The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems.
{"title":"Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges","authors":"Tiziana Cattai;Francesco Frattolillo;Andrea Lacava;Prasanna Raut;Jennifer Simonjan;Salvatore D'Oro;Tommaso Melodia;Evgenii Vinogradov;Enrico Natalizio;Stefania Colonnese;Francesca Cuomo;Luca Iocchi","doi":"10.1109/OJVT.2025.3586774","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3586774","url":null,"abstract":"The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2067-2081"},"PeriodicalIF":4.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773297","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 : 2025-07-02DOI: 10.1109/OJVT.2025.3585537
Donggu Lee;Ozgur Ozdemir;Ram Asokan;Ismail Guvenc
Uncrewed aerial vehicles (UAVs) are expected to play a key role in 6G-enabled vehicular-to-everything (V2X) communications, requiring high data rates, low latency, and reliable connectivity for mission-critical applications. Multi-input multi-output (MIMO) technology is essential for meeting these demands. However, UAV link performance is significantly affected by environmental factors such as signal attenuation, multipath propagation, and blockage from obstacles, particularly dense foliage in rural areas. In this paper, we investigate RF coverage and channel rank over UAV channels in foliage-dominated rural environments using ray tracing (RT) simulations. We conduct RT-based channel rank and RF coverage analysis over Lake Wheeler Field Labs at NC State University to examine the impact on UAV links. Custom-modeled trees are integrated into the RT simulations using NVIDIA Sionna, Blender, and the Open Street Map (OSM) database to capture realistic blockage effects. Results indicate that tree-induced blockage impacts RF coverage and channel rank at lower UAV altitudes. We also propose a Kriging interpolation-based 3D channel rank interpolation scheme, leveraging the observed spatial correlation of channel rank in the given environments. The accuracy of the proposed scheme is evaluated using the mean absolute error (MAE) metric and compared against baseline interpolation methods. Finally, we compare the RT-based received signal strength (RSS) and channel rank results with real-world measurements from the NSF AERPAW testbed, demonstrating reasonable consistency between simulation results and the measurements.
{"title":"Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios With Foliage","authors":"Donggu Lee;Ozgur Ozdemir;Ram Asokan;Ismail Guvenc","doi":"10.1109/OJVT.2025.3585537","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3585537","url":null,"abstract":"Uncrewed aerial vehicles (UAVs) are expected to play a key role in 6G-enabled vehicular-to-everything (V2X) communications, requiring high data rates, low latency, and reliable connectivity for mission-critical applications. Multi-input multi-output (MIMO) technology is essential for meeting these demands. However, UAV link performance is significantly affected by environmental factors such as signal attenuation, multipath propagation, and blockage from obstacles, particularly dense foliage in rural areas. In this paper, we investigate RF coverage and channel rank over UAV channels in foliage-dominated rural environments using ray tracing (RT) simulations. We conduct RT-based channel rank and RF coverage analysis over Lake Wheeler Field Labs at NC State University to examine the impact on UAV links. Custom-modeled trees are integrated into the RT simulations using NVIDIA Sionna, Blender, and the Open Street Map (OSM) database to capture realistic blockage effects. Results indicate that tree-induced blockage impacts RF coverage and channel rank at lower UAV altitudes. We also propose a Kriging interpolation-based 3D channel rank interpolation scheme, leveraging the observed spatial correlation of channel rank in the given environments. The accuracy of the proposed scheme is evaluated using the mean absolute error (MAE) metric and compared against baseline interpolation methods. Finally, we compare the RT-based received signal strength (RSS) and channel rank results with real-world measurements from the NSF AERPAW testbed, demonstrating reasonable consistency between simulation results and the measurements.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1943-1962"},"PeriodicalIF":5.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11066253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687724","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 : 2025-07-01DOI: 10.1109/OJVT.2025.3585160
Sooyeob Jung;Seongah Jeong;Jinkyu Kang;Gyeongrae Im;Sangjae Lee;Mi-Kyung Oh;Joon Gyu Ryu;Joonhyuk Kang
This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising non-terrestrial network (NTN) solution to provide high-traffic and delay-tolerant data transfer services, such as wide-area situational awareness (WASA) in smart grids and car share management in automotive applications, to IoT devices in global coverage. In particular, this study provides guidelines for the overall DtS-IoT system architecture and design details that conform to the Long Range Wide-Area Network (LoRaWAN). Furthermore, we also detail various DtS-IoT use cases. Considering low-Earth orbit (LEO) satellites, we develop the LR-FHSS transceiver to improve system efficiency, which is a leading attempt to build practical satellite communication systems using LR-FHSS, excluding commercial products. Moreover, we apply a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced soft-output-Viterbi-algorithm (SOVA) for the header and payload receptions. Lastly, we present proof-of-concept implementation and testbeds using an application-specific integrated circuit (ASIC) chipset and a field-programmable gate array (FPGA) that verify the performance of the proposed LR-FHSS transceiver design of DtS-IoT communication systems. The laboratory test results reveal that the proposed LR-FHSS-based framework with the robust synchronization technique can provide wide coverage, seamless connectivity, and high-throughput communication links for the realization of future satellite communication networks.
