Misalignment in wireless power transfer (WPT) systems leads to substantial output power fluctuations and efficiency degradation resulting from reduced mutual inductance (MI). To address this critical challenge, a novel analytical approach for flexible optimization of coil structure and Repositioned reverse-series rounded-rectangular (RRS-RR) coil structure with ferrite core is proposed in this study, where global geometry optimization of the RRS-RR coil structure is performed to enhance misalignment tolerance. Furthermore, a mathematical mapping framework is established to systematically quantify MI variations between transmitter coil (TX) and the receiver coil (RX) with ferrite core based on a multiple mirroring method. The spatial configuration of the RRS-RR coil is defined by minor geometric parameters, and global geometry optimization is implemented to determine the lateral position, axial position, and fillet radius. By eliminating the constraints imposed by the inner diameter of the TX coil, the mutual coupling region between TX and RX coils is expanded. The optimized coupler effectively suppresses MI fluctuations through enhanced magnetic flux transmission, achieving variation rates of only 3.53% (X-axis) and 5.69% (45° diagonal) under 200 mm misalignment. Experimental results from a 3.4-kW prototype demonstrate a DC–DC efficiency of 89.05% under perfect alignment. At 200-mm lateral and 45° diagonal misalignments, the efficiencies remain 87.47% and 86.78%, corresponding to decreases of only 1.58 and 2.27 percentage points, respectively.
Multi-operator cellular redundancy is widely proposed for the ultra-reliable, low-latency requirements of vehicle-to-everything (V2X) safety services, increasingly through predictive or learned schedulers. Such schedulers rest on an assumption: that outages on different operators correlate in ways a scheduler could exploit. Using three open vehicular cellular datasets spanning the radio layer and a V2X application-layer trace, we test it directly. The correlation is real but small: dominated by shared geography, it barely dents the benefit simple redundancy provides and is too weak for a scheduler to exploit. Instantaneous radio quality predicts throughput only weakly and inconsistently under operational mobility — a nonlinear model evaluated out of sample still leaves most of the variance unexplained — so, rather than rank operators by predicted rate, we pose operator selection at the outage layer. There, joint outages exceed location-conditional independence, but only modestly, dominated by physical co-location not operator coupling (Mantel–Haenszel rate ratio R = 1.47 off-mast, 95% CI [1.15, 1.98]); pairs that do not share low-band spectrum behave as negative controls. This weak dependence reduces the realized dual-operator availability gain by about 2.5% (retaining ∼97.5%; at most ∼5% for shared-mast pairs), so simple redundancy keeps essentially all its independence-predicted benefit, and at the V2X application layer it leaves even a perfect correlated-outage scheduler less than a single 3GPP reliability step. The practical answer is simple: field two operators and use whichever link is up or, under duplication-cost constraints, prefer the more reliable one with reactive failover. Under a binary per-second availability metric, a learned scheduler whose value rests on exploiting correlated-outage structure is unwarranted by this evidence; predictive objectives independent of that structure (mobility-, load-, or history-driven prediction, latency-tail control) lie outside our scope. A reproduction package emits every number from one fixed-seed pipeline.
Robust and reliable object detection under adverse conditions remains a critical challenge for automated driving systems (ADS). The performance of RGB-based (visible-spectrum) cameras degrades in poor lighting conditions, whereas the performance of RGB-Long-Wave Infrared (LWIR) fusion architectures remains limited in adverse weather and low thermal contrast scenarios due to partial spectral coverage. At the same time, Short-Wave Infrared (SWIR) has the potential to address some of these gaps but remains under-utilised in ADS. To address these limitations, we propose a novel tri-modality fusion architecture, TFE-Fusion, that simultaneously leverages information from RGB, SWIR, and LWIR modalities. Our architecture employs a feature-level fusion strategy that incorporates pixel-level weighting and a spectral attention mechanism, enabling dynamic fusion of complementary features from all three modalities. A YOLOv8-based detection head, modified for multi-modal input streams, is used for efficient and robust inference. Extensive experiments on the Multispectral Object Detection (MOD) dataset demonstrate that the proposed method outperforms the RGB-LWIR baseline by 2.48 percentage points in mAP@0.5:0.95. The most significant performance gains are observed for the vehicle class, where SWIR features compensate for low thermal contrast in LWIR imagery by capturing distinct material reflectance properties. These results validate the effectiveness of the proposed TFE-Fusion architecture for enhancing detection performance, particularly under low-light and low-thermal-contrast conditions.
