This paper proposes a two-stage approach for passive and active beamforming in multiple-input multiple-output (MIMO) interference channels (ICs) assisted by a beyond-diagonal reconfigurable intelligent surface (BD-RIS). In the first stage, the passive BD-RIS is designed to minimize the aggregate interference power at all receivers, a cost function called interference leakage (IL). To this end, we propose an optimization algorithm in the manifold of unitary matrices and a suboptimal but computationally efficient solution. In the second stage, users' active precoders are designed under different criteria such as minimizing the IL (min-IL), maximizing the signal-to-interference-plus-noise ratio (max-SINR), or maximizing the sum rate (max-SR). The residual interference not cancelled by the BD-RIS is treated as noise by the precoders. Our simulation results show that the max-SR precoders provide more than $20%$ sum rate improvement compared to other designs, especially when the BD-RIS has a moderate number of elements and users transmit with high power, in which case the residual interference is still significant.
{"title":"Interference Minimization in Beyond-Diagonal RIS-Assisted MIMO Interference Channels","authors":"Ignacio Santamaria;Mohammad Soleymani;Eduard Jorswieck;Jesús Gutiérrez","doi":"10.1109/OJVT.2025.3555425","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3555425","url":null,"abstract":"This paper proposes a two-stage approach for passive and active beamforming in multiple-input multiple-output (MIMO) interference channels (ICs) assisted by a beyond-diagonal reconfigurable intelligent surface (BD-RIS). In the first stage, the passive BD-RIS is designed to minimize the aggregate interference power at all receivers, a cost function called interference leakage (IL). To this end, we propose an optimization algorithm in the manifold of unitary matrices and a suboptimal but computationally efficient solution. In the second stage, users' active precoders are designed under different criteria such as minimizing the IL (min-IL), maximizing the signal-to-interference-plus-noise ratio (max-SINR), or maximizing the sum rate (max-SR). The residual interference not cancelled by the BD-RIS is treated as noise by the precoders. Our simulation results show that the max-SR precoders provide more than <inline-formula><tex-math>$20%$</tex-math></inline-formula> sum rate improvement compared to other designs, especially when the BD-RIS has a moderate number of elements and users transmit with high power, in which case the residual interference is still significant.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1005-1017"},"PeriodicalIF":5.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10943135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850876","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 : 2025-03-26DOI: 10.1109/OJVT.2025.3554365
Aamir Ullah Khan;Saw James Mint;Syed Najaf Haider Shah;Christian Schneider;Joerg Robert
Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.
{"title":"Exploring the Impact of Bistatic Target Reflectivity in ISAC-Enabled V2V Setup Across Diverse Geometrical Road Layouts","authors":"Aamir Ullah Khan;Saw James Mint;Syed Najaf Haider Shah;Christian Schneider;Joerg Robert","doi":"10.1109/OJVT.2025.3554365","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3554365","url":null,"abstract":"Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"948-968"},"PeriodicalIF":5.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830467","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 : 2025-03-21DOI: 10.1109/OJVT.2025.3553718
Tim Brophy;Darragh Mullins;Ashkan Parsi;Jonathan Horgan;Enda Ward;Patrick Denny;Ciarán Eising;Brian Deegan;Martin Glavin;Edward Jones
The reliable performance of object detection perception algorithms in automated vehicles under adverse conditions such as rain is critical for maintaining vulnerable road user safety. Visible-spectrum cameras provide a rich source of information and are cost-effective compared with other sensors; however, their performance can degrade under adverse environmental conditions. Despite the general consensus that the object detection performance in computer vision is adversely affected by rain, there is a relative lack of research investigating this relationship in detail. This study investigates the performance of object detection under rain conditions, focusing on algorithm performance and low-level object characteristics. Using the publicly available BDD100 k dataset, this study examines object detection performance across multiple deep-learning object detection architectures, analyzing error types and image characteristics under rain and no rain conditions. In addition, statistical methods were used to compare image-level metrics to determine statistical significance. The results reveal that rain is not detrimental to object detection performance, and in some cases, better performance is observed. For some models, medium-sized objects experience improved detection and classification under rain conditions, while large objects experience a slight decline in performance. The error analysis shows an increase in localization errors and a decrease in classification errors. The object-level analysis revealed statistically significant changes in the contrast-to-noise ratio, entropy, mean pixel value, pixel variance, hue, saturation, and value, with hue and saturation experiencing the most significant changes. This study highlights the need for more detailed weather labeling in datasets to fully understand the nuances of the relationship between rain and object detection.