{"title":"LR-FHSS Transceiver for Direct-to-Satellite IoT Communications: Design, Implementation, and Verification","authors":"Sooyeob Jung;Seongah Jeong;Jinkyu Kang;Gyeongrae Im;Sangjae Lee;Mi-Kyung Oh;Joon Gyu Ryu;Joonhyuk Kang","doi":"10.1109/OJVT.2025.3585160","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3585160","url":null,"abstract":"This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising non-terrestrial network (NTN) solution to provide high-traffic and delay-tolerant data transfer services, such as wide-area situational awareness (WASA) in smart grids and car share management in automotive applications, to IoT devices in global coverage. In particular, this study provides guidelines for the overall DtS-IoT system architecture and design details that conform to the Long Range Wide-Area Network (LoRaWAN). Furthermore, we also detail various DtS-IoT use cases. Considering low-Earth orbit (LEO) satellites, we develop the LR-FHSS transceiver to improve system efficiency, which is a leading attempt to build practical satellite communication systems using LR-FHSS, excluding commercial products. Moreover, we apply a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced soft-output-Viterbi-algorithm (SOVA) for the header and payload receptions. Lastly, we present proof-of-concept implementation and testbeds using an application-specific integrated circuit (ASIC) chipset and a field-programmable gate array (FPGA) that verify the performance of the proposed LR-FHSS transceiver design of DtS-IoT communication systems. The laboratory test results reveal that the proposed LR-FHSS-based framework with the robust synchronization technique can provide wide coverage, seamless connectivity, and high-throughput communication links for the realization of future satellite communication networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1920-1942"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687678","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}
This paper presents a novel stochastic channel model (SCM) for wireless personal area network (WPAN) short-range scenarios based on the third-generation partnership project (3GPP) framework and line-of-sight (LoS) multipath measurements at 105 GHz with a 4 GHz bandwidth channel sounder. We derive the statistical distributions of large- and small-scale parameters in a conference room, corridor, and office room for the WPAN usage. The ranges of transmitter (TX)–receiver (RX) distances are 0.7–4 m, 1–5 m, and 2–5 m, respectively. We conducted measurements at eight RX positions in the conference room and the corridor, and four RX positions in the office room. The TX and RX were placed at a height of 0.15 m from the table in the conference and office rooms, and at a height of 1.3 m from the floor in corridors. Our primary objective is to enable high-speed WPAN indoor communications in the sub-terahertz (THz) band while using the widely adopted 3GPP standards physical layer waveform for WPAN systems to promote broader commercial adoption. Hence, in this paper, we propose a channel simulation framework based on the well-accepted 3GPP SCM, thereby facilitating seamless system design and evaluation within the 3GPP community. The proposed SCM is validated based on extensive channel generation simulations by demonstrating the consistency of the generated channel responses with our propagation channel measurements.
