Search Results (159)

Nominal launch-power profiles can lose quality-of-transmission (QoT) margin when span and equipment parameters vary. We study this effect using C-band GNPy 2.14.1 simulations that recompute amplified-spontaneous-emission (ASE) noise and Gaussian-noise (GN)-model nonlinear interference under perturbations. Ten runs use 384 training scenarios and 1024 intensified-stress scenarios with scalar and spectral multipliers of 1.25 and 1.50. In paired within-GNPy comparisons, a finite-sample 5% lower-tail-mean selector, defined as the mean of the 20 worst training utilities, improves fifth-percentile minimum-channel generalized signal-to-noise-ratio (GSNR) margin over nominal optimization by 0.247 dB, with a 95% confidence-interval half-width of 0.014 dB. After normalization to the nominal total launch power, the gain remains 0.179 dB (half-width 0.017 dB), suggesting that spectral shape is a major contributor to the paired difference in this comparison. The gain lies between 0.245 and 0.248 dB when the training-tail fraction varies from 1% to 10%; relaxing the per-channel ceiling from 3.0 to 3.5 dBm removes almost all active bounds while retaining a 0.246 dB gain. Selected profiles mainly raise the low-frequency edge, and the benefit appears near the modeled reach boundary rather than on high-margin metro links. Erbium-doped fiber amplifier noise figure, gain ripple, and reconfigurable optical add-drop multiplexer equalization lead the sensitivity ranking. Reduced Manakov checks preserve power ordering while exposing model offsets. The results describe the specified GNPy configuration, finite search, and synthetic perturbation laws; field-calibrated performance remains to be established. Full article

To overcome the limitations of conventional model predictive control (MPC) for trajectory tracking of distributed-drive four-wheel-steering (4WS) vehicles, particularly its fixed weighting matrices and prediction and control horizons, this study investigates the integrated trajectory tracking and stability control of an automated distributed-drive electric vehicle equipped with four independently controlled in-wheel motors and a four-wheel-steering system. The main novelty of this study lies in the simultaneous online adaptation of the MPC weighting matrices and reconfiguration of the prediction and control horizons, together with the coordinated integration of four-wheel steering and direct yaw moment control (DYC) within a unified trajectory tracking framework. Unlike conventional adaptive MPC methods that primarily adjust weighting parameters, the proposed adaptive prediction and control horizon adjustment (APCHA) strategy jointly updates the prediction and control horizons according to the integrated tracking error, error variation rate, and control input variation rate. Meanwhile, a fuzzy adaptive weighting mechanism adjusts the MPC weighting matrices online. At the lower control layer, a torque allocation method considering both the tire load ratio and vertical tire loads is employed to realize the required direct yaw moment. Finally, CarSim–Simulink co-simulation is conducted to verify the effectiveness of the proposed control strategy. Simulation results demonstrate that, at a vehicle speed of 60 km/h and a road adhesion coefficient of $\mu =0.5$, the proposed Improved MPC-4WS controller reduces the maximum lateral tracking error by 34.9% compared with the conventional MPC-4WS controller, thereby demonstrating superior trajectory tracking performance. Furthermore, the ablation study verifies the effectiveness of the proposed hierarchical architecture by quantifying the contributions of the DYC module and the optimized torque allocation strategy. Full article

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update. Full article

This paper studies a cooperative wireless system in which a single-antenna base station (BS) communicates with a destination user (U) via a half-duplex energy-harvesting amplify-and-forward relay, while the direct BS–U link is unavailable. The destination (U) is equipped with a fluid antenna system (FAS) comprising multiple closely spaced receive ports, enabling spatial reconfigurability through instantaneous port selection. A power-splitting architecture is adopted at the relay to support simultaneous energy harvesting and information forwarding. All wireless links are modeled as flat Rayleigh fading, and the spatial correlation among the FAS ports is explicitly incorporated. To analytically characterize the impact of correlated port selection, a Gaussian copula framework is employed to model the joint distribution of the FAS-channel power gains. Exact integral expressions for the cumulative distribution function of the end-to-end signal-to-noise ratio are derived, from which the outage probability is obtained. For the special case of uncorrelated FAS ports, closed-form expressions are further developed using order statistics and special functions. In addition, asymptotic analysis is carried out to provide further insight into system performance in the high-signal-to-noise-ratio region. Numerical and Monte Carlo simulation results validate the analytical derivations and demonstrate that FAS-based receiver selection yields significant gains in outage performance, even in the presence of strong spatial correlation and energy-harvesting constraints. Full article