{"title":"Analysis of the Impact of Rain on Perception in Automated Vehicle Applications","authors":"Tim Brophy;Darragh Mullins;Ashkan Parsi;Jonathan Horgan;Enda Ward;Patrick Denny;Ciarán Eising;Brian Deegan;Martin Glavin;Edward Jones","doi":"10.1109/OJVT.2025.3553718","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3553718","url":null,"abstract":"The reliable performance of object detection perception algorithms in automated vehicles under adverse conditions such as rain is critical for maintaining vulnerable road user safety. Visible-spectrum cameras provide a rich source of information and are cost-effective compared with other sensors; however, their performance can degrade under adverse environmental conditions. Despite the general consensus that the object detection performance in computer vision is adversely affected by rain, there is a relative lack of research investigating this relationship in detail. This study investigates the performance of object detection under rain conditions, focusing on algorithm performance and low-level object characteristics. Using the publicly available BDD100 k dataset, this study examines object detection performance across multiple deep-learning object detection architectures, analyzing error types and image characteristics under rain and no rain conditions. In addition, statistical methods were used to compare image-level metrics to determine statistical significance. The results reveal that rain is not detrimental to object detection performance, and in some cases, better performance is observed. For some models, medium-sized objects experience improved detection and classification under rain conditions, while large objects experience a slight decline in performance. The error analysis shows an increase in localization errors and a decrease in classification errors. The object-level analysis revealed statistically significant changes in the contrast-to-noise ratio, entropy, mean pixel value, pixel variance, hue, saturation, and value, with hue and saturation experiencing the most significant changes. This study highlights the need for more detailed weather labeling in datasets to fully understand the nuances of the relationship between rain and object detection.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1018-1032"},"PeriodicalIF":5.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856345","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 : 2025-03-14DOI: 10.1109/OJVT.2025.3551209
Abdullah Abu Zaid;Baha Eddine Youcef Belmekki;Mohamed-Slim Alouini
Urban air mobility (UAM) is increasingly capturing the attention of researchers and industry experts, as it holds the promise of providing faster and more economical solutions for urban commuting. Ensuring reliable communication for UAM aircraft is of paramount importance in maintaining operational safety. To that end, we use stochastic geometry tools to analyze the joint uplink-downlink coverage probability of an integrated aerial-terrestrial heterogeneous network (HetNet) for UAM aircraft, specifically electric vertical takeoff and landing (eVTOL) vehicles. We assume eVTOLs travel on predefined air corridors which are modeled as a Poisson line process (PLP). Furthermore, we model the spatial distribution of eVTOLs as a Matern hardcore process (MHCP) with a designated safety distance. We model the aerial base stations (ABSs) as a two-dimensional (2D) binomial point process (BPP), and the terrestrial base stations (TBSs) as a 2D Poisson point process (PPP). We use a suitable air-to-ground channel model to include line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. In the paper, we derive distance distributions to the closest ABS, LOS TBS, and NLOS TBS to a typical eVTOL, then we provide the association probability of each BS. Furthermore, we characterize the uplink interference and derive Laplace transforms for the PLP-MHCP distributed eVTOLs. Finally, we derive the coverage probability of the overall HetNet and carry out Monte Carlo simulations to validate our expressions.
