This tutorial describes the 5G New Radio Vehicle-to-Everything (5G NR-V2X) air interface, with a specific focus on the features and capabilities introduced in 3GPP Release 16. It begins by outlining the motivation for 5G NR-V2X and then progresses to the standardized definitions of the air interface, upper layer standards, and application protocols. Simulated performance on two classes of applications, urban intersection and highway merge is presented, leading to a conclusion that the lower layer standardization can address maneuver coordination – where nearby vehicles could effectively communicate to and therefore cooperate with nearby relevant vehicles. This portends a next and perhaps concluding step in realizing the full benefits of Cooperative, Connected, and Automated Mobility (CCAM) in Europe and down the line, in other global regions.
{"title":"A Tutorial on 5G NR-V2X: Enhancements, Real-World Applications, and Performance Evaluation","authors":"Abolfazl Hajisami;Ralf Weber;Jim Misener;Ahmed Farhan Hanif","doi":"10.1109/OJVT.2025.3637712","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3637712","url":null,"abstract":"This tutorial describes the 5G New Radio Vehicle-to-Everything (5G NR-V2X) air interface, with a specific focus on the features and capabilities introduced in 3GPP Release 16. It begins by outlining the motivation for 5G NR-V2X and then progresses to the standardized definitions of the air interface, upper layer standards, and application protocols. Simulated performance on two classes of applications, urban intersection and highway merge is presented, leading to a conclusion that the lower layer standardization can address maneuver coordination – where nearby vehicles could effectively communicate to and therefore cooperate with nearby relevant vehicles. This portends a next and perhaps concluding step in realizing the full benefits of Cooperative, Connected, and Automated Mobility (CCAM) in Europe and down the line, in other global regions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"41-53"},"PeriodicalIF":4.8,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11269721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729520","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}
Pub Date : 2025-11-18DOI: 10.1109/OJVT.2025.3634375
Liqiang Wang;Meng Wang
The increasing complexity of perception and decision-making tasks in intelligent connected vehicles has driven the evolution of on-board computing platforms toward heterogeneous architectures. However, the dynamic nature of workloads, the need for multi-objective optimization, and stringent safety constraints pose significant challenges to scheduling. To address the limitations of existing approaches in balancing multiple objectives and ensuring safety, this paper proposes a deep reinforcement learning (DRL)-based hierarchical hybrid-action multi-objective adaptive scheduling framework. The framework optimizes latency, energy consumption, reliability, and thermal management by introducing a dynamic weight adjustment mechanism driven by the battery state of charge (SOC) and thermal accumulation. It integrates high-level global task allocation with low-level real-time resource adjustment for adaptive multi-objective trade-offs, while embedding a functional safety fallback mechanism to guarantee hard real-time performance and thermal safety for high-criticality tasks. Experimental results under highway cruising, urban congestion, and high-temperature scenarios show that the proposed method outperforms HEFT, E-List, and Vanilla-DRL in p95 latency, energy consumption, peak temperature, and high-criticality task satisfaction: p95 latency is reduced by 6%–14%, energy consumption by 5%–20%, peak temperature by 2–8°C, and satisfaction rates exceed 97.5%. After model compression, the strategy network achieves inference latency under 5 ms and nearly 40% power reduction on an automotive-grade heterogeneous platform, validating the engineering feasibility of the approach. This work provides a scalable and safety-aware solution for efficient heterogeneous computing scheduling in intelligent vehicles.
