Pub Date : 2026-03-03DOI: 10.1109/OJVT.2026.3668803
Kamran Shafafi;Manuel Ricardo;Rui Campos
Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations to support vehicular users and Vehicle-to-Everything (V2X) applications. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. Existing approaches rely on probabilistic blockage models or require dedicated infrastructure such as Reconfigurable Intelligent Surfaces. This paper proposes VTOPA, a Vision-Aided Traffic- and Obstacle-Aware Positioning Algorithm that complements these approaches by autonomously extracting environmental information—such as obstacle geometries and UE locations—via computer vision, enabling infrastructure-free deployment. The algorithm employs Particle Swarm Optimization to determine UAV positions that maximize aggregate throughput while prioritizing LoS connectivity and accounting for heterogeneous traffic demands. VTOPA is particularly suited for rapid deployment scenarios such as emergency response and temporary events. Evaluated through simulations in ns-3, VTOPA achieves up to 50% increase in aggregate throughput and 50% reduction in delay, outperforming state of the art benchmarks in obstacle-rich environments.
{"title":"Autonomous Vision-Aided UAV Positioning for Obstacle-Aware Wireless Connectivity","authors":"Kamran Shafafi;Manuel Ricardo;Rui Campos","doi":"10.1109/OJVT.2026.3668803","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3668803","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations to support vehicular users and Vehicle-to-Everything (V2X) applications. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. Existing approaches rely on probabilistic blockage models or require dedicated infrastructure such as Reconfigurable Intelligent Surfaces. This paper proposes VTOPA, a Vision-Aided Traffic- and Obstacle-Aware Positioning Algorithm that complements these approaches by autonomously extracting environmental information—such as obstacle geometries and UE locations—via computer vision, enabling infrastructure-free deployment. The algorithm employs Particle Swarm Optimization to determine UAV positions that maximize aggregate throughput while prioritizing LoS connectivity and accounting for heterogeneous traffic demands. VTOPA is particularly suited for rapid deployment scenarios such as emergency response and temporary events. Evaluated through simulations in ns-3, VTOPA achieves up to 50% increase in aggregate throughput and 50% reduction in delay, outperforming state of the art benchmarks in obstacle-rich environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"857-870"},"PeriodicalIF":4.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11419793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440648","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 : 2026-02-19DOI: 10.1109/OJVT.2026.3666359
Xiang Shi;I. Safak Bayram;Stuart Galloway
The rapid electrification of the transportation sector offers a promising avenue for ancillary services through Vehicle-to-Grid (V2G) applications. This is particularly critical for low-inertia systems, such as the U.K. grid, where the transition toward converter-based renewable generation necessitates very fast frequency response. Therefore, the viability of V2G for high-value frequency markets is constrained by strict latency requirements (e.g. one second)). Existing literature has predominantly focused on high-level economic aggregation models or communication network delays, largely neglecting the stochastic physical response dynamics of the EV On-Board Charger (OBC). This paper addresses this gap by developing a discrete-time Markov chain model that specifically characterizes the internal dynamics and response latency of OBC hardware. We integrate this model into a discrete-event simulation framework to evaluate end-to-end system latency, coupling stochastic OBC constraints with Over-the-Air (OTA) communication delays. We analyze the performance of fleets comprised of three common OBC ratings: 10 kW, 22 kW, and 43 kW. Contrary to the intuition that higher power ratings yield superior agility, our results demonstrate that high-capacity chargers may exhibit lower success rates in fast frequency markets due to insufficient ramp-rate-to-capacity ratios. Furthermore, we demonstrate that the frequency of mode switching events (switching between charging and discharging) is a dominant factor in performance degradation due to hardware hysteresis. These findings underscore that the efficacy of V2G applications requires precise EV-level control logic rather than relying solely on fleet-level optimization. Finally, the proposed models are evaluated against the PJM interconnection’s composite score methodology. The results demonstrate high accuracy, suggesting the proposed framework can serve as a preliminary, EV-specific V2G assessment tool for market operators.
