IEEE Open Journal of Vehicular Technology最新文献

{"title":"BFRN-IoV: Blockchain and Fog-Enabled Route Navigation for Digital Twin-Based Internet of Vehicles","authors":"Amrendra Singh Yadav;Mihir Bhatt;Sameer Yadav;Sanjeev Kumar Dwivedi;Harish Kumar Shakya","doi":"10.1109/OJVT.2026.3698109","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3698109","url":null,"abstract":"The rapid evolution of the Internet of Vehicles (IoV) necessitates secure, scalable, and low-latency route navigation mechanisms that can operate in highly dynamic vehicular environments. Emerging paradigms such as Vehicular Digital Twins (VDTs) further enhance IoV ecosystems by enabling real-time virtual representations of physical vehicles, facilitating predictive analytics, intelligent decision-making, and context-aware routing. However, conventional VANET-based approaches suffer from centralized trust dependencies, high computational overhead, and limited adaptability to real-time traffic conditions. This paper proposes BFRN-IoV, a blockchain- and fog-enabled route navigation framework that integrates lightweight ECC-HMAC-based mutual authentication, RSU-assisted fog routing, and global route validation via a Geo-Location Provider (GLP), while leveraging VDTs for enhanced situational awareness and dynamic route optimization. The framework ensures key security properties-including confidentiality, integrity, pseudonymity, unlinkability, and non-repudiation-using ECDH-derived session keys, HKDF-based key expansion, and HMAC verification, while preserving privacy through pseudonym-based identity management. A permissioned blockchain provides immutable and auditable logging of routing interactions without exposing vehicle identities. Simulation results using SUMO and implementation via Web3 demonstrate significant improvements in routing accuracy, along with reduced communication and computational overhead compared to existing approaches. Formal verification using the Scyther tool confirms robustness against replay, impersonation, and man-in-the-middle attacks. The proposed framework delivers a unified, secure, and efficient solution for real-time IoV route navigation, further strengthened by the integration of VDTs in next-generation intelligent transportation systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1701-1714"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11539974","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Multi-Objective EV Charging Placement with Discrete-Event Simulation","authors":"Ajmal Khan;Ahmad Suleman;Mohammed M. Bait-Suwailam;Abdul Jabbar Siddiqui;Shujaat Khan;Zeeshan Kaleem","doi":"10.1109/OJVT.2026.3703811","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3703811","url":null,"abstract":"Planning public electric vehicle charging station (EVCS) infrastructure requires more than coverage-based placement. In practice, effective deployment must jointly account for spatial reach, accessibility, congestion, queuing, and charging feasibility under uncertain demand. However, existing methods either optimize these objectives in isolation or incur high computational cost when operational realism is introduced. We address this challenge through a simulation-oriented benchmarking platform and the proposed multi-objective Hierarchical Charging Operations and Placement (HCOP) optimization framework. The platform models the charging ecosystem end-to-end, including road-network structure, stochastic origin–destination demand, vehicle energy constraints, station service dynamics, user choice behavior, and discrete-event simulation of queuing processes. Building on this environment, HCOP integrates coverage seeding, access-time refinement, and failure-aware screening into a unified, interpretable decision pipeline. The central contribution is an integrated optimization–simulation framework in which candidate placements are evaluated using system-level performance measures rather than static spatial proxies. Experiments across diverse network topologies, planning budgets, and demand-stress regimes show that HCOP achieves improved balance across coverage, access time, waiting time, detour, failure rate, and utilization compared to representative baselines, while maintaining favorable computational scaling. These results demonstrate that HCOP provides a scalable, operationally grounded approach to EVCS planning under uncertainty.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2559-2573"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11563820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital-Only Phase Compensation for Secure Phase-Based Ranging in Vehicular RKE Systems","authors":"Masaki Nishikawa;Shoji Otaka;Ichiro Seto;Tomoaki Otsuki","doi":"10.1109/OJVT.2026.3720811","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3720811","url":null,"abstract":"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 <inline-formula><tex-math>$pm 0.27^circ$</tex-math></inline-formula>, 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.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2508-2517"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11642576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Does Multi-Operator Cellular Redundancy Help V2X? A Cross-Layer Measurement Study","authors":"Masahiro Yoshida","doi":"10.1109/OJVT.2026.3730442","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3730442","url":null,"abstract":"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 <italic>R</i> = 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 <inline-formula><tex-math>${sim }97.5%$</tex-math></inline-formula>; at most <inline-formula><tex-math>${sim }5%$</tex-math></inline-formula> 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 <italic>correlated</i>-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.