{"title":"Deep Reinforcement Learning-Based Adaptive Scheduling for Intelligent Vehicle Heterogeneous Computing","authors":"Liqiang Wang;Meng Wang","doi":"10.1109/OJVT.2025.3634375","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3634375","url":null,"abstract":"The increasing complexity of perception and decision-making tasks in intelligent connected vehicles has driven the evolution of on-board computing platforms toward heterogeneous architectures. However, the dynamic nature of workloads, the need for multi-objective optimization, and stringent safety constraints pose significant challenges to scheduling. To address the limitations of existing approaches in balancing multiple objectives and ensuring safety, this paper proposes a deep reinforcement learning (DRL)-based hierarchical hybrid-action multi-objective adaptive scheduling framework. The framework optimizes latency, energy consumption, reliability, and thermal management by introducing a dynamic weight adjustment mechanism driven by the battery state of charge (SOC) and thermal accumulation. It integrates high-level global task allocation with low-level real-time resource adjustment for adaptive multi-objective trade-offs, while embedding a functional safety fallback mechanism to guarantee hard real-time performance and thermal safety for high-criticality tasks. Experimental results under highway cruising, urban congestion, and high-temperature scenarios show that the proposed method outperforms HEFT, E-List, and Vanilla-DRL in p95 latency, energy consumption, peak temperature, and high-criticality task satisfaction: p95 latency is reduced by 6%–14%, energy consumption by 5%–20%, peak temperature by 2–8°C, and satisfaction rates exceed 97.5%. After model compression, the strategy network achieves inference latency under 5 ms and nearly 40% power reduction on an automotive-grade heterogeneous platform, validating the engineering feasibility of the approach. This work provides a scalable and safety-aware solution for efficient heterogeneous computing scheduling in intelligent vehicles.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"108-123"},"PeriodicalIF":4.8,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11251222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729518","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}
Pub Date : 2025-11-11DOI: 10.1109/OJVT.2025.3631629
Yuhang Chen;Heyin Shen;Chong Han
The evolution of wireless communication toward next-generation networks introduces unprecedented demands on data rates, latency, and connectivity. To meet these requirements, two key trends have emerged: the use of higher communication frequencies to provide broader bandwidth, and the deployment of massive multiple-input multiple-output systems with large antenna arrays to compensate for propagation losses and enhance spatial multiplexing. These advancements significantly extend the Rayleigh distance, enabling near-field (NF) propagation alongside the traditional far-field (FF) regime. As user communication distances dynamically span both FF and NF regions, cross-field (CF) communication has also emerged as a practical consideration. Beam management (BM)—including beam scanning, channel state information estimation, beamforming, and beam tracking—plays a central role in maintaining reliable directional communications. While most existing BM techniques are developed for FF channels, recent works begin to address the unique characteristics of NF and CF regimes. This survey presents a comprehensive review of BM techniques from the perspective of propagation fields. We begin by building the basic through analyzing the modeling of FF, NF, and CF channels, along with the associated beam patterns for alignment. Then, we categorize BM techniques by methodologies, and discuss their operational differences across propagation regimes, highlighting how field-dependent channel characteristics influence design tradeoffs and implementation complexity. In addition, for each BM method, we identify open challenges and future research directions, including extending FF methods to NF/CF scenarios, developing unified BM strategies for field-agnostic deployment, and designing low-overhead BM solutions for dynamic environments.
{"title":"Cross Far- and Near-Field Beam Management Technologies in Millimeter-Wave and Terahertz MIMO Systems","authors":"Yuhang Chen;Heyin Shen;Chong Han","doi":"10.1109/OJVT.2025.3631629","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3631629","url":null,"abstract":"The evolution of wireless communication toward next-generation networks introduces unprecedented demands on data rates, latency, and connectivity. To meet these requirements, two key trends have emerged: the use of higher communication frequencies to provide broader bandwidth, and the deployment of massive multiple-input multiple-output systems with large antenna arrays to compensate for propagation losses and enhance spatial multiplexing. These advancements significantly extend the Rayleigh distance, enabling near-field (NF) propagation alongside the traditional far-field (FF) regime. As user communication distances dynamically span both FF and NF regions, cross-field (CF) communication has also emerged as a practical consideration. Beam management (BM)—including beam scanning, channel state information estimation, beamforming, and beam tracking—plays a central role in maintaining reliable directional communications. While most existing BM techniques are developed for FF channels, recent works begin to address the unique characteristics of NF and CF regimes. This survey presents a comprehensive review of BM techniques from the perspective of propagation fields. We begin by building the basic through analyzing the modeling of FF, NF, and CF channels, along with the associated beam patterns for alignment. Then, we categorize BM techniques by methodologies, and discuss their operational differences across propagation regimes, highlighting how field-dependent channel characteristics influence design tradeoffs and implementation complexity. In addition, for each BM method, we identify open challenges and future research directions, including extending FF methods to NF/CF scenarios, developing unified BM strategies for field-agnostic deployment, and designing low-overhead BM solutions for dynamic environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"73-107"},"PeriodicalIF":4.8,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11239413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729519","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}
The Vehicle-to-Ground (V2G) emergency communication network is a dedicated network established to respond to emergencies, such as natural disasters and traffic accidents, and it plays a crucial role in ensuring the safe and smooth operation of vehicles. Composed of numerous devices, this network is inevitably exposed to failure risks due to prolonged operation, complex designs, and insufficient management and maintenance. Faults in network nodes may undermine the reliability of vehicle-to-ground communication. Rapid fault localization is critical to the maintenance and management of network device. However, current localization methods face issues like excessively long probing paths, high localization costs, and low accuracy—all of which lead to subpar performance in real-world fault localization scenarios. To address these problems, we introduce a novel Multi-stage Group Probe (MGP) localization method, designed to balance localization cost and accuracy effectively. Specifically, we first present a network localization model and the concept of "uncertain information volume of network node states," which quantifies the cost and efficiency of localization. Second, leveraging graph theory, we propose the idea of network probing subgraphs and constrain the number of probing stations and probe lengths, while developing algorithms for selecting probing stations and planning probing paths. Additionally, we introduce a group probe localization method that incorporates information feedback to reduce costs. Finally, we evaluate the MGP against other probe localization approaches across different networks. Experimental results demonstrate that MGP outperforms comparative methods in terms of localization cost, accuracy, and efficiency.