Resource management in next-generation wireless communication networks poses substantial computational challenges due to its combinatorial structure and mixed discrete–continuous nature. Many such problems are NP-hard (non-deterministic polynomial-time hard), rendering conventional optimization techniques computationally demanding and often impractical for large-scale deployments. Recent advances in neutral atom quantum computing have demonstrated innovative potential for solving combinatorial optimization problems through graph-based formulations. However, their application to wireless resource management remains largely unexplored. This paper investigates the use of neutral atom quantum processors to address the maximum access problem (MAP) in uplink non-orthogonal multiple access (NOMA)-enabled non-terrestrial networks (NTNs). The MAP is first formulated as a large-scale mixed-integer optimization problem that jointly captures admission control, user clustering, channel assignment, and power allocation under signal-to-interference-plus-noise ratio (SINR) constraints, while accounting for imperfect successive interference cancellation (SIC) at the receiver. To enable hardware-aligned quantum implementation, the problem is transformed into a maximum independent set (MIS) problem over a conflict graph. The resulting graph structure is compatible with the operational principles of neutral atom quantum hardware, allowing direct mapping onto Rydberg atom arrays via blockade-induced interactions. Numerical evaluations assess admission performance, graph complexity, and computational behavior, highlighting the effectiveness of the proposed neutral atom-based framework for solving such graph-theoretic problems. By integrating quantum combinatorial optimization into wireless resource management, this work demonstrates a hardware-aligned mapping for NOMA-enabled NTN resource allocation and provides proof-of-concept validation on a current neutral atom processor, showcasing the potential of neutral atom quantum processors for addressing optimization problems in future 6G and beyond networks.
This paper presents a geometric and probabilistic framework for localizing static User Equipments (UEs) in Non-Terrestrial Networks (NTNs) using multiple Round-Trip Time (RTT) measurements from a single Low Earth Orbit (LEO) satellite. Motivated by the robustness and low complexity of RTT in 3GPP NTN systems and by the limitations of GNSS in low-cost devices, the approach exploits satellite mobility to transform time-separated RTT measurements into geometric constraints modeled via ellipsoidal and conic representations. Closed-form expressions relate RTT errors to uncertainties in range, angle-of-arrival, and UE position. A novel Region-of-Interest (RoI) metric is introduced to characterize single-satellite geometry, enabling the derivation of the likelihood function and a maximum-likelihood positioning algorithm. The impact of key parameters–number of measurements, satellite altitude, and RTT accuracy–is analyzed. Validation of the proposed multi-RTT technique is carried out by comparing it with simulation results and a well-known RTT-and- Doppler scheme. The close agreement with the simulation results confirms the validity of the proposed approach. The results further show that accurate positioning can be achieved with as few as two RTT measurements, while additional measurements improve accuracy, particularly at lower LEO altitudes. Finally, integration with the 5 G NR signaling is discussed in detail, showing that only minor extensions are required.
