Pub Date : 2026-01-16DOI: 10.1109/OJVT.2026.3651868
Edward Au
{"title":"Editor-in-Chief's Messages With Gratitude and Pride: A Year of Growth and Shared Excellence","authors":"Edward Au","doi":"10.1109/OJVT.2026.3651868","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3651868","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"i-i"},"PeriodicalIF":4.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11356006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982307","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}
Machine learning (ML) and deep learning (DL) have become essential tools in lithium-ion battery research, particularly for estimating the State of Health (SOH). However, conventional SOH estimation methods often rely on repeated charge/discharge cycles under strictly controlled laboratory conditions, limiting their applicability in real world scenarios. In this study, we present a comprehensive lithium-ion battery dataset developed by our team to support data driven approaches for battery diagnostics and predictive modeling. The dataset comprises nineteen lithium iron phosphate (LFP) cells with cycle lifetimes ranging from 500 to 2600 cycles and reflects realistic usage conditions, including non constant discharge currents and tests conducted at $25,^circ text{C}$, $35,^circ text{C}$, and $45,^circ text{C}$. To demonstrate the utility of this dataset, we used a brain inspired Spiking Neural Network (SNN) referred to as SpikeSOH, a neuromorphic model that uses sparse, time coded spikes to mimic biological neurons. This approach provides temporal precision while reducing energy consumption. Our results show that the SNN-based model achieves an average Mean Absolute Error (MAE) of 4.5%, while also outperforming conventional deep learning models in computational efficiency, with an average inference time of 3.55 $mu mathrm{s}$ and an average energy consumption of 0.36 mJ. These characteristics make the model particularly suitable for integration into energy constrained battery management systems. By providing a realistic, high quality dataset and demonstrating the advantages of energy efficient neuromorphic models, this work advances accurate and scalable SOH estimation methods, helping safer and more reliable deployment of lithium-ion batteries in both first life and second life applications.
{"title":"Spiking Neural Networks for Accurate and Efficient State of Health Estimation of Lithium-Ion Batteries Across Varying Temperatures","authors":"Slimane Arbaoui;Tedjani Mesbahi;Théo Heitzmann;Marwa Zitouni;Amel Hidouri;Lakhdar Mamouri;Ali Ayadi;Ahmed Samet;Romuald Boné","doi":"10.1109/OJVT.2026.3653419","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3653419","url":null,"abstract":"Machine learning (ML) and deep learning (DL) have become essential tools in lithium-ion battery research, particularly for estimating the State of Health (SOH). However, conventional SOH estimation methods often rely on repeated charge/discharge cycles under strictly controlled laboratory conditions, limiting their applicability in real world scenarios. In this study, we present a comprehensive lithium-ion battery dataset developed by our team to support data driven approaches for battery diagnostics and predictive modeling. The dataset comprises nineteen lithium iron phosphate (LFP) cells with cycle lifetimes ranging from 500 to 2600 cycles and reflects realistic usage conditions, including non constant discharge currents and tests conducted at <inline-formula><tex-math>$25,^circ text{C}$</tex-math></inline-formula>, <inline-formula><tex-math>$35,^circ text{C}$</tex-math></inline-formula>, and <inline-formula><tex-math>$45,^circ text{C}$</tex-math></inline-formula>. To demonstrate the utility of this dataset, we used a brain inspired Spiking Neural Network (SNN) referred to as SpikeSOH, a neuromorphic model that uses sparse, time coded spikes to mimic biological neurons. This approach provides temporal precision while reducing energy consumption. Our results show that the SNN-based model achieves an average Mean Absolute Error (MAE) of 4.5%, while also outperforming conventional deep learning models in computational efficiency, with an average inference time of 3.55 <inline-formula><tex-math>$mu mathrm{s}$</tex-math></inline-formula> and an average energy consumption of 0.36 mJ. These characteristics make the model particularly suitable for integration into energy constrained battery management systems. By providing a realistic, high quality dataset and demonstrating the advantages of energy efficient neuromorphic models, this work advances accurate and scalable SOH estimation methods, helping safer and more reliable deployment of lithium-ion batteries in both first life and second life applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"510-522"},"PeriodicalIF":4.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082179","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}
Bird’s-Eye View (BEV) maps provide a top-down semantic representation of the driving environment, which is critical for downstream tasks such as planning and navigation in autonomous driving systems. However, generating accurate BEV maps from on-board sensors remains challenging due to perspective distortion, occlusions, and the high computational cost of dense 3D reasoning, particularly under real-time constraints. Existing approaches often trade inference speed for accuracy or rely heavily on camera-based perception, limiting deployability. Therefore, this work presents a novel and efficient map segmentation architecture, using cameras and radars, in the BEV space by combining rich semantic information from cameras with accurate distance measurements from radar, without incurring excessive financial costs or overwhelming data processing requirements. Our model introduces a real-time map segmentation architecture considering aspects such as high accuracy, per-class balancing, and inference time. To accomplish this, we use an advanced loss set together with a new lightweight head to improve the perception results. Our results show that, with these modifications, our approach achieves results comparable to large models, reaching 53.5 mIoU, while also setting a new benchmark for inference time, improving it by 260% over the strongest baseline models.