{"title":"MGP: Multi-Stage Grouped Probe Detection for Fault Localization in Vehicle-to-Ground Communication Networks","authors":"Wenxiao Wang;Ping Dong;Yuyang Zhang;Wenxuan Qiao;Xiaoya Zhang;Chengxiao Yu;Hongke Zhang","doi":"10.1109/OJVT.2025.3630603","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3630603","url":null,"abstract":"The Vehicle-to-Ground (V2G) emergency communication network is a dedicated network established to respond to emergencies, such as natural disasters and traffic accidents, and it plays a crucial role in ensuring the safe and smooth operation of vehicles. Composed of numerous devices, this network is inevitably exposed to failure risks due to prolonged operation, complex designs, and insufficient management and maintenance. Faults in network nodes may undermine the reliability of vehicle-to-ground communication. Rapid fault localization is critical to the maintenance and management of network device. However, current localization methods face issues like excessively long probing paths, high localization costs, and low accuracy—all of which lead to subpar performance in real-world fault localization scenarios. To address these problems, we introduce a novel Multi-stage Group Probe (MGP) localization method, designed to balance localization cost and accuracy effectively. Specifically, we first present a network localization model and the concept of \"uncertain information volume of network node states,\" which quantifies the cost and efficiency of localization. Second, leveraging graph theory, we propose the idea of network probing subgraphs and constrain the number of probing stations and probe lengths, while developing algorithms for selecting probing stations and planning probing paths. Additionally, we introduce a group probe localization method that incorporates information feedback to reduce costs. Finally, we evaluate the MGP against other probe localization approaches across different networks. Experimental results demonstrate that MGP outperforms comparative methods in terms of localization cost, accuracy, and efficiency.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"27-40"},"PeriodicalIF":4.8,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11235598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674876","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}
In industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional information from a limited and varying number of wireless transmitters between consecutive time steps, leading to ambiguities in wireless infrastructure-based positioning. In this paper, we propose PosGNN, a novel data fusion solution based on the Graph Neural Network (GNN) approach that allows us to estimate the position of the User Equipment (UE) by fusing the positional information from the available wireless transmitters at each time step with the UE sensor technology. The performance of the proposed method is assessed using an experimental setup of Ultra-Wideband (UWB) technology as wireless infrastructure at $3.7 - text{4.2},text{GHz}$ frequency band, the Inertial Measurement Unit (IMU) as UE-side sensor, and the Automated Guided Vehicle (AGV) as the target UE to be positioned. The experimental results demonstrate the exceptional performance of our approach over the conventional model-based approach, Extended Kalman Filter (EKF), and the data-driven approach, Deep Neural Network (DNN), achieving an average positioning error of less than $text{15},text{cm}$ in harsh industrial environments.