{"title":"Aerial-Terrestrial Heterogeneous Networks for Urban Air Mobility: A Performance Analysis","authors":"Abdullah Abu Zaid;Baha Eddine Youcef Belmekki;Mohamed-Slim Alouini","doi":"10.1109/OJVT.2025.3551209","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3551209","url":null,"abstract":"Urban air mobility (UAM) is increasingly capturing the attention of researchers and industry experts, as it holds the promise of providing faster and more economical solutions for urban commuting. Ensuring reliable communication for UAM aircraft is of paramount importance in maintaining operational safety. To that end, we use stochastic geometry tools to analyze the joint uplink-downlink coverage probability of an integrated aerial-terrestrial heterogeneous network (HetNet) for UAM aircraft, specifically electric vertical takeoff and landing (eVTOL) vehicles. We assume eVTOLs travel on predefined air corridors which are modeled as a Poisson line process (PLP). Furthermore, we model the spatial distribution of eVTOLs as a Matern hardcore process (MHCP) with a designated safety distance. We model the aerial base stations (ABSs) as a two-dimensional (2D) binomial point process (BPP), and the terrestrial base stations (TBSs) as a 2D Poisson point process (PPP). We use a suitable air-to-ground channel model to include line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. In the paper, we derive distance distributions to the closest ABS, LOS TBS, and NLOS TBS to a typical eVTOL, then we provide the association probability of each BS. Furthermore, we characterize the uplink interference and derive Laplace transforms for the PLP-MHCP distributed eVTOLs. Finally, we derive the coverage probability of the overall HetNet and carry out Monte Carlo simulations to validate our expressions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"912-926"},"PeriodicalIF":5.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817904","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 : 2025-03-11DOI: 10.1109/OJVT.2025.3549387
Muhammad Farhan;Hassan Eesaar;Afaq Ahmed;Kil To Chong;Hilal Tayara
Highway accidents pose serious challenges and safety risks, often resulting in severe injuries and fatalities due to delayed detection and response. Traditional accident management methods heavily rely on manual reporting, which can be sometime inefficient and error-prone resulting in valuable life loss. This paper proposes a novel framework that integrates autonomous aerial systems (drones) with advanced deep learning models to enhance real-time accident detection and response capabilities. The system not only dispatch the drones but also provide live accident footage, accident identification and aids in coordinating emergency response. In this study we implemented our system in Gazebo simulation environment, where an autonomous drone navigates to specified location based on the navigation commands generated by Large Language Model (LLM) by processing the emergency call/transcript. Additionally, we created a dedicated accident dataset to train YOLOv11 m model for precise accident detection. At accident location the drone provides live video feeds and our YOLO model detects the incident, these high-resolution captured images after detection are analyzed by Moondream2, a Vision language model (VLM), for generating detailed textual descriptions of the scene, which are further refined by GPT 4-Turbo, large language model (LLM) for producing concise incident reports and actionable suggestions. This end-to-end system combines autonomous navigation, incident detection and incident response, thus showcasing its potential by providing scalable and efficient solutions for incident response management. The initial implementation demonstrates promising results and accuracy, validated through Gazebo simulation. Future work will focus on implementing this framework to the hardware implementation for real-world deployment in highway incident system.
{"title":"Transforming Highway Safety With Autonomous Drones and AI: A Framework for Incident Detection and Emergency Response","authors":"Muhammad Farhan;Hassan Eesaar;Afaq Ahmed;Kil To Chong;Hilal Tayara","doi":"10.1109/OJVT.2025.3549387","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3549387","url":null,"abstract":"Highway accidents pose serious challenges and safety risks, often resulting in severe injuries and fatalities due to delayed detection and response. Traditional accident management methods heavily rely on manual reporting, which can be sometime inefficient and error-prone resulting in valuable life loss. This paper proposes a novel framework that integrates autonomous aerial systems (drones) with advanced deep learning models to enhance real-time accident detection and response capabilities. The system not only dispatch the drones but also provide live accident footage, accident identification and aids in coordinating emergency response. In this study we implemented our system in Gazebo simulation environment, where an autonomous drone navigates to specified location based on the navigation commands generated by Large Language Model (LLM) by processing the emergency call/transcript. Additionally, we created a dedicated accident dataset to train YOLOv11 m model for precise accident detection. At accident location the drone provides live video feeds and our YOLO model detects the incident, these high-resolution captured images after detection are analyzed by Moondream2, a Vision language model (VLM), for generating detailed textual descriptions of the scene, which are further refined by GPT 4-Turbo, large language model (LLM) for producing concise incident reports and actionable suggestions. This end-to-end system combines autonomous navigation, incident detection and incident response, thus showcasing its potential by providing scalable and efficient solutions for incident response management. The initial implementation demonstrates promising results and accuracy, validated through Gazebo simulation. Future work will focus on implementing this framework to the hardware implementation for real-world deployment in highway incident system.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"829-845"},"PeriodicalIF":5.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777883","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}
TARA, which stands for Threat Analysis and Risk Assessment, serves as the foundational stage of cybersecurity implementation, particularly in the context of vehicular systems. While various considerations and risk assessment frameworks have been discussed in recent years, there is a notable lack of TARA models specifically designed for heavy-duty (HD) vehicles. The security considerations and vulnerabilities in HD vehicles differ significantly from those in light-duty (LD) vehicles, leading to different security impacts and varying attack feasibility. This makes existing models inadequate for accurately assessing risks in the context of HD vehicles. This study introduces a novel risk assessment model tailored for HD vehicles, addressing gaps in existing TARA frameworks such as EVITA, HEAVENS, and ISO/SAE 21434. The key contribution of this work lies in the customization of impact and feasibility metrics within the ISO/SAE framework to better account for the unique security challenges posed by HD vehicles. Unlike prior models, this approach adapts the impact criteria to reflect the diverse range of security concerns specific to HD vehicles, which have been inadequately addressed in existing frameworks. Additionally, through a comprehensive analysis of threat vectors and vehicle interfaces, the model refines feasibility criteria, ensuring a more accurate and context-aware assessment of security risks. By adopting these enhancements, the proposed model offers more precise risk assessments that align with HD vehicle considerations, helping to prioritize threats and make optimal decisions regarding risk treatment.