Emergency sixth-generation (6G) networks require rapid, reliable, and energy-efficient connectivity when terrestrial infrastructure is damaged or unavailable. Unmanned aerial vehicles (UAVs) with integrated sensing and communication (ISAC) capabilities can provide flexible aerial coverage while enabling situational awareness in disaster-affected areas. However, their performance is constrained by limited onboard energy, obstructed air-to-ground links, and the competing requirements of communication reliability and sensing accuracy. To address these challenges, this paper proposes RESCUE-ISAC, an energy-efficient reconfigurable intelligent surface (RIS)-assisted UAV-ISAC framework for emergency 6G networks. In the proposed system, a multi-antenna UAV serves ground emergency users and senses a target area, while an RIS improves the wireless propagation environment through passive beamforming. The energy-efficiency maximization problem is formulated as a joint optimization of UAV active beamforming and RIS phase shifts subject to communication signal-to-interference-plus-noise ratio (SINR), sensing signal-to-noise ratio (SNR), transmit power, and unit-modulus constraints. An alternating block-wise optimization approach is developed to solve the resulting non-convex problem. Simulation results demonstrate that RESCUE-ISAC improves energy efficiency, link reliability, sensing performance, mobility robustness, and runtime–performance trade-off compared with heuristic, lightweight, and optimization-based benchmark schemes.
In research and development toward beyond 5G and 6G, open-source 5G platforms compliant with the 3rd Generation Partnership Project (3GPP) standards, such as OpenAirInterface (OAI), have gained attention for their compatibility, extensibility, and low development cost. However, these platforms currently provide limited support for multicell environments. To evaluate multicell interference in a hardware-in-the-loop (HIL) setup using actual user equipment (UE), conventional approaches require a prohibitive amount of hardware, including numerous software-defined radios (SDRs) and large-scale radio-frequency (RF) combining networks. Previously, the authors have proposed a pseudo-interference generation mechanism that approximates aggregate inter-cell interference from multiple base stations (BSs) as additive white Gaussian noise (AWGN), enabling efficient multicell evaluation in a fully virtual-space 5G wireless emulator. In this study, this concept is extended by integrating a pseudointerference generator with SDRs that function as virtual-to-real converters. This approach enables a real-time, low-cost HIL evaluation platform for downlink performance in practical multicell interference scenarios using OAI SDR-based UEs. The developed platform successfully translates the virtual interference model into a physical RF environment by addressing hardwarespecific challenges, including power calibration and fixed-point precision limitations. Experimental results show that the block error rate (BLER) characteristics agree well with ideal multicell simulations while capturing realistic hardware degradations, thereby validating the effectiveness of the proposed platform for practical and scalable multicell performance evaluation.
Accurate tire-force estimation is essential for chassis stability control and motion planning in intelligent vehicles, especially under transient load transfer, combined-slip operation, and variations in tire-road friction. This paper presents a physics-guided tire-force estimator based on a Physics-Informed Neural Network with a Differentiable Friction Ellipse Projection (PINN-DFEP) layer, termed the PINN-DFEP estimator. The estimator predicts longitudinal, lateral, and vertical tire forces from vehicle and suspension-related signals. In the proposed architecture, the Magic Formula is used as a differentiable nominal shape-prior term to regularize the learned force-slip relationship, while the DFEP layer projects the raw longitudinal-lateral force estimates onto the admissible friction domain at the network output. This design embeds tire-mechanics priors into the learning process and enforces friction feasibility with respect to the supplied road-friction coefficient and boundary-defining vertical load. The estimator is trained using CarSim-MATLAB/Simulink co-simulation data from sinusoidal steering maneuvers and evaluated under unseen fishhook and J-turn maneuvers with prescribed Gaussian noise added to the corresponding input or measurement channels. Compared with the learning-based baselines and a physics-based UKF observer under the same test maneuvers and force references, the PINN-DFEP estimator achieves lower tire-force estimation errors while enforcing longitudinal-lateral feasibility with respect to the supplied friction boundary in the tested co-simulation scenarios.