{"title":"PosGNN: A Graph Neural Network Based Multimodal Data Fusion for Indoor Positioning in Industrial Non-Line-of-Sight Scenarios","authors":"Karthik Muthineni;Alexander Artemenko;Daniel Abode;Josep Vidal;Montse Nájar","doi":"10.1109/OJVT.2025.3630970","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3630970","url":null,"abstract":"In industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional information from a limited and varying number of wireless transmitters between consecutive time steps, leading to ambiguities in wireless infrastructure-based positioning. In this paper, we propose PosGNN, a novel data fusion solution based on the Graph Neural Network (GNN) approach that allows us to estimate the position of the User Equipment (UE) by fusing the positional information from the available wireless transmitters at each time step with the UE sensor technology. The performance of the proposed method is assessed using an experimental setup of Ultra-Wideband (UWB) technology as wireless infrastructure at <inline-formula><tex-math>$3.7 - text{4.2},text{GHz}$</tex-math></inline-formula> frequency band, the Inertial Measurement Unit (IMU) as UE-side sensor, and the Automated Guided Vehicle (AGV) as the target UE to be positioned. The experimental results demonstrate the exceptional performance of our approach over the conventional model-based approach, Extended Kalman Filter (EKF), and the data-driven approach, Deep Neural Network (DNN), achieving an average positioning error of less than <inline-formula><tex-math>$text{15},text{cm}$</tex-math></inline-formula> in harsh industrial environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"15-26"},"PeriodicalIF":4.8,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11235985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612200","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}
Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on tasks that require both visual and textual understanding. Their strong generalisation abilities make them a promising component for automated driving systems, which must handle unexpected corner cases. However, to be trusted in such safety-critical applications, a model must first possess a reliable perception system. Since critical objects and agents in traffic scenes are often at a distance, we require systems that are not “shortsighted,” i.e., systems with strong perception capabilities at both close (up to 20 meters) and long (30+ meters) range. With this in mind, we introduce Distance-Annotated Traffic Perception Question Answering (DTPQA), the first Visual Question Answering (VQA) benchmark focused solely on perception-based questions in traffic scenes, enriched with distance annotations. By excluding questions that require reasoning, we ensure that model performance reflects perception capabilities alone. Since automated driving hardware has limited processing power and cannot support large VLMs, our study centers on smaller VLMs. We evaluate several state-of-the-art (SOTA) small VLMs on DTPQA and show that, despite the simplicity of the questions, these models significantly underperform compared to humans (∼60% average accuracy for the best-performing small VLM versus ∼85% human performance). However, the human sample size was relatively small, which imposes statistical limitations. We also identify specific perception tasks, such as distinguishing left from right, that remain particularly challenging. We hope our findings will encourage further research into improving the perception capabilities of small VLMs in traffic scenarios, making them more suitable for automated driving applications.
{"title":"Evaluating Small Vision-Language Models on Distance-Dependent Traffic Perception","authors":"Nikos Theodoridis;Tim Brophy;Reenu Mohandas;Ganesh Sistu;Fiachra Collins;Anthony Scanlan;Ciarán Eising","doi":"10.1109/OJVT.2025.3629318","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3629318","url":null,"abstract":"Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on tasks that require both visual and textual understanding. Their strong generalisation abilities make them a promising component for automated driving systems, which must handle unexpected corner cases. However, to be trusted in such safety-critical applications, a model must first possess a reliable perception system. Since critical objects and agents in traffic scenes are often at a distance, we require systems that are not “shortsighted,” i.e., systems with strong perception capabilities at both close (up to 20 meters) and long (30+ meters) range. With this in mind, we introduce Distance-Annotated Traffic Perception Question Answering (DTPQA), the first Visual Question Answering (VQA) benchmark focused solely on perception-based questions in traffic scenes, enriched with distance annotations. By excluding questions that require reasoning, we ensure that model performance reflects perception capabilities alone. Since automated driving hardware has limited processing power and cannot support large VLMs, our study centers on smaller VLMs. We evaluate several state-of-the-art (SOTA) small VLMs on DTPQA and show that, despite the simplicity of the questions, these models significantly underperform compared to humans (∼60% average accuracy for the best-performing small VLM versus ∼85% human performance). However, the human sample size was relatively small, which imposes statistical limitations. We also identify specific perception tasks, such as distinguishing left from right, that remain particularly challenging. We hope our findings will encourage further research into improving the perception capabilities of small VLMs in traffic scenarios, making them more suitable for automated driving applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"54-72"},"PeriodicalIF":4.8,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11230063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729342","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}
Pub Date : 2025-11-04DOI: 10.1109/OJVT.2025.3628652
Louis-Romain Joly;Vivien Lacorre;Krister Wolff
Efficient representation and querying of railway networks are crucial for autonomous railway systems and digital infrastructure management. This paper introduces VEctorial Railway NEtwork (VERNE), an interpretable data structure and algorithm that integrates vector-based spatial partitioning with a railway-specific topological framework to enhance network representation and navigation. VERNE is designed to optimize query efficiency, reduce memory footprint, and ensure scalability for real-time applications. Its internal mechanism results from a comparative performance analysis between a $k$-d tree, an STRtree and two custom algorithms, highlighting trade-offs in computational efficiency and memory overhead. The proposed approach is validated using datasets from both the French and Swedish railway networks, demonstrating its effectiveness in real-world scenarios. The results indicate that VERNE provides a robust and scalable solution for railway infrastructure modeling, offering improvements in localization speed and computational efficiency. Another advantage is that it inherently manipulates atomic elements which can contain any information relevant to directly perform navigation onboard an autonomous robot. This work contributes to the advancement of railway digitalization by providing a structured methodology for spatial data processing in autonomous railway systems.