{"title":"Cyber Threat Susceptibility Assessment for Heavy-Duty Vehicles Based on ISO/SAE 21434","authors":"Narges Rahimi;Beth-Anne Schuelke-Leech;Mitra Mirhassani","doi":"10.1109/OJVT.2025.3550307","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3550307","url":null,"abstract":"TARA, which stands for Threat Analysis and Risk Assessment, serves as the foundational stage of cybersecurity implementation, particularly in the context of vehicular systems. While various considerations and risk assessment frameworks have been discussed in recent years, there is a notable lack of TARA models specifically designed for heavy-duty (HD) vehicles. The security considerations and vulnerabilities in HD vehicles differ significantly from those in light-duty (LD) vehicles, leading to different security impacts and varying attack feasibility. This makes existing models inadequate for accurately assessing risks in the context of HD vehicles. This study introduces a novel risk assessment model tailored for HD vehicles, addressing gaps in existing TARA frameworks such as EVITA, HEAVENS, and ISO/SAE 21434. The key contribution of this work lies in the customization of impact and feasibility metrics within the ISO/SAE framework to better account for the unique security challenges posed by HD vehicles. Unlike prior models, this approach adapts the impact criteria to reflect the diverse range of security concerns specific to HD vehicles, which have been inadequately addressed in existing frameworks. Additionally, through a comprehensive analysis of threat vectors and vehicle interfaces, the model refines feasibility criteria, ensuring a more accurate and context-aware assessment of security risks. By adopting these enhancements, the proposed model offers more precise risk assessments that align with HD vehicle considerations, helping to prioritize threats and make optimal decisions regarding risk treatment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"969-990"},"PeriodicalIF":5.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10921673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830529","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 : 2025-03-10DOI: 10.1109/OJVT.2025.3549918
Max Schurwanz;Jan Mietzner;Peter Adam Hoeher
This paper introduces a joint communication and sensing (JCAS) system design that employs a discrete Fourier transform (DFT)-spread orthogonal frequency-division multiplexing (OFDM) waveform integrated with a multiple-input multiple-output (MIMO) antenna array. This system has been designed with the specific requirements of future remotely piloted or autonomous aircraft systems in urban air mobility (UAM) settings in mind. The objective is to provide high-bandwidth data transmission in conjunction with precise radar sensing, thereby enhancing situational awareness and facilitating efficient spectrum usage. The paper makes a number of significant contributions to the field, including the development of a flexible MIMO DFT-spread OFDM system model and the introduction of a phase compensation term for comprehensive direction-of-arrival estimation. Additionally, the effects of non-linear power amplifiers on system efficacy are analyzed through detailed simulations, providing a rigorous evaluation of the proposed design's practicality and resilience. The numerical analysis establishes a framework for the design of a JCAS system for UAM, taking into account the influence of realistic electronic components and the respective performance requirements for communication and sensing.