The use of high-performance computers (HPCs) in Software Defined Vehicles (SDVs) provides an additional source of in-vehicle data from future vehicles. Today, troubleshooting is mainly focused on diagnostic trouble codes (DTCs) in electronic control units (ECUs) which can be retrieved via Unified Diagnostic Services (UDS). Log files from HPCs can provide important information about the processes in the vehicle and are therefore also of interest when analyzing the fault memory and repairing the vehicle. In order to use this information, it is necessary to match DTCs and log files. The Retrospective Event Correlation Engine (RECE) introduces a suitable procedure for matching DTCs and log files by using correlation identifiers (IDs) in the vehicle. Creating a proof of concept within a limited controlled test environment offers the chance to demonstrate the function and use of statistical log correlation and anomaly detection. An implementation of this procedure in future architectures may enable workshops to include log files and their correlation into their troubleshooting strategies.
Location and velocity of moving sources in the near-field region of a receiver antenna array, while accounting for electromagnetic interactions between array elements, i.e., mutual coupling (MC), is crucial to estimate in wireless communication systems. Scatterers are inevitably present in the near-field region of the receiver array, causing the direct signal from the source and the reflected signals from the scatterers to become coherent. As such, even high-resolution estimation methods, such as the multiple signal classification (MUSIC) algorithm, cannot be used to estimate the location of coherent sources because the rank of their covariance matrix is lower than the number of sources. In this paper, we propose a method to estimate the location and velocity of moving coherent signals in the presence of MC, and to detect the line-of-sight (LOS) signal. We employ the maximum likelihood (ML) method to jointly estimate unknown parameters, i.e., direction of arrival (DOA), range, radial velocity, and transverse velocity, and to detect the LOS signal. To solve the ML problem, we use the gradient descent algorithm, which requires initial values for unknowns. To provide meaningful initial values, we first estimate the initial DOA and range assuming that the sources are static. Then, using the estimated location, we estimate the initial velocities. Finally, the ML method is applied to refine all estimated parameters. Moreover, using the estimated unknown parameters, we detect the LOS signal. The numerical results demonstrate the effectiveness of the proposed method for a wide range of signal-to-noise ratios.
This paper considers a multi-antenna base station (BS) communicating with a single-antenna user through a reconfigurable intelligent surface (RIS). In large RIS deployments, feeding back the high-dimensional phase vector $\boldsymbol{\theta }\in [0,2\pi)^{N}$, where $N$ is the number of RIS elements, poses a major scalability challenge. Conventional codebook-based compression is limited to fixed linear subspaces. To address this, we propose Phase-Aware Quantum RIS (PAQ-RIS), a hybrid quantum–classical autoencoder that compresses RIS phase profiles using variational quantum circuits. Phase shifts are angle-encoded into quantum states, processed by parameterized quantum circuits, and reconstructed by a classical decoder. Results show that PAQ-RIS improves beamforming accuracy by up to 14.5% over classical autoencoders for an RIS with $N=512$ elements using $q=9$ qubits at a feedback compression ratio of approximately 19%. For $N=1024$, the framework continues to provide performance gains, achieving beamforming accuracy improvements of up to 11.33%. However, for larger RIS configurations with $N=2048$ elements, the performance gain decreases, with the best improvement dropping to 4.44% due to barren plateaus and near-Haar-random latent states, establishing the practical scalability limits of quantum-assisted compression.
Phase-based ranging is effective for mitigating relay attacks in remote keyless entry (RKE) systems; however, it typically requires costly radio frequency architecture redesigns to maintain phase coherency. Such hardware modifications increase development costs and compromise the low power consumption and compact integration advantages of vehicular systems-on-chip (SoCs). The fundamental issue arises when conventional transceivers switch frequencies for transmission and reception, rendering the local oscillator's (LO's) initial phase non-coherent. This paper proposes a digital-only phase compensation scheme that enables high-precision ranging on existing non-coherent transceiver architectures without hardware modifications to the radio frequency front-end. By estimating and compensating for phase variations introduced during frequency switching, this method utilizes a duplicated digital integrator architecture within the fractional-N phase-locked loop (PLL). This integrator operates independently of frequency-switching feedback, serving as a stable phase reference for precise estimation. Mathematical analysis and mixed-signal simulations at the register-transfer level verify that the proposed method achieves a steady-state detection error within $\pm 0.27^\circ$, ensuring robust performance for secure vehicular localization. With a minimal area increase of approximately 30% to the digital integrator block and power consumption of less than 1 mW, this approach can be seamlessly integrated into legacy systems to support secure localization, dynamic modulation (e.g., frequency shift keying (FSK)), and phase-sensitive applications such as angle-of-arrival (AoA) estimation.