{"title":"VERNE: A Spatial Data Structure Representing Railway Networks for Autonomous Robot Navigation","authors":"Louis-Romain Joly;Vivien Lacorre;Krister Wolff","doi":"10.1109/OJVT.2025.3628652","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3628652","url":null,"abstract":"Efficient representation and querying of railway networks are crucial for autonomous railway systems and digital infrastructure management. This paper introduces <bold>VEctorial Railway NEtwork (VERNE)</b>, an interpretable data structure and algorithm that integrates vector-based spatial partitioning with a railway-specific topological framework to enhance network representation and navigation. VERNE is designed to optimize query efficiency, reduce memory footprint, and ensure scalability for real-time applications. Its internal mechanism results from a comparative performance analysis between a <inline-formula><tex-math>$k$</tex-math></inline-formula>-d tree, an STRtree and two custom algorithms, highlighting trade-offs in computational efficiency and memory overhead. The proposed approach is validated using datasets from both the French and Swedish railway networks, demonstrating its effectiveness in real-world scenarios. The results indicate that VERNE provides a robust and scalable solution for railway infrastructure modeling, offering improvements in localization speed and computational efficiency. Another advantage is that it inherently manipulates atomic elements which can contain any information relevant to directly perform navigation onboard an autonomous robot. This work contributes to the advancement of railway digitalization by providing a structured methodology for spatial data processing in autonomous railway systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1-14"},"PeriodicalIF":4.8,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11224796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595123","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}
Pub Date : 2025-10-30DOI: 10.1109/OJVT.2025.3627139
Priyanka Maity;Deepika Harish;Suraj Srivastava;Aditya K. Jagannatham;Lajos Hanzo
With the growing demand for integrated sensing and communication (ISAC) in next-generation wireless networks, efficient target localization techniques conceived for mmWave MIMO systems have becomeincreasingly important. In this context, we propose a Sparse Bayesian Learning (SBL)-aided extended target localization framework for orthogonal frequency division multiplexing (OFDM)-based mmWave MIMO systems. The proposed approach explicitly considers the impact of intercarrier interference (ICI) arising in mobile environments, which is often overlooked in conventional schemes. Our framework is designed for hybrid mmWave MIMO architectures, where the number of radio frequency (RF) chains is considerably lower than the number of antennas, ensuring hardware efficiency. To achieve high-precision target localization, we introduce a delay, Doppler, and angular (DDA)-domain representation of the target, enabling accurate estimation of target parameters. The proposed algorithm leverages the inherent three-dimensional (3D) sparsity in the DDA domain of the scattering environment and employs the powerful SBL framework for effective parameter estimation. Furthermore, to address practical scenarios where the actual target parameters may not align with finite-resolution grids, we develop an enhanced off-grid SBL (OSBL) method based on super-resolution principles. This recursive grid refinement approach progressively improves the estimation accuracy. Additionally, we derive the Cramér-Rao bound (CRB) and Bayesian CRB to theoretically characterize the achievable estimation performance. Simulation results confirm that the proposed method significantly outperforms existing algorithms in terms of estimation accuracy and robustness.