{"title":"Stochastic Modeling of EV On-Board Chargers for Fast Frequency Response Under Communication Delays","authors":"Xiang Shi;I. Safak Bayram;Stuart Galloway","doi":"10.1109/OJVT.2026.3666359","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3666359","url":null,"abstract":"The rapid electrification of the transportation sector offers a promising avenue for ancillary services through Vehicle-to-Grid (V2G) applications. This is particularly critical for low-inertia systems, such as the U.K. grid, where the transition toward converter-based renewable generation necessitates very fast frequency response. Therefore, the viability of V2G for high-value frequency markets is constrained by strict latency requirements (e.g. one second)). Existing literature has predominantly focused on high-level economic aggregation models or communication network delays, largely neglecting the stochastic physical response dynamics of the EV On-Board Charger (OBC). This paper addresses this gap by developing a discrete-time Markov chain model that specifically characterizes the internal dynamics and response latency of OBC hardware. We integrate this model into a discrete-event simulation framework to evaluate end-to-end system latency, coupling stochastic OBC constraints with Over-the-Air (OTA) communication delays. We analyze the performance of fleets comprised of three common OBC ratings: 10 kW, 22 kW, and 43 kW. Contrary to the intuition that higher power ratings yield superior agility, our results demonstrate that high-capacity chargers may exhibit lower success rates in fast frequency markets due to insufficient ramp-rate-to-capacity ratios. Furthermore, we demonstrate that the frequency of mode switching events (switching between charging and discharging) is a dominant factor in performance degradation due to hardware hysteresis. These findings underscore that the efficacy of V2G applications requires precise EV-level control logic rather than relying solely on fleet-level optimization. Finally, the proposed models are evaluated against the PJM interconnection’s composite score methodology. The results demonstrate high accuracy, suggesting the proposed framework can serve as a preliminary, EV-specific V2G assessment tool for market operators.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"817-828"},"PeriodicalIF":4.8,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11399913","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362541","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 : 2026-02-17DOI: 10.1109/OJVT.2026.3665967
Ákos M. Bokor;Szilárd Aradi;Tamás Bécsi;László Palkovics;Ádám Szabó
This paper proposes hybrid Neural Network-based Model Predictive Control (NN-MPC) architectures for real-time autonomous vehicle path tracking. To mitigate the computational burden of Model Predictive Control (MPC) in real-time operation and the absence of stability and safety guarantees in purely supervised approaches, we integrate supervised learning directly into the optimization process to accelerate solver convergence. Specifically, Multilayer perceptrons are trained to learn a constraint-correction term added to the closed-form unconstrained MPC solution, and to provide learning-assisted warm starts for an active-set QP solver by predicting the Lagrange multiplier vector and the active-constraint pattern. The first solution avoids online quadratic programming (QP) and achieves an approximately two orders of magnitude reduction in computation time, at the cost of approximate constraint enforcement in highly transient conditions, whereas the latter two solutions retain the original constrained optimization problem. The learning components are trained on a simplified design model and evaluated in the high-fidelity CarMaker environment to assess robustness under unmodeled dynamics and modeling errors. In CarMaker validation, the classification-based warm start reduces the number of QP iterations by approximately 30% and computation time by 23% relative to a classical shift-initialization warm start, supporting more predictable real-time operation in representative driving scenarios.
{"title":"Learning to Predict Constraints: Hybrid Neural-MPC Control Architectures for Real-Time Vehicle Path Tracking","authors":"Ákos M. Bokor;Szilárd Aradi;Tamás Bécsi;László Palkovics;Ádám Szabó","doi":"10.1109/OJVT.2026.3665967","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3665967","url":null,"abstract":"This paper proposes hybrid Neural Network-based Model Predictive Control (NN-MPC) architectures for real-time autonomous vehicle path tracking. To mitigate the computational burden of Model Predictive Control (MPC) in real-time operation and the absence of stability and safety guarantees in purely supervised approaches, we integrate supervised learning directly into the optimization process to accelerate solver convergence. Specifically, Multilayer perceptrons are trained to learn a constraint-correction term added to the closed-form unconstrained MPC solution, and to provide learning-assisted warm starts for an active-set QP solver by predicting the Lagrange multiplier vector and the active-constraint pattern. The first solution avoids online quadratic programming (QP) and achieves an approximately two orders of magnitude reduction in computation time, at the cost of approximate constraint enforcement in highly transient conditions, whereas the latter two solutions retain the original constrained optimization problem. The learning components are trained on a simplified design model and evaluated in the high-fidelity CarMaker environment to assess robustness under unmodeled dynamics and modeling errors. In CarMaker validation, the classification-based warm start reduces the number of QP iterations by approximately 30% and computation time by 23% relative to a classical shift-initialization warm start, supporting more predictable real-time operation in representative driving scenarios.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"844-856"},"PeriodicalIF":4.8,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11397851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440639","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 : 2026-02-09DOI: 10.1109/OJVT.2026.3662508