5G electromagnetic field (EMF) measurement is more challenging than measurement in previous cellular generations because 5G New Radio uses time-division duplexing, flexible bandwidths, beam sweeping, massive MIMO, and user-specific traffic beams. As a result, the measured synchronisation signal block (SSB) or PBCH-DMRS level may not directly represent the maximum exposure produced by data transmission. This motivates a combined tutorial and structured survey of existing 5G EMF measurement studies and procedures. This paper reviews the literature on 5G EMF measurement by classifying existing methods into frequency-selective measurement, code-selective measurement, actual exposure assessment, maximum-exposure extrapolation, and network-counter-based assessment. Representative field studies, public measurement reports, and network-data-based studies are compared according to their measurement scenarios, exposure objectives, and limitations. The paper further discusses key uncertainty sources, including beam/gain offset, TDD duty cycle, bandwidth extrapolation, traffic variation, spatial sampling, and equipment-related uncertainty. Finally, open challenges related to FR2 millimetre-wave measurements and reconfigurable propagation environments are discussed. By combining tutorial background with a structured survey, this paper clarifies 5G EMF measurement procedures, maximum-exposure extrapolation, uncertainty sources, and FR2 millimetre-wave measurement challenges. Full article

Reconfigurable intelligent surfaces (RIS) equipped with extremely large aperture arrays (ELAA) are emerging as a key technology for enhancing beamforming gain, spatial multiplexing, and angular resolution in sixth-generation (6G) wireless networks. When combined with non-orthogonal multiple access (NOMA), RIS can further improve spectral efficiency, system throughput, and energy efficiency. However, most existing studies on RIS-aided NOMA assume far-field propagation, where the incident wavefronts are approximately planar. In contrast, RIS-ELAA systems operating at millimeter wave (mmWave) experience spherical wavefronts in the radiative near-field regions. Also, it creates spatial non-stationarity and distance-dependent phase curvature. These effects invalidate the conventional monotonic path-loss assumption and make fairness-oriented NOMA design more challenging. This paper proposes a joint element and power optimization (JEPO) algorithm for near-field RIS-ELAA-assisted indoor NOMA systems, in which the RIS is dynamically partitioned into user-specific subarrays performing near-field phase synthesis toward the near user (NU) and far user (FU). The reversed far-to-near successive interference cancellation (SIC) ordering, governed by an effective FU channel gain greater than an effective NU channel gain, is formally established, and a closed-form optimal power allocation is derived by reducing the max-min fairness condition to a scalar quadratic in the target signal-to-interference-plus-noise ratio (SINR), eliminating iterative power search. Simulation results confirm that JEPO consistently outperforms four baseline schemes across transmit power, NU distance, angular separation, and RIS aperture size, with the largest gain observed at ${\theta }_{\mathrm{NU}}\approx {40}^{\circ }$ where fixed-partition baselines collapse to near-zero fairness while JEPO maintains robust performance. Full article

Visibility graphs map time series to networks whose combinatorial structure encodes fractality, recovering the Hurst exponent of self-affine processes. We ask what the visibility construction carries beyond this fractal content. We lift the visibility graph to a 1-varifold, a measure on position and direction space from geometric measure theory, and equip it with a multiscale positive definite kernel. The lift embeds visibility graphs of unequal size in a common Hilbert space and yields a channel-resolved measure of cross-series geometric alignment. On a 25.8-year daily panel of thirteen commodity and currency layers, we define a relative alignment contrast that compares commodity currencies and safe haven currencies in their geometric alignment with the commodity complex. During global risk-off episodes the contrast is large and positive: commodity currencies import commodity shock geometry far beyond a time-shift independence benchmark, while the Japanese yen remains near geometric independence and the franc is confounded by a managed regime. The contrast is significant under three stress definitions with autocorrelation robust inference, holds as a continuous dose response, survives the removal of any single crisis, withstands moment, fractal, and topological controls, is direction-consistent across sixteen specifications, and collapses under a time-shift placebo. Detrended fluctuation analysis explains only two percent of it, so the reconfiguration is geometric information beyond fractality at this horizon, and a scaling exponent of the kernel mass separates a fractal-free component from a fractal-driven one. For investors, financial institutions, and policymakers, the contrast is a real-time structural diagnostic of flight to safety: it marks when commodity currencies stop diversifying the commodity complex while genuine safe havens still do, signaling through a channel that second-moment risk measures are built to miss. Full article