With the unprecedented growth of wireless devices and data-intensive applications across diverse communication domains, there is an increasing demand for enhanced spectral efficiency, wider coverage, and efficient power utilization. In this context, intelligent reflecting surfaces (IRS) and non-orthogonal multiple access (NOMA) have emerged as promising technologies to enhance coverage, capacity, and user fairness. While a large body of literature exists on their individual and joint performance, the development of a unified and generalized analytical framework remains largely unexplored for IRS-NOMA systems. To address these gaps, this work investigates an IRS NOMA system with receiver diversity under a generalized fading environment characterized by exponential and confluent hypergeometric functions, namely, $\kappa -\mu$/shadowed, extended $\eta -\mu$, and Beaulieu-Xie/shadowed distributions. The proposed framework improves both spectral efficiency and signal-to-noise ratio. Recognizing that ideal transceiver hardware is not available in practice, the analysis is extended to account for inherent hardware impairments, offering insights into the performance of realistic communication setups. Closed-form analytical expressions are derived for several key performance metrics, including the amount of fading, channel quality estimation index, rate outage probability, channel capacity under optimal rate adaptation, and channel inversion with fixed rate schemes. Additionally, asymptotic analysis is provided for both low- and high-power regimes to yield deeper insights into system behavior. The derived analytical results are validated through Monte-Carlo simulations, which demonstrate an excellent match with theoretical predictions, confirming their accuracy and practical relevance.
Reliable long-range maritime communication remains challenging for shore-based systems due to the radio-horizon limitation and the strong spatial variability of sea-surface propagation. This paper investigates an evaporation-duct-aware multi-waypoint maritime communication mission, where a surface vessel sequentially visits prescribed task waypoints and offloads the data demand generated at each waypoint to a shore-based base station. By exploiting an evaporation-duct-aware channel gain map (CGM), we formulate a bi-objective trajectory optimization problem that jointly minimizes communication time and mission duration under waypoint-ordering, segment-wise communication-completion, and maneuverability constraints. To solve this long-horizon continuous-control problem, we develop a map-assisted Proximal Policy Optimization (PPO) framework, termed DuctMap-PPO. The proposed method adopts a Beta-policy parameterization to generate feasible bounded heading increments and designs a stage-gated reward mechanism to coordinate communication-demand completion and waypoint progression under different communication—navigation preferences. Simulation results show that CGM assistance is essential for efficient multi-waypoint maritime communication. Compared with a population-based multi-objective baseline and standard PPO, DuctMap-PPO achieves a better Pareto front at a lower computational cost. The results further demonstrate interpretable trajectory behaviors under different preference weights, useful adaptability across different evaporation duct conditions, and the effectiveness of the proposed action parameterization and reward design.
Autonomous vehicular networks (AVNs) supported by multi-access edge computing (MEC) require reliable execution of latency-critical workloads under dynamic traffic, wireless, and computing conditions. Existing resource-allocation methods often lack the ability to jointly provide system-level coordination, real-time adaptability, and dependency-aware execution for directed acyclic graph (DAG)-based autonomous-driving workloads. This paper proposes GAPPO-AVNs, a hierarchical GA-bounded Proximal Policy Optimization (PPO) framework for delay-constrained energy optimisation in MEC-enabled AVNs. The key novelty lies in a feasibility-guided GA–PPO coordination mechanism, where a genetic algorithm (GA) periodically generates global resource bounds for CPU frequency, transmission power, bandwidth allocation, and task offloading, while PPO performs online refinement within the GA-bounded feasible region through a Monitor–Analyse–Plan–Execute (MAPE) control loop. A DAG-based task model is also incorporated to capture subtask precedence constraints and support dependency-aware parallel execution. Simulation results show that GAPPO-AVNs improves the energy–delay trade-off, task-completion reliability, and policy stability compared with GA-only, PPO-only, PSO-based, DE-based, and heuristic baselines. These results confirm the effectiveness of GA-bounded action control for scalable resource allocation in MEC-enabled AVNs.