{"title":"A Novel Stochastic Channel Model for WPAN Short-Range Scenarios Based on 3GPP Framework and LoS Multipath Measurements at 105 GHz","authors":"Mihiro Hashimoto;Yusuke Koda;Norichika Ohmi;Hiroaki Endo;Hiroshi Harada","doi":"10.1109/OJVT.2025.3584903","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3584903","url":null,"abstract":"This paper presents a novel stochastic channel model (SCM) for wireless personal area network (WPAN) short-range scenarios based on the third-generation partnership project (3GPP) framework and line-of-sight (LoS) multipath measurements at 105 GHz with a 4 GHz bandwidth channel sounder. We derive the statistical distributions of large- and small-scale parameters in a conference room, corridor, and office room for the WPAN usage. The ranges of transmitter (TX)–receiver (RX) distances are 0.7–4 m, 1–5 m, and 2–5 m, respectively. We conducted measurements at eight RX positions in the conference room and the corridor, and four RX positions in the office room. The TX and RX were placed at a height of 0.15 m from the table in the conference and office rooms, and at a height of 1.3 m from the floor in corridors. Our primary objective is to enable high-speed WPAN indoor communications in the sub-terahertz (THz) band while using the widely adopted 3GPP standards physical layer waveform for WPAN systems to promote broader commercial adoption. Hence, in this paper, we propose a channel simulation framework based on the well-accepted 3GPP SCM, thereby facilitating seamless system design and evaluation within the 3GPP community. The proposed SCM is validated based on extensive channel generation simulations by demonstrating the consistency of the generated channel responses with our propagation channel measurements.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2155-2170"},"PeriodicalIF":4.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061805","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853049","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 massive multiple-input multiple-output (MIMO) systems utilizing the frequency-division duplex (FDD), conventional methods for acquiring downlink channel state information (CSI) lead to high computational complexity and heavy feedback overheads. To tackle the aforementioned difficulties, we propose an end-to-end deep learning (DL)-based framework for CSI acquisition, called high-speed orthogonal probabilistic feature-based attention network (HOPFNet), which integrates pilot design and CSI feedback. Unlike conventional end-to-end network designs, HOPFNet ignores channel estimation at the user equipment (UE). Instead, it directly maps the pilot signals received at the UE into feedback codewords, which are then transmitted back to the base station (BS) to reconstruct the downlink channel. In recent years, Transformer-based networks have proven highly effective for CSI acquisition. However, the self-attention mechanism of Transformer-based networks introduces high computational complexity, posing challenges to actual deployment. To this end, we propose a lightweight Transformer, which is based on a high-speed orthogonal probabilistic feature-based attention (HOPFA) mechanism. The simulation results verify that the proposed HOPFNet can significantly reduce computation complexity while attaining lower normalized mean square error (NMSE) compared to the benchmark models. In addition, these results demonstrate superior efficiency in computing resources.
{"title":"HOPFNet: An End-to-End CSI Acquisition Method for FDD Massive MIMO Systems","authors":"Qiang Sun;Haoye Li;Yushi Shen;Honghui Ji;Zejun Li;Miaomiao Xu;Jiayi Zhang","doi":"10.1109/OJVT.2025.3584626","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3584626","url":null,"abstract":"In massive multiple-input multiple-output (MIMO) systems utilizing the frequency-division duplex (FDD), conventional methods for acquiring downlink channel state information (CSI) lead to high computational complexity and heavy feedback overheads. To tackle the aforementioned difficulties, we propose an end-to-end deep learning (DL)-based framework for CSI acquisition, called high-speed orthogonal probabilistic feature-based attention network (HOPFNet), which integrates pilot design and CSI feedback. Unlike conventional end-to-end network designs, HOPFNet ignores channel estimation at the user equipment (UE). Instead, it directly maps the pilot signals received at the UE into feedback codewords, which are then transmitted back to the base station (BS) to reconstruct the downlink channel. In recent years, Transformer-based networks have proven highly effective for CSI acquisition. However, the self-attention mechanism of Transformer-based networks introduces high computational complexity, posing challenges to actual deployment. To this end, we propose a lightweight Transformer, which is based on a high-speed orthogonal probabilistic feature-based attention (HOPFA) mechanism. The simulation results verify that the proposed HOPFNet can significantly reduce computation complexity while attaining lower normalized mean square error (NMSE) compared to the benchmark models. In addition, these results demonstrate superior efficiency in computing resources.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1977-1989"},"PeriodicalIF":4.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11059824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739945","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}
This paper investigates the impact of randomly deployed non-orthogonal co-channel interference (CCI), originating from the information exchange process among non-orthogonal multiple access (NOMA) users, in an active, passive, and absorptive reconfigurable intelligent surface (RIS)-assisted dual-hop network. More specifically, the study considers that the information exchange process involves the source utilizing active, passive, or absorptive RIS architecture, along with a line of sight (LOS)/non-line of sight (NLOS) link between source and destination terminals. Additionally, this study considers the limited non-orthogonal CCI affecting the destination terminal in an independent and identically distributed (i.i.d.) non-orthogonal CCI scenario. Theoretical insights and Monte Carlo-based simulations collectively demonstrate that non-orthogonal CCI severely degrades system performance, particularly in high signal-to-noise ratio conditions, leading to notable losses in system coding gain. Meanwhile, results also reveal that increasing the number of RIS elements stabilizes the system and mitigates the impact of CCI on its performance.