{"title":"FIN: Fast Inference Network for Map Segmentation","authors":"Ruan Bispo;Tim Brophy;Reenu Mohandas;Anthony Scanlan;Ciarán Eising","doi":"10.1109/OJVT.2026.3652797","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3652797","url":null,"abstract":"Bird’s-Eye View (BEV) maps provide a top-down semantic representation of the driving environment, which is critical for downstream tasks such as planning and navigation in autonomous driving systems. However, generating accurate BEV maps from on-board sensors remains challenging due to perspective distortion, occlusions, and the high computational cost of dense 3D reasoning, particularly under real-time constraints. Existing approaches often trade inference speed for accuracy or rely heavily on camera-based perception, limiting deployability. Therefore, this work presents a novel and efficient map segmentation architecture, using cameras and radars, in the BEV space by combining rich semantic information from cameras with accurate distance measurements from radar, without incurring excessive financial costs or overwhelming data processing requirements. Our model introduces a real-time map segmentation architecture considering aspects such as high accuracy, per-class balancing, and inference time. To accomplish this, we use an advanced loss set together with a new lightweight head to improve the perception results. Our results show that, with these modifications, our approach achieves results comparable to large models, reaching 53.5 mIoU, while also setting a new benchmark for inference time, improving it by 260% over the strongest baseline models.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"523-536"},"PeriodicalIF":4.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11344801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082196","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 : 2026-01-12DOI: 10.1109/OJVT.2026.3652024
Sharvil Bhatt;Jayaprakash Kar
While Vehicular Ad-hoc Networks (VANETs) can potentially improve driver safety and traffic management efficiency (e.g. through timely sharing of traffic status among vehicles), security and privacy are two ongoing issues that need to be addressed. This article proposes a novel blockchain-based authentication scheme leveraging conditional privacy within temporary platoons. The proposed model is an extended model based on Temporary Blockchain Platoons Formation. Our scheme utilizes blockchain technology to ensure data integrity and prevent unauthorized access, while enabling conditional privacy preservation where data sharing is limited based on pre-defined trust levels within the platoon. This significantly reduces reliance on Roadside Units (RSUs), leading to a cost-effective approach. Compared to traditional Public Key Infrastructure (PKI), our scheme offers improved security, enhanced privacy, and reduced infrastructure costs, promoting wider VANET implementation. The augmented model also incorporates an “Accident Prevention Mechanism,” a critical component absent in the preceding Platoon proposed model, thereby enhancing the overall safety and robustness of the system. Apart from the extra proposed features, rest features and mechanisms will be working as in already present Platoon Model.