{"title":"DFT-Spread OFDM-Based MIMO Joint Communication and Sensing System","authors":"Max Schurwanz;Jan Mietzner;Peter Adam Hoeher","doi":"10.1109/OJVT.2025.3549918","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3549918","url":null,"abstract":"This paper introduces a joint communication and sensing (JCAS) system design that employs a discrete Fourier transform (DFT)-spread orthogonal frequency-division multiplexing (OFDM) waveform integrated with a multiple-input multiple-output (MIMO) antenna array. This system has been designed with the specific requirements of future remotely piloted or autonomous aircraft systems in urban air mobility (UAM) settings in mind. The objective is to provide high-bandwidth data transmission in conjunction with precise radar sensing, thereby enhancing situational awareness and facilitating efficient spectrum usage. The paper makes a number of significant contributions to the field, including the development of a flexible MIMO DFT-spread OFDM system model and the introduction of a phase compensation term for comprehensive direction-of-arrival estimation. Additionally, the effects of non-linear power amplifiers on system efficacy are analyzed through detailed simulations, providing a rigorous evaluation of the proposed design's practicality and resilience. The numerical analysis establishes a framework for the design of a JCAS system for UAM, taking into account the influence of realistic electronic components and the respective performance requirements for communication and sensing.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"868-880"},"PeriodicalIF":5.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10919057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792856","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 : 2025-03-07DOI: 10.1109/OJVT.2025.3544148
Iman Valiulahi;Christos Masouros;Mahmoud Alaaeldin;Emad Alsusa
Integrated sensing and communication (ISAC) receiver design involves the challenge of jointly estimating the communication signal together with the direction of arrivals (DOAs) of the transmitters. This letter proposes an off-the-grid estimator for the ISAC receiver that jointly estimates the DOAs of $K$ transmitters together with the communication data. We focus on the challenging case of incomplete observation, i.e., where only a subset of the received signals in space and time are available. We propose a convex optimization based on the dual of atomic norm minimization (ANM). Though the problem is non-deterministic polynomial time (NP)-hard, we leverage the Schur complement technique to develop semidefinite relaxations (SDRs) to implement it. Moreover, we study a fast algorithm based on the alternating direction method of multipliers (ADMM) technique. Finally, our numerical results explore the feasibility of the joint estimation with incomplete observations, while outperforming classical DOA estimators.
{"title":"ISAC Receiver Design: Joint DoA and Data Estimation in the Presence of Incomplete Signal Observations","authors":"Iman Valiulahi;Christos Masouros;Mahmoud Alaaeldin;Emad Alsusa","doi":"10.1109/OJVT.2025.3544148","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3544148","url":null,"abstract":"Integrated sensing and communication (ISAC) receiver design involves the challenge of jointly estimating the communication signal together with the direction of arrivals (DOAs) of the transmitters. This letter proposes an off-the-grid estimator for the ISAC receiver that jointly estimates the DOAs of <inline-formula><tex-math>$K$</tex-math></inline-formula> transmitters together with the communication data. We focus on the challenging case of incomplete observation, i.e., where only a subset of the received signals in space and time are available. We propose a convex optimization based on the dual of atomic norm minimization (ANM). Though the problem is non-deterministic polynomial time (NP)-hard, we leverage the Schur complement technique to develop semidefinite relaxations (SDRs) to implement it. Moreover, we study a fast algorithm based on the alternating direction method of multipliers (ADMM) technique. Finally, our numerical results explore the feasibility of the joint estimation with incomplete observations, while outperforming classical DOA estimators.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"846-852"},"PeriodicalIF":5.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817870","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}
Modern vehicles are increasingly vulnerable to cyber-attacks due to the lack of encryption and authentication in the Controller Area Network, which coordinates communication between Electronic Control Units. This study investigates the use of Recurrent Neural Networks to improve the accuracy and efficiency of Intrusion Detection Systems in vehicular networks. Focusing on sequential CAN data, we compare the performance of different RNN architectures, including SimpleRNN, LSTM, and GRU, in detecting common attack types like Denial-of-Service, Fuzzing, Replay, and Malfunction. Sixty-three RNN models were tested with various hyperparameters, including optimizers and learning rates. Our findings indicate that GRU models achieve superior detection performance, particularly in resource-constrained environments, offering near 99% accuracy in identifying cyber threats. The study also explores the implications of six different hardware choices, revealing that devices like Jetson and Raspberry Pi, when paired with optimal hyperparameters, can deliver efficient real-time IDS performance at a lower cost. These results contribute to the ongoing effort to secure vehicular communication systems and highlight the importance of balancing accuracy, resource usage, and system cost in IDS deployment.