{"title":"Active, Passive, and Absorptive RIS-Aided 6G Network Under Non-Orthogonal CCI","authors":"Volkan Özduran;Ehsan Soleimani-Nasab;Nikolaos Nomikos;Imran Shafique Ansari;Panagiotis Trakadas","doi":"10.1109/OJVT.2025.3583545","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3583545","url":null,"abstract":"This paper investigates the impact of randomly deployed non-orthogonal co-channel interference (CCI), originating from the information exchange process among non-orthogonal multiple access (NOMA) users, in an active, passive, and absorptive reconfigurable intelligent surface (RIS)-assisted dual-hop network. More specifically, the study considers that the information exchange process involves the source utilizing active, passive, or absorptive RIS architecture, along with a line of sight (LOS)/non-line of sight (NLOS) link between source and destination terminals. Additionally, this study considers the limited non-orthogonal CCI affecting the destination terminal in an independent and identically distributed (i.i.d.) non-orthogonal CCI scenario. Theoretical insights and Monte Carlo-based simulations collectively demonstrate that non-orthogonal CCI severely degrades system performance, particularly in high signal-to-noise ratio conditions, leading to notable losses in system coding gain. Meanwhile, results also reveal that increasing the number of RIS elements stabilizes the system and mitigates the impact of CCI on its performance.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2048-2066"},"PeriodicalIF":4.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11052664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757680","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 : 2025-06-25DOI: 10.1109/OJVT.2025.3582885
Kunlun Li;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo
Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.
{"title":"Indoor Localization and Tracking in Reconfigurable Intelligent Surface Aided mmWave Systems","authors":"Kunlun Li;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo","doi":"10.1109/OJVT.2025.3582885","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3582885","url":null,"abstract":"Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1815-1831"},"PeriodicalIF":5.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11050944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671199","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}
Traditionally, battery cells used for electric vehicles are designed to have a high-energy density. This paper studies the use of a high-power density battery for an electric vehicle, which results in lower losses but a higher battery weight compared with the use of a high-energy density battery. The two batteries are compared with power Hardware-In-the-Loop tests for a Nissan Leaf. The experimental results show that energy consumption is slightly lower for the high-power battery despite an increase in the total mass. The improvement in energy consumption is up to 4.5% for high-speed driving cycles despite an increase of 103 kg for the vehicle weight. Moreover, the fast-charging time is divided by 2 with the high-power battery due to lower self-heating.
{"title":"Comparison of High-Energy Density and High-Power Density Batteries for an Electric Vehicle","authors":"Salma Fadili;Ronan German;Alain Bouscayrol;Clément Mayet;Philippe Fiani;Eric Noirtat","doi":"10.1109/OJVT.2025.3583070","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3583070","url":null,"abstract":"Traditionally, battery cells used for electric vehicles are designed to have a high-energy density. This paper studies the use of a high-power density battery for an electric vehicle, which results in lower losses but a higher battery weight compared with the use of a high-energy density battery. The two batteries are compared with power Hardware-In-the-Loop tests for a Nissan Leaf. The experimental results show that energy consumption is slightly lower for the high-power battery despite an increase in the total mass. The improvement in energy consumption is up to 4.5% for high-speed driving cycles despite an increase of 103 kg for the vehicle weight. Moreover, the fast-charging time is divided by 2 with the high-power battery due to lower self-heating.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1910-1919"},"PeriodicalIF":4.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11050920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739804","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 : 2025-06-24DOI: 10.1109/OJVT.2025.3581878
David Alejandro Urquiza Villalonga;Alejandro López Barrios;Máximo Morales-Céspedes;M. Julia Fernández-Getino García
Wireless networks are evolving to provide high data rates, ultra-low latency, reliable communications, and the connectivity of multiple devices in a reduced area. However, massive densification of networks leads to an increase in interfering signals. In this context, interference alignment (IA) algorithms have been proposed to manage interference while increasing the achievable degrees of freedom. However, the practical implementation of IA algorithms faces several issues such as the lack of perfect channel state information (CSI), network synchronization, or modeling a highly heterogeneous signal-to-interference-plus-noise (SINR) distribution. In this work, we propose an experimental evaluation of IA emulating an interference-limited network but focusing on the user perspective. In contrast to previous works, a hardware testbed with universal software radio peripherals (USRPs) is implemented to model heterogeneous SINR networks. The role of both closed and open loops for providing CSI is evaluated. Then, the impact of CSI updating on the spectral efficiency and also on the bit error rate (BER) is analyzed. Furthermore, precoding techniques such as zero-forcing (ZF) or singular value decomposition (SVD) are also considered for comparison purposes. All the results are based on real measurements providing valuable insights into the performance of IA algorithms in real wireless networks.