In the era of sixth-generation (6 G) and the Internet of Things (IoT), ultra-reliable and low-latency communication (URLLC) has emerged as a pivotal requirement, necessitating the use of short-packet communications (SPC) in the finite blocklength (FBL) regime. This paper investigates the performance of a downlink non-orthogonal multiple access (NOMA) system over fluctuating Nakagami-$m$ fading channels, which effectively capture the complex interplay between multipath scattering and shadowing severity. To bridge the gap between theory and practice, our analysis accounts for the impact of imperfect successive interference cancellation (SIC) and residual interference. By leveraging the properties of Meijer G-functions, we derive exact and asymptotic closed-form expressions for two key performance metrics: the average achievable rate (AAR) and the average block error rate (ABLER). The accuracy of the derived analytical framework is rigorously validated through extensive Monte Carlo simulations, demonstrating a perfect alignment across various system parameters. Our numerical results provide critical insights into the reliability-latency trade-offs, revealing that while NOMA offers superior spectral efficiency, it exhibits a distinct reliability penalty compared to orthogonal multiple access (OMA) in the short-packet regime. Furthermore, the study identifies power allocation trade-offs and quantifies how increasing the blocklength and fading parameters can mitigate the effects of channel dispersion and hardware impairments. These findings offer a robust theoretical foundation for the design and optimization of high-reliability, low-latency NOMA-based wireless networks.
This paper provides a comparative study of several stator windings' arrangements, to design a multi-three phase Surface Mounted Permanent Magnet electric machine as part of a powerline for aircraft electric propulsion that includes several three phase inverters, and a mechanical speed reducer. The objective of the study is to find the multi three-phase winding that gives the best compromise between all the aircraft requirements: maximum fault tolerance capability, and minimum powerline mass and losses. Following design considerations, a Half Bridge (HB) inverter, with a Y-connected distributed winding, was chosen for the electric motor. The multi three-phase winding modularity allows to operate in degraded mode, by turning off one or several inverters. However, the remaining available power depends on the winding arrangement, as well as the number of possible faults before a complete powerline failure. A qualitative comparison is therefore performed between the windings to assess their fault tolerance. Moreover, Design Optimization (DO) studies are carried out to compare the different modularity solutions in terms of power density and efficiency. The DO is performed on a system level, including the electric motor, the speed reducer and eventual passive filters that are necessary in faulty cases. Finally, some optimal motor designs are chosen and validated through Finite Element Analysis (FEA).
Orthogonal time-frequency space (OTFS) modulation meets the bit error rate (BER) requirements of modern wireless communication systems operating under high Doppler spreads. To enhance spectral efficiency, superimposed pilots are employed, with arrangements such as Zadoff-Chu sequences and sparse pilots proposed to minimize the peak-to-average power ratio (PAPR) of transmitted signals. Nevertheless, individual approaches fail to achieve optimal BER performance across diverse channel conditions due to inherent trade-offs. To overcome this limitation, we propose the flexible superimposed pilot (Flex-SP) framework that adaptively optimizes channel estimation and data detection. The method operates in two stages: an offline stage, where a reference database (RDB) is used to build a flexible adjustment database (FADB) through joint BER and complexity optimization; and an online stage, where the transmitter and receiver utilize the FADB to dynamically switch between different pilot arrangements while adjusting power and interference-cancellation iterations based on real-time channel state information (CSI). Simulation results show gains of up to an order of magnitude in BER, or up to a 100% reduction in interference-cancellation iterations, thereby significantly reducing computational cost, with negligible impact on BER performance.