Elmahedi Mahalal;Eslam Hasan;Muhammad Ismail;Zi-Yang Wu;Mostafa M. Fouda;Zubair Md Fadlullah;Nei Kato
This paper investigates the vulnerability of wireless secret key generation (WSKG) to eavesdropping by analyzing the channel impulse response (CIR) between a legitimate user and an eavesdropper under various scenarios in dynamic indoor light fidelity (LiFi) networks. These scenarios include (a) different user densities (2, 4, and 8 users), (b) fields of view (FoVs) of $30^circ$, $60^circ$, and $90^circ$, and (c) various room layouts. Results show that higher user densities increase downlink CIR similarity, as users’ movement traces become closer. For instance, with eight users and a $30^circ$ FoV, CIR similarity peaks at over 85% –95% during entering and exiting stages and remains no less than 40% during wandering. Consequently, an eavesdropper can generate a key with 27% –37% similarity to the legitimate user’s key. To mitigate this threat, we propose a novel defense using a generative adversarial network (GAN) trained with crafted uplink CIRs. GANs model the complex statistical properties of legitimate CIRs and generate synthetic noise that mimics environmental and system characteristics without replicating real user CIRs. This prevents eavesdroppers from extracting useful information and avoids privacy concerns linked to handling actual CIR data. Furthermore, traditional reversed CIR methods are less effective in dynamic environments, where conditions change rapidly and are easier to reverse-engineer. Our GAN-generated noise, applied within the defense zone of the legitimate user, reduces CIR similarities from up to 95% to approximately 1%, effectively nullifying key leakage. These findings highlight the potential of GAN-based noise to significantly enhance WSKG security in dynamic indoor LiFi networks.
{"title":"GAN-Based Artificial Noise Generation Against Eavesdropping for Wireless Secret Key Generation in Dynamic Indoor LiFi Networks","authors":"Elmahedi Mahalal;Eslam Hasan;Muhammad Ismail;Zi-Yang Wu;Mostafa M. Fouda;Zubair Md Fadlullah;Nei Kato","doi":"10.1109/OJVT.2026.3662508","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3662508","url":null,"abstract":"This paper investigates the vulnerability of wireless secret key generation (WSKG) to eavesdropping by analyzing the channel impulse response (CIR) between a legitimate user and an eavesdropper under various scenarios in dynamic indoor light fidelity (LiFi) networks. These scenarios include (a) different user densities (2, 4, and 8 users), (b) fields of view (FoVs) of <inline-formula><tex-math>$30^circ$</tex-math></inline-formula>, <inline-formula><tex-math>$60^circ$</tex-math></inline-formula>, and <inline-formula><tex-math>$90^circ$</tex-math></inline-formula>, and (c) various room layouts. Results show that higher user densities increase downlink CIR similarity, as users’ movement traces become closer. For instance, with eight users and a <inline-formula><tex-math>$30^circ$</tex-math></inline-formula> FoV, CIR similarity peaks at over 85% –95% during entering and exiting stages and remains no less than 40% during wandering. Consequently, an eavesdropper can generate a key with 27% –37% similarity to the legitimate user’s key. To mitigate this threat, we propose a novel defense using a generative adversarial network (GAN) trained with crafted uplink CIRs. GANs model the complex statistical properties of legitimate CIRs and generate synthetic noise that mimics environmental and system characteristics without replicating real user CIRs. This prevents eavesdroppers from extracting useful information and avoids privacy concerns linked to handling actual CIR data. Furthermore, traditional reversed CIR methods are less effective in dynamic environments, where conditions change rapidly and are easier to reverse-engineer. Our GAN-generated noise, applied within the defense zone of the legitimate user, reduces CIR similarities from up to 95% to approximately 1%, effectively nullifying key leakage. These findings highlight the potential of GAN-based noise to significantly enhance WSKG security in dynamic indoor LiFi networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"871-886"},"PeriodicalIF":4.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440649","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 this paper, we study an uncrewed aerial vehicle (UAV)-assisted 3D uplink (UL)-heavy non-orthogonal multiple access (NOMA) system for low-altitude economy, in which UL communication is growing more crucial in emergency hotspots like football stadiums. To the best of our knowledge, this is the first effort to investigate joint UL resource allocation alongside stereoscopic trajectory design for UAV-assisted 3D UL-heavy NOMA, in light of users' instantaneous rate-sensitive and elevated average rate-oriented traffic requirements. Specifically, we put forward a joint bandwidth and power allocation (J-BPA) algorithm by demonstrating that the inter-user interference within each NOMA group can be inherently eliminated while deriving users' UL sum-rate. Given the non-differentiable nature of the Lagrange dual function, the constrained ellipsoid method is employed to obtain the optimal solution. Furthermore, to further reduce computational complexity and boost the degrees of freedom in resource allocation, an enhanced J-BPA scheme is proposed, with closed-form optimal expressions derived for both intra-group and inter-group power allocation among NOMA groups. Both the proposed J-BPA and enhanced J-BPA are compatible with stereoscopic trajectory optimization, which are alternatively solved to achieve rapid convergence, and demonstrate superior performance over existing methods in terms of minimum average UL rate and user fairness.