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2716-2730"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11676386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Autonomous Ground Vehicle Path Planning in Unstructured Environments","authors":"Cheng Li;Xiangyang Xu;Yifei Han;Xitao Wu","doi":"10.1109/OJVT.2026.3735213","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3735213","url":null,"abstract":"As the demand for autonomous ground vehicles (AGVs) in complex unstructured environments continues to grow, their autonomous navigation capabilities are also facing increasingly higher requirements. Unstructured scenarios typically feature irregular terrain elevations, which poses core challenges to traditional path planning algorithms, including insufficient terrain traversability assessment, rough modeling of dynamic constraints, and poor coupling in multi-objective optimization. These challenges can lead to safety risks and execution failures in planned paths. To address these issues, this paper proposes a path planning method that integrates platform physical constraints and multi-dimensional optimization. We construct a multi-dimensional constraint model that includes geometric traversability, motion stability, and power availability, and transform it into hard boundary conditions and dynamically weighted cost terms for path search. Furthermore, we propose an improved A* algorithm-based path planning architecture that, through a dynamic constraint feedback mechanism, achieves closed-loop coupling between theoretical constraints and planning decisions, systematically addressing the challenges of autonomous navigation in unstructured environments. Experimental results show that the comprehensive performance of path planning has improved by more than 8%.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2834-2850"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11695100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RESCUE-ISAC: Energy-Efficient Active–Passive Beamforming for Emergency UAV-ISAC in 6G Networks","authors":"Ruhul Amin Khalil;Saba Mahmood;Tariqullah Jan;Junaid Bahadar Khan;Bushra Bashir","doi":"10.1109/OJVT.2026.3717136","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3717136","url":null,"abstract":"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.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2285-2300"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11623463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Unified Computationally Efficient Model Predictive Control for Multilevel Inverter–Fed Multiphase PMSM Drives for Electric Vehicles","authors":"Aenugu Mastanaiah;Tejavathu Ramesh;Tangirala Isradani;Praveen Kumar Bonthagorla","doi":"10.1109/OJVT.2026.3672614","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3672614","url":null,"abstract":"Multiphase PMSM drives fed by multilevel inverters are attractive for transportation electrification because they deliver high reliability, smooth torque, and good voltage quality. However, finite-control-set MPC in these multiphase, high-level converters suffers from an exponential growth of switching candidates, which jeopardizes real-time implementation on automotive-grade controllers. This paper aims to develop a generalized MPC scheme that preserves dynamic performance while drastically reducing computational burden. The proposed method restricts the evaluation to at most <inline-formula><tex-math>$ 2m$</tex-math></inline-formula> (with <inline-formula><tex-math>$m$</tex-math></inline-formula> denoting the number of phases) neighbouring switching states around the present vector, independent of inverter level, thereby shrinking the search space from <inline-formula><tex-math>$n^{m}$</tex-math></inline-formula> (where <inline-formula><tex-math>$n$</tex-math></inline-formula> is the number of inverter levels) to a small fixed set. An outer ultra-local model-free speed controller generates the <inline-formula><tex-math>$i_{q}^{ast }$</tex-math></inline-formula> reference directly from speed and current measurements, removing PI retuning across machines and converter configurations. Simulation and dSPACE-based experimental results on three- and five-phase PMSM drives with three-, five-, and seven-level inverters show balanced, nearly sinusoidal currents with reduced current THD and torque ripple comparable to or better than conventional MPC, while keeping execution time within the <inline-formula><tex-math>$&lt; 100,mu text{s}$</tex-math></inline-formula> budget for 10 kHz operation. These results confirm the real-time feasibility, harmonic-quality improvement, and scalability of the proposed strategy for multiphase, multilevel traction drives.