{"title":"A Blockchain-Based Privacy-Preserving Authentication Scheme for Secure Platoon Communications in VANET","authors":"Sharvil Bhatt;Jayaprakash Kar","doi":"10.1109/OJVT.2026.3652024","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3652024","url":null,"abstract":"While Vehicular Ad-hoc Networks (VANETs) can potentially improve driver safety and traffic management efficiency (e.g. through timely sharing of traffic status among vehicles), security and privacy are two ongoing issues that need to be addressed. This article proposes a novel blockchain-based authentication scheme leveraging conditional privacy within temporary platoons. The proposed model is an extended model based on Temporary Blockchain Platoons Formation. Our scheme utilizes blockchain technology to ensure data integrity and prevent unauthorized access, while enabling conditional privacy preservation where data sharing is limited based on pre-defined trust levels within the platoon. This significantly reduces reliance on Roadside Units (RSUs), leading to a cost-effective approach. Compared to traditional Public Key Infrastructure (PKI), our scheme offers improved security, enhanced privacy, and reduced infrastructure costs, promoting wider VANET implementation. The augmented model also incorporates an “Accident Prevention Mechanism,” a critical component absent in the preceding Platoon proposed model, thereby enhancing the overall safety and robustness of the system. Apart from the extra proposed features, rest features and mechanisms will be working as in already present Platoon Model.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"468-490"},"PeriodicalIF":4.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11340613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082018","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 recent years, the research interest in bidirectional charging of electric vehicles has increased significantly, driven by improved accessibility to charging and payment information as well as the increasing emphasis on integrating variable renewable energy sources more effectively into the grid. Integrating bidirectional charging with the grid/building/home can also reduce grid congestion. Despite this, broader implementation of this technology has not yet been achieved. In this context, this article comprehensively surveys direct current (DC) off-board vehicle to grid/building/home chargers and analyses the gaps which prevent the technologies’ wide implementation. These gaps are analysed by considering areas such as the development direction of bidirectional charging technology, battery cost and its degradation, V2G applicable standards, grid codes and charging protocols, deployment of V2G chargers (off-board versus on-board/wireless), market feasibility of V2G services, and the cost of bidirectional off-board chargers. The first survey of twenty-five commercial bidirectional chargers is presented and investigated in relation to the above-mentioned areas. Four key (technical, regulatory, financial, and behavioural) barriers are identified and discussed for the wide implementation of vehicle to grid/building/home charging.
{"title":"DC Off-Board Vehicle to Grid/Building/Home: A Survey and Gap Analysis","authors":"Carina Engström;Gautam Rituraj;Koen Linders;Marcel Esser;Wenli Shi;Ville Tikka;Gautham Ram Chandra Mouli","doi":"10.1109/OJVT.2026.3652429","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3652429","url":null,"abstract":"In recent years, the research interest in bidirectional charging of electric vehicles has increased significantly, driven by improved accessibility to charging and payment information as well as the increasing emphasis on integrating variable renewable energy sources more effectively into the grid. Integrating bidirectional charging with the grid/building/home can also reduce grid congestion. Despite this, broader implementation of this technology has not yet been achieved. In this context, this article comprehensively surveys direct current (DC) off-board vehicle to grid/building/home chargers and analyses the gaps which prevent the technologies’ wide implementation. These gaps are analysed by considering areas such as the development direction of bidirectional charging technology, battery cost and its degradation, V2G applicable standards, grid codes and charging protocols, deployment of V2G chargers (off-board versus on-board/wireless), market feasibility of V2G services, and the cost of bidirectional off-board chargers. The first survey of twenty-five commercial bidirectional chargers is presented and investigated in relation to the above-mentioned areas. Four key (technical, regulatory, financial, and behavioural) barriers are identified and discussed for the wide implementation of vehicle to grid/building/home charging.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"448-467"},"PeriodicalIF":4.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11344785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082168","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 : 2026-01-12DOI: 10.1109/OJVT.2026.3651808
Joseba Sarabia;Mauricio Marcano;Sergio Diaz;Joshué Pérez Rastelli;Asier Zubizarreta
This article presents an openly documented real-vehicle evaluation of an adaptive shared control system that integrates a nonlinear Model Predictive Controller (NMPC), a Driver Monitoring System (DMS) and a haptic Human Machine Interface (HMI). While similar concepts have been explored by several industrial players, detailed performance data and transparent methodologies remain scarce in the public domain. This work aims to help fill that gap by providing insights into the behavior and integration of such systems under real driving conditions. The system establishes an active safety envelope for manual driving scenarios, employing a shared control strategy that activates during potentially hazardous situations caused by driver misbehavior or external road challenges. A key contribution of this work is the real-world validation of the approach in a vehicle with human drivers, demonstrating how shared control can be effectively deployed beyond simulation. The proposed framework integrates three elements: 1) an NMPC-based shared controller that shifts control authority across three different modes (Electronic Power Steering, Shared Control, and Fully Automated) by adjusting its weights; 2) a DMS that triggers mode switching; and 3) a haptic feedback interface that communicates system state, transitions, and driver intervention requests. The controller enforces a lane-keeping safety envelope and intervenes only when necessary. Results from real-vehicle experiments across two use cases show that the system enhances safety by mitigating lane-departure risks and improving driver attentiveness.