{"title":"Toward UAV-Assisted 3D UL-Heavy NOMA for Low-Altitude Economy: Joint Bandwidth, Power Allocation and Stereoscopic Trajectory Design","authors":"Haiyong Zeng;Xiaocong Li;Shoulin Huang;Xingxing Ju;Zhongxiang Wei;Tingting Zhang;Hongbin Chen;Xu Zhu","doi":"10.1109/OJVT.2026.3661631","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3661631","url":null,"abstract":"In this paper, we study an uncrewed aerial vehicle (UAV)-assisted 3D uplink (UL)-heavy non-orthogonal multiple access (NOMA) system for low-altitude economy, in which UL communication is growing more crucial in emergency hotspots like football stadiums. To the best of our knowledge, this is the first effort to investigate joint UL resource allocation alongside stereoscopic trajectory design for UAV-assisted 3D UL-heavy NOMA, in light of users' instantaneous rate-sensitive and elevated average rate-oriented traffic requirements. Specifically, we put forward a joint bandwidth and power allocation (J-BPA) algorithm by demonstrating that the inter-user interference within each NOMA group can be inherently eliminated while deriving users' UL sum-rate. Given the non-differentiable nature of the Lagrange dual function, the constrained ellipsoid method is employed to obtain the optimal solution. Furthermore, to further reduce computational complexity and boost the degrees of freedom in resource allocation, an enhanced J-BPA scheme is proposed, with closed-form optimal expressions derived for both intra-group and inter-group power allocation among NOMA groups. Both the proposed J-BPA and enhanced J-BPA are compatible with stereoscopic trajectory optimization, which are alternatively solved to achieve rapid convergence, and demonstrate superior performance over existing methods in terms of minimum average UL rate and user fairness.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"737-750"},"PeriodicalIF":4.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223618","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 : 2026-02-04DOI: 10.1109/OJVT.2026.3661153
Goli Srikanth;Shaik Basheeruddin Shah;Nazar T. Ali;Vijay Kumar Chakka;Jorge Querol;Ahmed Altunaiji;Dragan I. Olćan
This article proposes a novel Multiple Access (MA) scheme called Ramanujan Periodic-subspace Division Multiple Access (RPDMA) for subcarrier sizes $N = 2^{m}, min mathbb {N}$, to address the high Peak-to-Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiple Access (OFDMA). Building on the properties of Ramanujan subspaces, we design transmitter and receiver models that allocate users on a subspace-wise basis, ensuring zero inter-user interference, providing inherent frequency diversity. We analyze the computational complexity of OFDMA, SC-FDMA, and RPDMA, and find that RPDMA has substantially lower per-user transmitter complexity than both OFDMA and SC-FDMA, while its receiver complexity is comparable to SC-FDMA and higher than that of OFDMA. We further introduce a generalized framework, termed Nested Periodic-subspace Division Multiple Access (NPDMA), which unifies both RPDMA and OFDMA under a common family of multi-carrier MA schemes. We derive the theoretical PAPR of RPDMA and demonstrate its superiority over OFDMA. The analysis is validated through numerical simulations under two multi-user scenarios with diverse Quality of Service (QoS) requirements. The results demonstrate that RPDMA achieves lower PAPR than both OFDMA and SC-FDMA, with users assigned larger subspaces benefiting from even greater PAPR reduction. We prove that both the sum and per-user Spectral Efficiency (SE) of RPDMA are identical to those of OFDMA and SC-FDMA. In terms of Bit Error Rate (BER), SC-FDMA achieves the best performance, while RPDMA still outperforms OFDMA as the SNR increases.