{"title":"Adaptive RNN Hyperparameter Tuning for Optimized IDS Across Platforms","authors":"Kamronbek Yusupov;Md Rezanur Islam;Ibrokhim Muminov;Mahdi Sahlabadi;Kangbin Yim","doi":"10.1109/OJVT.2025.3547761","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547761","url":null,"abstract":"Modern vehicles are increasingly vulnerable to cyber-attacks due to the lack of encryption and authentication in the Controller Area Network, which coordinates communication between Electronic Control Units. This study investigates the use of Recurrent Neural Networks to improve the accuracy and efficiency of Intrusion Detection Systems in vehicular networks. Focusing on sequential CAN data, we compare the performance of different RNN architectures, including SimpleRNN, LSTM, and GRU, in detecting common attack types like Denial-of-Service, Fuzzing, Replay, and Malfunction. Sixty-three RNN models were tested with various hyperparameters, including optimizers and learning rates. Our findings indicate that GRU models achieve superior detection performance, particularly in resource-constrained environments, offering near 99% accuracy in identifying cyber threats. The study also explores the implications of six different hardware choices, revealing that devices like Jetson and Raspberry Pi, when paired with optimal hyperparameters, can deliver efficient real-time IDS performance at a lower cost. These results contribute to the ongoing effort to secure vehicular communication systems and highlight the importance of balancing accuracy, resource usage, and system cost in IDS deployment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"991-1004"},"PeriodicalIF":5.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845520","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 use of a reconfigurable intelligent surface (RIS) in radar systems significantly enhances target detection, particularly in challenging non-line-of-sight (NLoS) scenarios. In urban environments, where structures frequently obstruct line-of-sight (LoS) paths, the integration of RISs with existing radars can offer a viable solution for enhancing signal-to-noise ratio (SNR) and improving target detection. Approaches utilizing a single RIS can still fail in scenarios where a link cannot be established. This paper presents a novel approach for deriving a comprehensive expression for the received power, SNR and path loss (PL) in systems where multiple RISs assist a monostatic radar. We analyze the power received in dual RIS configurations and extend this to include additional RISs, demonstrating how each additional RIS placement affects the system's performance. Moreover, the analysis explores the impact of different Swerling target models on the SNR and PL, highlighting the optimal angles for target detection. This multi-RIS strategy offers a substantial performance boost over conventional radars and single RIS-assisted systems, particularly in environments with obstacles. Simulation results demonstrate a significant improvement in SNR with a dual RIS-assisted radar, with up to 14.42 dB gains observed when employing a $46 times 46$ element RIS configuration at L-band and 65.47 dB gain when employing a $328 times 328$ element RIS configuration at X-band, corresponding to a RIS size of $ 5text{ m} times 5text{ m}$ at both frequencies, showing the efficacy of the proposed multi-RIS strategy.
{"title":"Improving SNR for NLoS Target Detection Using Multi-RIS-Assisted Monostatic Radar","authors":"Salman Liaquat;Ijaz Haider Naqvi;Faran Awais Butt;Saleh Alawsh;Nor Muzlifah Mahyuddin;Ali Hussein Muqaibel","doi":"10.1109/OJVT.2025.3547163","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547163","url":null,"abstract":"The use of a reconfigurable intelligent surface (RIS) in radar systems significantly enhances target detection, particularly in challenging non-line-of-sight (NLoS) scenarios. In urban environments, where structures frequently obstruct line-of-sight (LoS) paths, the integration of RISs with existing radars can offer a viable solution for enhancing signal-to-noise ratio (SNR) and improving target detection. Approaches utilizing a single RIS can still fail in scenarios where a link cannot be established. This paper presents a novel approach for deriving a comprehensive expression for the received power, SNR and path loss (PL) in systems where multiple RISs assist a monostatic radar. We analyze the power received in dual RIS configurations and extend this to include additional RISs, demonstrating how each additional RIS placement affects the system's performance. Moreover, the analysis explores the impact of different Swerling target models on the SNR and PL, highlighting the optimal angles for target detection. This multi-RIS strategy offers a substantial performance boost over conventional radars and single RIS-assisted systems, particularly in environments with obstacles. Simulation results demonstrate a significant improvement in SNR with a dual RIS-assisted radar, with up to 14.42 dB gains observed when employing a <inline-formula><tex-math>$46 times 46$</tex-math></inline-formula> element RIS configuration at L-band and 65.47 dB gain when employing a <inline-formula><tex-math>$328 times 328$</tex-math></inline-formula> element RIS configuration at X-band, corresponding to a RIS size of <inline-formula><tex-math>$ 5text{ m} times 5text{ m}$</tex-math></inline-formula> at both frequencies, showing the efficacy of the proposed multi-RIS strategy.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"774-789"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698350","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}
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