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1021-1035"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RLC3-LPI: A Reinforcement Learning-Based Multi-UAV Cooperative Communication Coverage Algorithm With Low Interception Probability","authors":"Shihong Zhao;Furong Yang;Qihui Wu;Fangwei Ye;Lijuan Zhang","doi":"10.1109/OJVT.2026.3668852","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3668852","url":null,"abstract":"In low-altitude applications, Unmanned Aerial Vehicles (UAVs) have gained widespread adoption, expanding their roles across diverse domains such as search and rescue, communication coverage, and security surveillance. In complex and remote environments, UAVs are frequently deployed as communication relays to support ground search units (GUs). However, a critical challenge persists in ensuring effective communication coverage for GUs during penetration missions while simultaneously minimizing interception risk. To address this challenge, we propose a multi-agent reinforcement learning-based cooperative communication coverage algorithm with low probability of interception (RLC<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>-LPI) for multi-UAV trajectory planning. Our approach employs a centralized training and decentralized execution paradigm, eliminating the UAV swarm's reliance on global environmental awareness and enhancing scalability in real-world scenarios. Within RLC<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>-LPI, the low-intercept coverage problem is formulated as a partially observable Markov decision process, and a federation-enhanced deep reinforcement learning algorithm is developed, incorporating meticulously designed reward functions to optimize connectivity, coverage efficiency, and interception avoidance. Extensive experimental results demonstrate that RLC<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>-LPI outperforms existing benchmarks, achieving higher coverage and connectivity rates while significantly reducing interception probability.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"975-990"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11417716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Sustainable LEO Satellite Operations: A Degradation-Aware Scheduling Framework for DoD Control and Energy Optimization","authors":"Ahmad Y. Alhusenat;Jinjin Tian;Hana Rababah;Qizhou Wang;Lei You;Xingjun Zhang;Lei Lei;Symeon Chatzinotas","doi":"10.1109/OJVT.2026.3693843","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3693843","url":null,"abstract":"Next-generation low earth orbit (LEO) satellite constellations demand the seamless integration of communication, sensing, and computation capabilities under stringent energy, thermal, and longevity constraints. This paper proposes an optimization framework that couples adaptive satellite selection with a sequential Lyapunov-based resource allocation to enable sustainable constellation operations. Deployed in a hybrid centralized-distributed architecture, the framework performs satellite selection at ground control stations while enabling autonomous onboard resource optimization.The adaptive selection mechanism uses diversity-weighted scoring across six real-time metrics, including interference awareness, for improved load balancing and interference-aware task assignment. A sequential optimization approach first derives a closed-form solution for CPU frequency under thermal and timing constraints, followed by a convex formulation for transmit power. This is integrated with a Lyapunov drift-plus-penalty framework that manages a sophisticated battery pack system using discrete binary cell-level assignment for both charging and discharging. This degradation-aware system autonomously maintains the depth-of-discharge (DoD) below 25% per cell, enforces thermal safety margins, and guarantees queue stability, ensuring long-term operational integrity. The overall problem is decomposed into polynomial-time stages suitable for real-time deployment. Simulation results demonstrate that the proposed framework reduces energy consumption by up to 84%, achieves an average DoD of 22.23% with all cells remaining below the 25% threshold, and extends the battery’s expected cycle life by 28.6% compared to baseline methods.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1457-1473"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520902","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Channel Estimation Assisted Bistatic Localization in Terrestrial and Non-Terrestrial Networks","authors":"Kunlun Li;Chao Zhang;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo","doi":"10.1109/OJVT.2026.3683456","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3683456","url":null,"abstract":"Low-Earth-orbit (LEO) satellites are regarded as a key enabler for 6G communications and localization, due to their large coverage beyond conventional terrestrial networks. In this paper, we propose a downlink LEO base station (BS) bistatic localization framework relying on hybrid beamforming that alleviates the reliance on ultra-fine angle estimation by jointly exploiting time-frequency-spatial observations. A multiple-measurement-vector (MMV) based sparse model is constructed for attaining accurate channel gains and angles from limited pilots and moderate array sizes, where a modified block orthogonal matching pursuit (BOMP) algorithm is proposed to enhance robustness under highly correlated sensing matrices for localization purposes. After geometry-based timing-advance and Doppler pre-compensation at the BS, a two-dimensional (2D) upsampling matched filter having fine delay-Doppler grids is applied to estimate the residual time of arrival (ToA) and Doppler frequency. Then, the final user equipment (UE) position is obtained by intersecting the BS-centered angle-of-arrival (AoA) ray with a bistatic-range ellipse derived from the residual delay. The numerical results under realistic LEO-BS bistatic scenarios demonstrate that the proposed scheme achieves meter-level localization accuracy and highlight the performance gains attained by increasing the number of pilot symbols, subcarriers, and angular resolutions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1234-1247"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11482486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}