针对正交频分多址（OFDMA）中峰值平均功率比（PAPR）较高的问题，提出了一种新的子载波尺寸为$N = 2^{m}, min mathbb {N}$的Ramanujan周期子空间分割多址（RPDMA）方案。基于拉马努金子空间的特性，我们设计了以子空间为基础分配用户的发射和接收模型，确保零用户间干扰，提供固有的频率分集。我们分析了OFDMA、SC-FDMA和RPDMA的计算复杂度，发现RPDMA的每用户发送器复杂度明显低于OFDMA和SC-FDMA，而其接收器复杂度与SC-FDMA相当，高于OFDMA。我们进一步介绍了一个广义框架，称为嵌套周期子空间分割多址（NPDMA），它将RPDMA和OFDMA统一在一个通用的多载波多址方案族下。推导了RPDMA的理论PAPR，并论证了其相对于OFDMA的优越性。通过两种具有不同服务质量（QoS）需求的多用户场景的数值模拟验证了分析结果。结果表明，RPDMA比OFDMA和SC-FDMA实现更低的PAPR，分配更大的子空间的用户受益于更大的PAPR降低。我们证明了RPDMA的和和每用户频谱效率（SE）与OFDMA和SC-FDMA相同。在误码率方面，SC-FDMA达到了最好的性能，而RPDMA随着信噪比的增加仍然优于OFDMA。
{"title":"RPDMA: A PAPR-Aware Multiple Access Scheme","authors":"Goli Srikanth;Shaik Basheeruddin Shah;Nazar T. Ali;Vijay Kumar Chakka;Jorge Querol;Ahmed Altunaiji;Dragan I. Olćan","doi":"10.1109/OJVT.2026.3661153","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3661153","url":null,"abstract":"This article proposes a novel Multiple Access (MA) scheme called <bold>Ramanujan Periodic-subspace Division Multiple Access (RPDMA)</b> for subcarrier sizes <inline-formula><tex-math>$N = 2^{m}, min mathbb {N}$</tex-math></inline-formula>, to address the high Peak-to-Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiple Access (OFDMA). Building on the properties of Ramanujan subspaces, we design transmitter and receiver models that allocate users on a subspace-wise basis, ensuring zero inter-user interference, providing inherent frequency diversity. We analyze the computational complexity of OFDMA, SC-FDMA, and RPDMA, and find that RPDMA has substantially lower per-user transmitter complexity than both OFDMA and SC-FDMA, while its receiver complexity is comparable to SC-FDMA and higher than that of OFDMA. We further introduce a generalized framework, termed <bold>Nested Periodic-subspace Division Multiple Access (NPDMA)</b>, which unifies both RPDMA and OFDMA under a common family of multi-carrier MA schemes. We derive the theoretical PAPR of RPDMA and demonstrate its superiority over OFDMA. The analysis is validated through numerical simulations under two multi-user scenarios with diverse Quality of Service (QoS) requirements. The results demonstrate that RPDMA achieves lower PAPR than both OFDMA and SC-FDMA, with users assigned larger subspaces benefiting from even greater PAPR reduction. We prove that both the sum and per-user Spectral Efficiency (SE) of RPDMA are identical to those of OFDMA and SC-FDMA. In terms of Bit Error Rate (BER), SC-FDMA achieves the best performance, while RPDMA still outperforms OFDMA as the SNR increases.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"800-816"},"PeriodicalIF":4.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362519","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}
Enhancing fuel efficiency in hybrid electric vehicles (HEVs) requires energy management strategies (EMSs) that can operate effectively under nonlinear powertrain dynamics and uncertain, time-varying driving conditions. This paper proposes a deep reinforcement learning (DRL)- based EMS using the double actors regularized critics softmax deep deterministic policy gradient (DARC SD3) algorithm, which integrates Boltzmann-softmax value estimation, a dual-actor architecture, and critic regularization to improve learning stability and value-estimation accuracy. Simulation results show that the proposed DARC SD3 achieves faster convergence, improved state-of-charge (SOC) regulation, and reduced value estimation bias compared with DDPG, TD3, and baseline SD3. Under the FTP-75 driving cycle, the proposed EMS attains 94.6% of the dynamic programming (DP) benchmark fuel economy, while reducing engine transients and smoothing battery power flow. Further evaluation on an unseen composite driving cycle confirms that the trained policy maintains consistent fuel economy and SOC control, demonstrating strong generalization capability across diverse driving conditions.