{"title":"Super-Resolution-Based Bayesian Learning for the Localization of Extended Targets in mmWave MIMO OFDM Systems","authors":"Priyanka Maity;Deepika Harish;Suraj Srivastava;Aditya K. Jagannatham;Lajos Hanzo","doi":"10.1109/OJVT.2025.3627139","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3627139","url":null,"abstract":"With the growing demand for integrated sensing and communication (ISAC) in next-generation wireless networks, efficient target localization techniques conceived for mmWave MIMO systems have becomeincreasingly important. In this context, we propose a Sparse Bayesian Learning (SBL)-aided extended target localization framework for orthogonal frequency division multiplexing (OFDM)-based mmWave MIMO systems. The proposed approach explicitly considers the impact of intercarrier interference (ICI) arising in mobile environments, which is often overlooked in conventional schemes. Our framework is designed for hybrid mmWave MIMO architectures, where the number of radio frequency (RF) chains is considerably lower than the number of antennas, ensuring hardware efficiency. To achieve high-precision target localization, we introduce a delay, Doppler, and angular (DDA)-domain representation of the target, enabling accurate estimation of target parameters. The proposed algorithm leverages the inherent three-dimensional (3D) sparsity in the DDA domain of the scattering environment and employs the powerful SBL framework for effective parameter estimation. Furthermore, to address practical scenarios where the actual target parameters may not align with finite-resolution grids, we develop an enhanced off-grid SBL (OSBL) method based on super-resolution principles. This recursive grid refinement approach progressively improves the estimation accuracy. Additionally, we derive the Cramér-Rao bound (CRB) and Bayesian CRB to theoretically characterize the achievable estimation performance. Simulation results confirm that the proposed method significantly outperforms existing algorithms in terms of estimation accuracy and robustness.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"3000-3016"},"PeriodicalIF":4.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11222915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560673","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}
Pub Date : 2025-10-22DOI: 10.1109/OJVT.2025.3623883
Hugo Hawkins;Chao Xu;Lie-Liang Yang;Lajos Hanzo
Affine Frequency Division Multiplexing (AFDM) has attracted substantial research interest due to its implementational similarity to Orthogonal Frequency-Division Multiplexing (OFDM), whilst attaining comparable performance to Orthogonal Time Frequency Space (OTFS). Hence, we embark on an in-depth performance characterisation of coded AFDM and of its equivalent OTFS counterpart. Soft-Minimum Mean Square Error (MMSE) reception taking into account a priori probabilities in the weighting matrix is applied in conjunction with Recursive Systematic Convolutional (RSC)- and RSCUnity Rate Convolutional (URC) coding to AFDM. Iterative decoding convergence analysis is carried out with the aid of the powerful semi-analytical tool of EXtrinsic Information Transfer (EXIT) chart, and its Bit Error Rate (BER) performance is compared to OFDM and to the equivalent OTFS configurations. As there are no consistent configurations of AFDM and OTFS utilised in the literature to compare their relative performances, two AFDM configurations and three OTFS configurations are considered. The results show that the BER of AFDM is lower than that of the equivalent OTFS configurations at high Energy per bit over Noise power (E$_{mathrm{{b}}}$/N$_{0}$) for small system matrix dimensions, for a low number of iterations, and for high code rates. In all other cases, the BER of AFDM is shown to be similar to that of its equivalent OTFS configurations. Given that the RSC BER performance fails to improve beyond two iterations, this solution is recommended for low-complexity transceivers. By contrast, if the extra complexity of the RSC-URC aided transceiver is affordable, an extra (E$_{mathrm{{b}}}$/N$_{0}$) gain of 1.8 dB may be attained at a BER of $10^{-5}$ and a code rate of 0.5.