{"title":"Evaluating Shared Control in Real-World Conditions","authors":"Joseba Sarabia;Mauricio Marcano;Sergio Diaz;Joshué Pérez Rastelli;Asier Zubizarreta","doi":"10.1109/OJVT.2026.3651808","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3651808","url":null,"abstract":"This article presents an openly documented real-vehicle evaluation of an adaptive shared control system that integrates a nonlinear Model Predictive Controller (NMPC), a Driver Monitoring System (DMS) and a haptic Human Machine Interface (HMI). While similar concepts have been explored by several industrial players, detailed performance data and transparent methodologies remain scarce in the public domain. This work aims to help fill that gap by providing insights into the behavior and integration of such systems under real driving conditions. The system establishes an active safety envelope for manual driving scenarios, employing a shared control strategy that activates during potentially hazardous situations caused by driver misbehavior or external road challenges. A key contribution of this work is the real-world validation of the approach in a vehicle with human drivers, demonstrating how shared control can be effectively deployed beyond simulation. The proposed framework integrates three elements: 1) an NMPC-based shared controller that shifts control authority across three different modes (Electronic Power Steering, Shared Control, and Fully Automated) by adjusting its weights; 2) a DMS that triggers mode switching; and 3) a haptic feedback interface that communicates system state, transitions, and driver intervention requests. The controller enforces a lane-keeping safety envelope and intervenes only when necessary. Results from real-vehicle experiments across two use cases show that the system enhances safety by mitigating lane-departure risks and improving driver attentiveness.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"418-431"},"PeriodicalIF":4.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11339954","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082121","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 : 2026-01-06DOI: 10.1109/OJVT.2025.3646498
{"title":"IEEE Open Journal of Vehicular Technology Information for Authors","authors":"","doi":"10.1109/OJVT.2025.3646498","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3646498","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"C3-C3"},"PeriodicalIF":4.8,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929682","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}
Continuous-variable quantum key distribution (CV-QKD) systems face challenges in maintaining efficient reconciliation over long distances due to the time variant signal-to-noise ratio (SNR) imposed by channel quality fluctuations. Hence fixed-rate error-correction schemes using low-density parity-check (LDPC) or Polar codes lead to high block-error rates (BLER) and degraded secret key rates (SKR). To overcome this, we propose an incremental redundancy aided hybrid automatic repeat request (IR-HARQ) protocol using rate-compatible Polar and LDPC codes. Explicitly, by puncturing a mother code and progressively transmitting additional redundant bits, our method dynamically adapts the effective coding rate to the prevalent channel conditions, achieving 2–3 dB SNR gains per retransmission. This adaptive strategy avoids unnecessary redundancy in good channels and strengthens protection in poor channels, thereby improving reconciliation efficiency. Simulation results show that our IR-HARQ scheme significantly enhances the BLER, throughput, and secure transmission distance compared with single-transmission schemes. Moreover, our study highlights that Polar IR-HARQ achieves superior performance in short block-length and low-SNR scenarios, while LDPC IR-HARQ is more competitive for longer codes and higher SNR. These findings confirm IR-HARQ as an attractive and versatile reconciliation solution for real-world CV-QKD deployments.