{"title":"Deep Reinforcement Learning for Energy Management in Hybrid Electric Vehicles With Softmax Double-Actor Regularized Critics","authors":"Jewaliddin Shaik;Sri Phani Krishna Karri;Anugula Rajamallaiah;Kishore Bingi;Ramani Kannan;Vikas Singh Panwar","doi":"10.1109/OJVT.2026.3660677","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3660677","url":null,"abstract":"Enhancing fuel efficiency in hybrid electric vehicles (HEVs) requires energy management strategies (EMSs) that can operate effectively under nonlinear powertrain dynamics and uncertain, time-varying driving conditions. This paper proposes a deep reinforcement learning (DRL)- based EMS using the double actors regularized critics softmax deep deterministic policy gradient (DARC SD3) algorithm, which integrates Boltzmann-softmax value estimation, a dual-actor architecture, and critic regularization to improve learning stability and value-estimation accuracy. Simulation results show that the proposed DARC SD3 achieves faster convergence, improved state-of-charge (SOC) regulation, and reduced value estimation bias compared with DDPG, TD3, and baseline SD3. Under the FTP-75 driving cycle, the proposed EMS attains 94.6% of the dynamic programming (DP) benchmark fuel economy, while reducing engine transients and smoothing battery power flow. Further evaluation on an unseen composite driving cycle confirms that the trained policy maintains consistent fuel economy and SOC control, demonstrating strong generalization capability across diverse driving conditions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"723-736"},"PeriodicalIF":4.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223765","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 : 2026-02-03DOI: 10.1109/OJVT.2026.3660437
Naheel Faisal Kamal;Sertac Bayhan;Haitham Abu-Rub
The electric vehicle (EV) charging communication system typically relies on common security measures to protect against cyber attacks. However, little attention has been given to the privacy of the communicated data of the chargers. This paper presents a new technique for profiling EVs using an arbitrary time window of measured data from EV chargers, allowing an attacker to identify an EV with a minimal amount of information. The attack surface is first explored, showing how a profiling attack can be performed under different threat models. This assessment is considered across all the components of the EV charging infrastructure communication system. A deep neural network-based architecture is then constructed out of multiple smaller models for best possible prediction. These models are then trained using datasets of real EV charging sessions. Results of randomized test cases are then used to evaluate the trained models showing a relatively high prediction accuracy. This study signifies the privacy threat in the existing charging infrastructure and proposes general recommendations to protect the drivers' privacy.
{"title":"Profiling on EV Chargers: Attack Surface Assessment and a Deep Learning-Based Approach","authors":"Naheel Faisal Kamal;Sertac Bayhan;Haitham Abu-Rub","doi":"10.1109/OJVT.2026.3660437","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3660437","url":null,"abstract":"The electric vehicle (EV) charging communication system typically relies on common security measures to protect against cyber attacks. However, little attention has been given to the privacy of the communicated data of the chargers. This paper presents a new technique for profiling EVs using an arbitrary time window of measured data from EV chargers, allowing an attacker to identify an EV with a minimal amount of information. The attack surface is first explored, showing how a profiling attack can be performed under different threat models. This assessment is considered across all the components of the EV charging infrastructure communication system. A deep neural network-based architecture is then constructed out of multiple smaller models for best possible prediction. These models are then trained using datasets of real EV charging sessions. Results of randomized test cases are then used to evaluate the trained models showing a relatively high prediction accuracy. This study signifies the privacy threat in the existing charging infrastructure and proposes general recommendations to protect the drivers' privacy.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"626-638"},"PeriodicalIF":4.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175658","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 rapid growth of Intelligent Transportation Systems (ITS) and autonomous vehicles highlights the need to enhance V2V connectivity. Recently, millimeter waves (mmWave) are used extensively to offer high data rates; however, they suffer from blockage sensitivity and high path loss, especially in dense urban areas. Visible Light Communication (VLC) emerges as a promising alternative due to its wide bandwidth, immunity to electromagnetic interference, and inherent security. Nevertheless, its dependency on Line-of-Sight (LOS) restricts performance at road intersections. To address this challenge, this article proposes a Reconfigurable Intelligent Surface (RIS)-assisted V2V VLC system that enhances link reliability in LOS-blocked intersections. We formulate the optimization problem to jointly tune the RIS elements’ orientation to maximize total system capacity (Max-Sum) while ensuring fairness among communicating vehicles (Max-Min). To reduce computational complexity, we introduce a low-complexity heuristic approach based on analytical derivation of optimal RIS angles for a simplified one-to-one V2V model, which is then generalized for multiple vehicles. Simulation results demonstrate that the proposed heuristic achieves near-optimal performance with significantly reduced computation time, proving its potential for practical ITS deployments.