Fault location in distribution networks is challenged by dynamic topology changes and heterogeneous equipment. This paper proposes a Dynamic and Hierarchical Multi-Head Graph Attention Network (DHMGAT) that overcomes the limitations of static graph assumptions. Unlike methods that treat network structure as fixed or neglect line parameters, DHMGAT employs a hierarchical multi-head attention mechanism to encode topology dynamically. An Edge Feature Encoding Module fuses physical line attributes—impedance and switch states—directly into node embeddings. A Topology-Gated Pooling mechanism adapts to radial structural variations, and a Physics-Constrained Data Augmentation strategy ensures robustness under limited-sample and anomalous-data conditions. Evaluated on the IEEE 33-node and IEEE 123-node systems under comprehensive fault scenarios, DHMGAT achieves localization accuracies of 96.70% and 94.31%, respectively, with near-perfect calibration (ECE = 0.066). It maintains accuracy above 92% under high-noise conditions and N-1 topological reconfiguration, and above 88% under severe feature loss (up to 30% missing data), substantially outperforming conventional graph neural networks. Full article

The deep integration of sci-tech and industrial innovation, rooted in the fusion of exploratory and exploitative ambidextrous capabilities, is a common global challenge. Traditional actors like enterprises and universities struggle due to the inherent imbalance of ambidextrous capability. Developed countries (e.g., Germany’s Fraunhofer, Finland’s VTT) have achieved integration through new-type research organizations, but rely on a “static coordination” model across departments ill-suited for rapidly changing, multi-logic environments. In contrast, China’s new-type R&D institutions (NTRI), emerging as innovative organizations, are naturally equipped to handle such institutional complexity and have become key drivers of deep integration. This study takes NTRI as a longitudinal single-case study object. Based on ambidextrous innovation theory and resource action theory, it constructs an analytical framework of “identifying integration challenges—addressing integration challenges—achieving integrated innovation” to explore how NTRI build dynamic ambidextrous capability through resource actions to drive the internal mechanism of integrating sci-tech innovation and industrial innovation. The results show that: (1) Accurately identifying integration breakpoints, bottlenecks, and hurdles at different development phases and establishing integration goals are key prerequisites for achieving integrated innovation; (2) the process of achieving integrated innovation is essentially a dynamic reconstruction of ambidextrous capability, involving resource bricolage to reconfigure demand-driven ambidextrous linking capability, utilizing resource orchestration to fission context-synchronized ambidextrous integration capability, and executing resource concerto to leapfrog networked symbiotic ambidextrous empowerment capability; and (3) the integrated innovation of NTRI at different phases exhibits a dynamic evolution, evolving from unidirectional spillover-integrated innovation to bidirectional interactive integrated innovation, and ultimately to empowering symbiotic integrated innovation. Full article

Conventional industrial manipulators are often costly and come with steep learning curves, which limits their scalability in hands-on robotics education. This paper presents a compact and modular vision-guided sorting platform based on a 4-DOF SCARA robot, designed for rapid assembly, reconfiguration, and beginner-friendly deployment in laboratory courses. A collaborative visual perception strategy is proposed, which introduces a lightweight YOLOv8 algorithm for robust material category recognition, while HSV-based color segmentation and Hough circle localization are utilized to extract sub-pixel centroid features. The pixel measurements are mapped to the robot base frame through an integrated nine-point hand–eye calibration model, and joint commands are generated via a joint-space quintic polynomial interpolation algorithm to ensure continuity and avoid kinematic singularities. The overall system adopts a hierarchical architecture in which the vision host communicates target commands to a motion controller via TCP/IP, while joint actuators are driven through a CAN bus. Feasibility is first verified in a Webots digital prototype with synchronized conveyor and manipulator control, and is then validated on a physical platform equipped with a compliant TPU-based soft gripper to improve grasp tolerance under localization noise. Experiments demonstrate that the system achieves an average recognition accuracy of 98.1% and a mean positioning error of 0.189 mm. The proposed platform provides an extensible testbed for teaching kinematics, perception-to-control integration, and modular robotic system development. Full article

Enhancing the robustness and fault tolerance of finite-state machines (FSMs) is crucial for safety-critical systems, such as transportation control systems and medical equipment. This issue becomes particularly important when developing control units for unmanned aerial vehicles (UAVs), which are exposed to external disturbances from electronic warfare (EW) systems. Under such conditions, traditional methods for creating fault-tolerant finite-state machines (FTFSMs), initially designed to address the effects of ionizing radiation that cause rare single-event upsets (SEUs), are often ineffective. This paper proposes a novel method for developing FTFSMs that can withstand multi-bit upsets (MBUs) affecting the FSM’s wires and memory cells due to external disturbances. The FTFSM architecture additionally includes an output register and a concurrent error detection (CED) circuit. When a fault is detected, the FTFSM switches to standby mode. Once the external disturbance ceases, the FTFSM resumes normal operation from the point of interruption without altering the control algorithm. In cases of critical errors, the FSM circuit can be reconfigured via the system processor. Experimental studies have shown that the proposed approach incurs exceptionally low overhead costs. Additionally, the paper presents a technique for calculating the probability of fault detection for FTFSMs implemented in field-programmable gate arrays (FPGAs). Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods. Full article