{"title":"Hardware Evaluation of Interference Alignment Techniques Under Different Channel State Information Updating Rates","authors":"David Alejandro Urquiza Villalonga;Alejandro López Barrios;Máximo Morales-Céspedes;M. Julia Fernández-Getino García","doi":"10.1109/OJVT.2025.3581878","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3581878","url":null,"abstract":"Wireless networks are evolving to provide high data rates, ultra-low latency, reliable communications, and the connectivity of multiple devices in a reduced area. However, massive densification of networks leads to an increase in interfering signals. In this context, interference alignment (IA) algorithms have been proposed to manage interference while increasing the achievable degrees of freedom. However, the practical implementation of IA algorithms faces several issues such as the lack of perfect channel state information (CSI), network synchronization, or modeling a highly heterogeneous signal-to-interference-plus-noise (SINR) distribution. In this work, we propose an experimental evaluation of IA emulating an interference-limited network but focusing on the user perspective. In contrast to previous works, a hardware testbed with universal software radio peripherals (USRPs) is implemented to model heterogeneous SINR networks. The role of both closed and open loops for providing CSI is evaluated. Then, the impact of CSI updating on the spectral efficiency and also on the bit error rate (BER) is analyzed. Furthermore, precoding techniques such as zero-forcing (ZF) or singular value decomposition (SVD) are also considered for comparison purposes. All the results are based on real measurements providing valuable insights into the performance of IA algorithms in real wireless networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1760-1773"},"PeriodicalIF":5.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11046342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641054","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 : 2025-06-23DOI: 10.1109/OJVT.2025.3582035
Ghada Afifi;Bassem Mokhtar
Smart vehicles are increasingly equipped with advanced sensors and computational resources which enable them to detect surroundings and enhance driving safety. VEoTC (Vehicular Edge of Things Computing) solutions aim to exploit these embedded sensors and resources to provide computational services to other users. VEoTC can enhance the Quality of Experience (QoE) of vehicle and mobile users requesting computational tasks by providing context-aware services closer to the users that are otherwise not easily accessible in real time. Additionally, such solutions can extend the computational coverage to areas lacking Roadside Unit (RSU) infrastructure. However, VEoTC frameworks face several challenges in effectively localizing and allocating the distributed resources and offloading tasks successfully due to the high mobility of vehicles and fluctuating user densities. The paper proposes a distributed Machine Learning (ML)-based solution which optimizes task scheduling to smart vehicles and/or RSUs through joint resource allocation and task offloading. We formulate a belief-based optimization problem to maximize the QoE of vehicular users while providing performance guarantees that account for geospatial uncertainty associated with the availability of embedded resources. We propose a Deep Reinforcement Learning (DRL)-based solution to solve the formulated problem in real-time adapting to the dynamic network conditions. We analyze the performance of the proposed approach as compared to benchmark optimization and other ML-based techniques. Furthermore, we conduct hardware-based field test experiments to verify the effectiveness of our proposed algorithm to satisfy the stringent real-time latency requirements for various vehicular applications. According to our extensive simulation and experimental results, the proposed solution has the potential to satisfy the stringent QoE guarantees required for critical road safety applications.
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