{"title":"Low-Complexity Heuristic Optimization of RIS Orientation for V2V Visible Light Communication at Urban Intersections","authors":"Mona Hosny;Mai Kafafy;Ashraf Eltholth;Hossam Selmy;Mohamed Khairy","doi":"10.1109/OJVT.2026.3660708","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3660708","url":null,"abstract":"The rapid growth of Intelligent Transportation Systems (ITS) and autonomous vehicles highlights the need to enhance V2V connectivity. Recently, millimeter waves (mmWave) are used extensively to offer high data rates; however, they suffer from blockage sensitivity and high path loss, especially in dense urban areas. Visible Light Communication (VLC) emerges as a promising alternative due to its wide bandwidth, immunity to electromagnetic interference, and inherent security. Nevertheless, its dependency on Line-of-Sight (LOS) restricts performance at road intersections. To address this challenge, this article proposes a Reconfigurable Intelligent Surface (RIS)-assisted V2V VLC system that enhances link reliability in LOS-blocked intersections. We formulate the optimization problem to jointly tune the RIS elements’ orientation to maximize total system capacity (Max-Sum) while ensuring fairness among communicating vehicles (Max-Min). To reduce computational complexity, we introduce a low-complexity heuristic approach based on analytical derivation of optimal RIS angles for a simplified one-to-one V2V model, which is then generalized for multiple vehicles. Simulation results demonstrate that the proposed heuristic achieves near-optimal performance with significantly reduced computation time, proving its potential for practical ITS deployments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"781-799"},"PeriodicalIF":4.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362289","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 : 2026-01-29DOI: 10.1109/OJVT.2026.3659052
Manu Jo Varghese;Frank Jiang;Abdur Rakib;Robin Doss;Adnan Anwar
The increasing complexity and connectivity of modern vehicles have made automotive networks, particularly the Controller Area Network (CAN) bus, vulnerable to cyberattacks. Fuzzing is a critical technique for proactively finding security weaknesses, but traditional methods are inefficient and struggle to scale with the complexity of modern vehicles. This paper introduces AMCFF-RL, an adaptive framework that uses Deep Reinforcement Learning (DRL) with multi-modal feature extraction to systematically analyse for vulnerabilities. Rather than relying on unguided or purely random fuzzing, AMCFF-RL integrates multi-modal feature extraction with DRL and advanced visualization, allowing it to learn and adapt its strategy based on real-time feedback from the network and thereby improve the efficiency and effectiveness of the fuzzing process. Comprehensive visualization tools serve a dual purpose: they offer human-interpretable insights while also generating rich feature representations that support the anomaly detection pipeline and the DRL agent.
{"title":"AMCFF-RL: An Adaptive Multi-Modal CAN Bus Fuzzing Framework Leveraging Deep Reinforcement Learning","authors":"Manu Jo Varghese;Frank Jiang;Abdur Rakib;Robin Doss;Adnan Anwar","doi":"10.1109/OJVT.2026.3659052","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3659052","url":null,"abstract":"The increasing complexity and connectivity of modern vehicles have made automotive networks, particularly the Controller Area Network (CAN) bus, vulnerable to cyberattacks. Fuzzing is a critical technique for proactively finding security weaknesses, but traditional methods are inefficient and struggle to scale with the complexity of modern vehicles. This paper introduces AMCFF-RL, an adaptive framework that uses Deep Reinforcement Learning (DRL) with multi-modal feature extraction to systematically analyse for vulnerabilities. Rather than relying on unguided or purely random fuzzing, AMCFF-RL integrates multi-modal feature extraction with DRL and advanced visualization, allowing it to learn and adapt its strategy based on real-time feedback from the network and thereby improve the efficiency and effectiveness of the fuzzing process. Comprehensive visualization tools serve a dual purpose: they offer human-interpretable insights while also generating rich feature representations that support the anomaly detection pipeline and the DRL agent.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"612-625"},"PeriodicalIF":4.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175655","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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