To meet the increasingly strict requirements of modern communication, radar detection and electronic measurement systems for wide-bandwidth, low-insertion-loss and high-isolation signal routing, this paper presents a 16 × 16 programmable switch matrix that simultaneously achieves wideband operation (DC-40 GHz), low insertion loss (≤0.9 dB maximum), high isolation (>50 dB typical), and systematic modular scalability, a combination not found in existing implementations. The matrix, constructed with high-quality coaxial switches and optimized RF circuitry and electromagnetic structures, provides flexible and stable single-pole multi-throw (SPMT) signal routing across an ultra-wide frequency range from DC to 40 GHz. The switch matrix features a modular architecture, integrating multiple RF switching units, drive control circuits, and communication interface modules. This architecture achieves minimal signal path depth while maintaining full connectivity between any input and output port, directly minimizing cumulative insertion loss. Through precise impedance matching design and isolation structure optimization, the system still exhibits outstanding transmission characteristics at the 40 GHz high-frequency end: typical insertion loss does not exceed 0.9 dB, and the isolation between channels is better than 50 dB, effectively ensuring the integrity of signals in complex multi-channel environments. To meet the requirements of automated testing and remote control, the equipment integrates dual communication interfaces (serial port/network port), supports the SCPI command set and TCP/IP protocol, and can be conveniently embedded in various test platforms to achieve instrument interconnection and test process automation. Experimental verification shows that this matrix exhibits excellent switching stability and signal consistency across the entire 40 GHz, with a switching action time of less than 10 ms. Furthermore, it is capable of real-time topology reconfiguration via a microcontroller or FPGA. These innovations collectively deliver a switch matrix that meets the demanding requirements of 5G communication, millimeter-wave radar, and aerospace defense systems—applications where bandwidth, signal integrity, and system flexibility are paramount. Full article

In large-scale dam construction, the efficiency of concrete transport operations is fundamentally governed by the coordination between horizontal hauling and vertical hoisting capacities. Traditional experience-based scheduling approaches often fail to capture the stochastic, cyclic, and resource-coupled nature of these transport systems. This study developed a closed queuing network-based stochastic simulation framework to model dam concrete transportation as a finite-population cyclic service system. The process was abstracted into sequential service stages with stochastic service times, and a structured state-space representation combined with time-step simulation was constructed to describe dynamic resource occupation and task transitions under varying truck and cable crane configurations. Application to a real large-scale dam project revealed a characteristic multi-stage performance evolution pattern governed by capacity matching mechanisms. As the truck fleet size increased, system performance transitioned from a transport-limited regime to a capacity-coordination regime and ultimately to a hoisting-saturated regime in which further fleet expansion yielded diminishing returns. Sensitivity analysis demonstrated that hoisting capacity imposed an upper bound on system throughput, while adaptive fleet reconfiguration could restore operational equilibrium under constrained equipment availability. The results indicated that dam concrete transport should be treated as a dynamic capacity regulation problem rather than a static allocation task. The proposed framework provides an interpretable and quantitative decision-support tool for equipment configuration, bottleneck identification, and adaptive scheduling in large-scale hydraulic infrastructure projects. Full article

This paper proposed a reconfigurable intelligent surface (RIS)-empowered reference-modulated differential chaos shift keying (RM-DCSK) wireless powered communication (WPC) system. As a noncoherent chaotic communication scheme, the proposed system exploits the reference reuse property of RM-DCSK, where the reference signal simultaneously carries data information, thereby improving spectral efficiency while maintaining noncoherent and channel-estimation-free reception with low receiver circuit complexity. Furthermore, RIS is utilized to reconfigure the propagation environment and mitigate the path loss effect of WPC links. At the user equipment (UE), a harvest–store–use (HSU) energy harvesting and finite-buffer model is developed, and a threshold-based on/off transmission policy is adopted to enable sustainable uplink transmission. To quantify the gain of energy buffering and management, a bufferless baseline system is further established. Closed-form bit error rate (BER) expressions are obtained under multi-path Rayleigh fading channels for both the proposed RIS-RM-DCSK-WPC system and bufferless baseline system. Finally, simulation results validate the analysis and demonstrate that the proposed system achieves superior BER performance compared with representative benchmarks, including existing RIS-aided DCSK-WPC, RM-DCSK-WPC, and bufferless RIS-RM-DCSK-WPC systems. Full article