{"title":"Rate-Compatible Polar- and LDPC-Coded Hybrid ARQ Aided Reverse Reconciliation in CV-QKD","authors":"Dingzhao Wang;Xin Liu;Chao Xu;Soon Xin Ng;Lajos Hanzo","doi":"10.1109/OJVT.2025.3650700","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3650700","url":null,"abstract":"Continuous-variable quantum key distribution (CV-QKD) systems face challenges in maintaining efficient reconciliation over long distances due to the time variant signal-to-noise ratio (SNR) imposed by channel quality fluctuations. Hence fixed-rate error-correction schemes using low-density parity-check (LDPC) or Polar codes lead to high block-error rates (BLER) and degraded secret key rates (SKR). To overcome this, we propose an incremental redundancy aided hybrid automatic repeat request (IR-HARQ) protocol using rate-compatible Polar and LDPC codes. Explicitly, by puncturing a mother code and progressively transmitting additional redundant bits, our method dynamically adapts the effective coding rate to the prevalent channel conditions, achieving 2–3 dB SNR gains per retransmission. This adaptive strategy avoids unnecessary redundancy in good channels and strengthens protection in poor channels, thereby improving reconciliation efficiency. Simulation results show that our IR-HARQ scheme significantly enhances the BLER, throughput, and secure transmission distance compared with single-transmission schemes. Moreover, our study highlights that Polar IR-HARQ achieves superior performance in short block-length and low-SNR scenarios, while LDPC IR-HARQ is more competitive for longer codes and higher SNR. These findings confirm IR-HARQ as an attractive and versatile reconciliation solution for real-world CV-QKD deployments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"432-446"},"PeriodicalIF":4.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11328821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082197","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 : 2025-12-30DOI: 10.1109/OJVT.2025.3650061
{"title":"2025 Index IEEE Open Journal of Vehicular Technology Vol. 6","authors":"","doi":"10.1109/OJVT.2025.3650061","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3650061","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"3017-3065"},"PeriodicalIF":4.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11319354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886576","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 : 2025-12-26DOI: 10.1109/OJVT.2025.3649122
Khac-Hoang Ngo;Diego Cuevas;Ruben De Miguel Gil;Victor Monzon Baeza;Ana Garcia Armada;Ignacio Santamaria
Noncoherent communication is a promising paradigm for future wireless systems where acquiring accurate channel state information (CSI) is challenging or infeasible. It provides methods to bypass the need for explicit channel estimation in practical scenarios such as high-mobility networks, massive distributed antenna arrays, and energy-constrained Internet-of-Things devices. This survey provides a comprehensive overview of noncoherent communication strategies in multiple-input multiple-output (MIMO) systems. We classify noncoherent communication schemes into three main approaches where CSI-free signal recovery is based on subspace detection (i.e., Grassmannian signaling), differential detection, and energy detection. For each approach, we review the theoretical foundation and design methodologies. We also provide comparative insights into their suitability across different channel models and system constraints, highlighting application scenarios where noncoherent methods offer performance and scalability advantages. Furthermore, we discuss practical considerations of noncoherent communication, including compatibility with orthogonal frequency division multiplexing (OFDM), resilience to hardware impairments, and scalability with the number of users. Finally, we provide an outlook on future challenges and research directions in designing robust and efficient noncoherent systems for next-generation wireless networks.
{"title":"Noncoherent MIMO Communications: Theoretical Foundation, Design Approaches, and Future Challenges","authors":"Khac-Hoang Ngo;Diego Cuevas;Ruben De Miguel Gil;Victor Monzon Baeza;Ana Garcia Armada;Ignacio Santamaria","doi":"10.1109/OJVT.2025.3649122","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3649122","url":null,"abstract":"Noncoherent communication is a promising paradigm for future wireless systems where acquiring accurate channel state information (CSI) is challenging or infeasible. It provides methods to bypass the need for explicit channel estimation in practical scenarios such as high-mobility networks, massive distributed antenna arrays, and energy-constrained Internet-of-Things devices. This survey provides a comprehensive overview of noncoherent communication strategies in multiple-input multiple-output (MIMO) systems. We classify noncoherent communication schemes into three main approaches where CSI-free signal recovery is based on subspace detection (i.e., Grassmannian signaling), differential detection, and energy detection. For each approach, we review the theoretical foundation and design methodologies. We also provide comparative insights into their suitability across different channel models and system constraints, highlighting application scenarios where noncoherent methods offer performance and scalability advantages. Furthermore, we discuss practical considerations of noncoherent communication, including compatibility with orthogonal frequency division multiplexing (OFDM), resilience to hardware impairments, and scalability with the number of users. Finally, we provide an outlook on future challenges and research directions in designing robust and efficient noncoherent systems for next-generation wireless networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"381-401"},"PeriodicalIF":4.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026321","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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