仿射频分复用（AFDM）由于其实现与正交频分复用（OFDM）相似，同时获得与正交时频空间（OTFS）相当的性能而引起了大量的研究兴趣。因此，我们着手对编码AFDM及其等效OTFS对立物进行深入的性能表征。考虑到加权矩阵中的先验概率的软最小均方误差（MMSE）接收与递归系统卷积（RSC）和RSCUnity Rate卷积（URC）编码一起应用于AFDM。借助强大的外部信息传输（EXtrinsic Information Transfer， EXIT）图半分析工具进行了迭代译码收敛分析，并将其误码率（BER）性能与OFDM和等效OTFS配置进行了比较。由于在文献中没有使用一致的AFDM和OTFS配置来比较它们的相对性能，因此考虑两种AFDM配置和三种OTFS配置。结果表明，当系统矩阵维数小、迭代次数少、码率高时，AFDM的误码率比等效OTFS配置的误码率低。在所有其他情况下，AFDM的误码率显示与其等效OTFS配置的误码率相似。考虑到RSC误码率的性能在两次迭代之后就无法提高，建议将此解决方案用于低复杂度的收发器。相比之下，如果RSC-URC辅助收发器的额外复杂性是可以承受的，则可以在BER为10^{-5}$和码率为0.5的情况下获得1.8 dB的额外增益（E$_{ mathm {{b}}}$/N$_{0}$）。
{"title":"Iterative Soft-MMSE Detection Aided AFDM and OTFS","authors":"Hugo Hawkins;Chao Xu;Lie-Liang Yang;Lajos Hanzo","doi":"10.1109/OJVT.2025.3623883","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3623883","url":null,"abstract":"Affine Frequency Division Multiplexing (AFDM) has attracted substantial research interest due to its implementational similarity to Orthogonal Frequency-Division Multiplexing (OFDM), whilst attaining comparable performance to Orthogonal Time Frequency Space (OTFS). Hence, we embark on an in-depth performance characterisation of coded AFDM and of its equivalent OTFS counterpart. Soft-Minimum Mean Square Error (MMSE) reception taking into account <italic>a priori</i> probabilities in the weighting matrix is applied in conjunction with Recursive Systematic Convolutional (RSC)- and RSCUnity Rate Convolutional (URC) coding to AFDM. Iterative decoding convergence analysis is carried out with the aid of the powerful semi-analytical tool of EXtrinsic Information Transfer (EXIT) chart, and its Bit Error Rate (BER) performance is compared to OFDM and to the equivalent OTFS configurations. As there are no consistent configurations of AFDM and OTFS utilised in the literature to compare their relative performances, two AFDM configurations and three OTFS configurations are considered. The results show that the BER of AFDM is lower than that of the equivalent OTFS configurations at high Energy per bit over Noise power (E<inline-formula><tex-math>$_{mathrm{{b}}}$</tex-math></inline-formula>/N<inline-formula><tex-math>$_{0}$</tex-math></inline-formula>) for small system matrix dimensions, for a low number of iterations, and for high code rates. In all other cases, the BER of AFDM is shown to be similar to that of its equivalent OTFS configurations. Given that the RSC BER performance fails to improve beyond two iterations, this solution is recommended for low-complexity transceivers. By contrast, if the extra complexity of the RSC-URC aided transceiver is affordable, an extra (E<inline-formula><tex-math>$_{mathrm{{b}}}$</tex-math></inline-formula>/N<inline-formula><tex-math>$_{0}$</tex-math></inline-formula>) gain of 1.8 dB may be attained at a BER of <inline-formula><tex-math>$10^{-5}$</tex-math></inline-formula> and a code rate of 0.5.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2944-2959"},"PeriodicalIF":4.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510082","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}
Pub Date : 2025-10-20DOI: 10.1109/OJVT.2025.3623913
Adam Weaver;Annette von Jouanne;Douglas Sicker;Alex Yokochi
Modern vehicles are increasingly more cyber-physical as well as more connected with each passing year. Manufacturers are innovating new technologies (including performance, automation, comfort, and safety) that enhance the driver/passenger experience and continue to move towards increased automation. However, each of these cyber-physical systems poses a potential additional vulnerable attack surface for malicious actors to exploit. Due to high costs, safety risks, and logistical difficulties of testing full vehicles in motion, most research in assessing the cybersecurity of vehicles has focused on simulation, vehicle subsystem(s), or constricted case studies, and not real-world vehicle testing and realistic human interaction assessment. To address this shortcoming, hardware-in-the-loop (HIL) cyber vulnerability testing of fully operational vehicles is needed. This paper presents a review of vehicle cybersecurity research and testing including common technical, logistical, and human factors issues as well as current regulations and guidance. Informative research-focused case studies are presented followed by a proposed cybersecurity vehicle-in-the-loop testbed integrated with a dynamometer to provide a comprehensive, robust, and safe test environment where true effects of cyber testing can be evaluated on a complete vehicle.
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