–3.0 K, and the PM2.5 distribution in the operational area indicated improved smoke coverage. The adaptive regulation strategy also avoided continuous fixed-output operation, suggesting its potential to improve energy-use efficiency. Overall, the proposed system provides a feasible field-operable approach for improving canopy thermal conditions and reducing frost-risk exposure in mountainous orchards, although further biological validation is still required. Full article

This work presents the design and implementation of a flexible RFID tag based on a biocompatible and environmentally friendly conductive polymer, PEDOT:PSS, which is deposited onto a polyimide/fabric substrate using screen-printing techniques. The complete system consists of a dipole antenna based on PEDOT:PSS and a compact inductive metallic loop on a separate flexible printed circuit board (PCB) designed to match the capacitive impedance of a commercial RFID chip. The modular architecture, with the integrated circuit (IC) mounted on a reusable PCB substrate, shows efficient power transfer while allowing for easy disassembly, recycling, and consequently circularity of the PEDOT:PSS antenna and IC. By leveraging biocompatible materials and additive manufacturing processes, the proposed approach contributes to the advancement of sustainable and low-impact wireless technologies, addressing environmental concerns in next-generation electronics. Full article

This study proposes a metasurface integrated reconfigurable unit-cell coupler designed for wireless power transfer (WPT) applications in unmanned aerial vehicles (UAVs). In near-field capacitive WPT systems, flexible UAV charging is restricted by rotational misalignment, which causes null power points (NPP) where energy transfer is suppressed. To address this, the proposed model emulates 1-bit digital coding states through Symmetric Excitation (SE) and Cross-Excitation (CE) states. Since precise unit-cell characterization is a prerequisite for array expansion, this research focuses on meta-atom-level analysis at 6.78 MHz with a deep sub-wavelength profile (0.002λ). Characterized through 3D full-wave analysis, the unit-cell achieves peak transmission coefficients of 0.945 for SE State and 0.903 for CE State. Crucially, these states exhibit complementary extinction angles at 90° and 45°, respectively, ensuring that the NPP of one state is effectively bypassed by the high transmissivity of the other. This dynamic switching between coding states maintains stable power transfer across a full 360° rotation, providing a technical foundation for scalable, intelligent metasurface-based wireless charging platforms. Full article