Research on vehicle-to-everything (V2X) is attracting significant attention nowadays, driven by the recent advances in beyond-5G (B5G) networks and the multi-access edge computing (MEC) paradigm. However, the inherent heterogeneity of B5G combined with the security vulnerabilities of MEC infrastructure in dynamic V2X scenarios introduces unprecedented challenges. Efficient resource and security management in multi-domain V2X environments is vital, especially with the growing threat of distributed denial-of-service (DDoS) attacks against critical V2X services within MEC. Our approach employs the zero-touch network and service management (ZSM) standard, integrating autonomous security into end-to-end (E2E) slicing management. We consider an entire 5G network, including vehicular user equipment, radio access networks, MEC, and core components, in the presence of DDoS targeting V2X services. Our framework complies with security service-level agreements (SSLAs) and policies, autonomously deploying and interconnecting security sub-slices across domains. Security requirements are continuously monitored and, upon DDoS detection, our framework reacts with a coordinated E2E strategy. The strategy mitigates DDoS at the MEC and deploys countermeasures in neighboring domains. Performance assessment reveals effective DDoS detection and mitigation with low latency, aligned with the mission-critical nature of certain V2X services. This work is part of ETSI ZSM PoC “security SLA assurance in 5G network slices”.
{"title":"ZSM-Based E2E Security Slice Management for DDoS Attack Protection in MEC-Enabled V2X Environments","authors":"Rodrigo Asensio-Garriga;Pol Alemany;Alejandro M. Zarca;Roshan Sedar;Charalampos Kalalas;Jordi Ortiz;Ricard Vilalta;Raul Muñoz;Antonio Skarmeta","doi":"10.1109/OJVT.2024.3375448","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3375448","url":null,"abstract":"Research on vehicle-to-everything (V2X) is attracting significant attention nowadays, driven by the recent advances in beyond-5G (B5G) networks and the multi-access edge computing (MEC) paradigm. However, the inherent heterogeneity of B5G combined with the security vulnerabilities of MEC infrastructure in dynamic V2X scenarios introduces unprecedented challenges. Efficient resource and security management in multi-domain V2X environments is vital, especially with the growing threat of distributed denial-of-service (DDoS) attacks against critical V2X services within MEC. Our approach employs the zero-touch network and service management (ZSM) standard, integrating autonomous security into end-to-end (E2E) slicing management. We consider an entire 5G network, including vehicular user equipment, radio access networks, MEC, and core components, in the presence of DDoS targeting V2X services. Our framework complies with security service-level agreements (SSLAs) and policies, autonomously deploying and interconnecting security sub-slices across domains. Security requirements are continuously monitored and, upon DDoS detection, our framework reacts with a coordinated E2E strategy. The strategy mitigates DDoS at the MEC and deploys countermeasures in neighboring domains. Performance assessment reveals effective DDoS detection and mitigation with low latency, aligned with the mission-critical nature of certain V2X services. This work is part of ETSI ZSM PoC “security SLA assurance in 5G network slices”.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"485-495"},"PeriodicalIF":6.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10465254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340026","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 : 2024-03-09DOI: 10.1109/OJVT.2024.3399072
Hongzhao Zheng;Mohamed Atia;Halim Yanikomeroglu
Channel and delay coefficient are two essential parameters for the characterization of a multipath propagation environment. It is crucial to generate realistic channel and delay coefficient in order to study the channel characteristics that involves signals propagating through environments with severe multipath effects. While many deterministic channel models, such as ray-tracing (RT), face challenges like high computational complexity, data requirements for geometrical information, and inapplicability for space-ground links, and nongeometry-based stochastic channel models (NGSCMs) might lack spatial consistency and offer lower accuracy, we present a scalable tutorial for the channel modeling of dual mobile space-ground links in urban areas, utilizing the Quasi Deterministic Radio Channel Generator (QuaDRiGa), which adopts a geometry-based stochastic channel model (GSCM), in conjunction with an International Telecommunication Union (ITU) provided state duration model. This tutorial allows for the generation of realistic channel and delay coefficients in a multipath environment for dual mobile space-ground links. We validate the accuracy of the work by analyzing the generated channel and delay coefficient from several aspects, such as received signal power and amplitude, multipath delay distribution, delay spread and Doppler spectrum.
{"title":"Realistic Channel and Delay Coefficient Generation for Dual Mobile Space-Ground Links: A Tutorial","authors":"Hongzhao Zheng;Mohamed Atia;Halim Yanikomeroglu","doi":"10.1109/OJVT.2024.3399072","DOIUrl":"10.1109/OJVT.2024.3399072","url":null,"abstract":"Channel and delay coefficient are two essential parameters for the characterization of a multipath propagation environment. It is crucial to generate realistic channel and delay coefficient in order to study the channel characteristics that involves signals propagating through environments with severe multipath effects. While many deterministic channel models, such as ray-tracing (RT), face challenges like high computational complexity, data requirements for geometrical information, and inapplicability for space-ground links, and nongeometry-based stochastic channel models (NGSCMs) might lack spatial consistency and offer lower accuracy, we present a scalable tutorial for the channel modeling of dual mobile space-ground links in urban areas, utilizing the Quasi Deterministic Radio Channel Generator (QuaDRiGa), which adopts a geometry-based stochastic channel model (GSCM), in conjunction with an International Telecommunication Union (ITU) provided state duration model. This tutorial allows for the generation of realistic channel and delay coefficients in a multipath environment for dual mobile space-ground links. We validate the accuracy of the work by analyzing the generated channel and delay coefficient from several aspects, such as received signal power and amplitude, multipath delay distribution, delay spread and Doppler spectrum.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"762-777"},"PeriodicalIF":6.4,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10526419","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141004387","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 : 2024-03-08DOI: 10.1109/OJVT.2024.3398566
Shota Mori;Keiichi Mizutani;Hiroshi Harada
Improving spectral efficiency is an important issue for the next generation of the 5th generation mobile communication (5G) systems. Full-duplex cellular (FDC) and dynamic-FDC (DDC) systems based on the 5G signal format (5G-FDC and 5G-DDC) have gained substantial attention for introducing in-band full-duplex (IBFD) into 5G. However, self-interference (SI) at a base station (BS) and inter-user interference (IUI) in user equipment (UE) are significant hurdles in implementing FDC and DDC systems. This study proposes an IUI cancellation (IUIC) scheme based on successive interference cancellation tailored to the signal configuration and channel coding of 5G. Additionally, we introduce user scheduling and adaptive modulation algorithms for 5G-DDC. We evaluate the proposed schemes using link- and system-level simulations. The results demonstrate a remarkable 40 dB reduction in IUI with a 3.4 dB decline in reception quality. Furthermore, our IUIC method reduces the IUI of close-distance UE pairs, expands the candidate UE pairs for IBFD operation, and significantly enhances the IBFD application ratio in the downlink slot by 51.0% compared to conventional 5G-DDC. Moreover, the gain of the uplink average throughput increases by 11.4% when the BS and UE transmission powers are at their maximum.
{"title":"Inter-User Interference Cancellation Scheme for 5G-Based Dynamic Full-Duplex Cellular System","authors":"Shota Mori;Keiichi Mizutani;Hiroshi Harada","doi":"10.1109/OJVT.2024.3398566","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3398566","url":null,"abstract":"Improving spectral efficiency is an important issue for the next generation of the 5th generation mobile communication (5G) systems. Full-duplex cellular (FDC) and dynamic-FDC (DDC) systems based on the 5G signal format (5G-FDC and 5G-DDC) have gained substantial attention for introducing in-band full-duplex (IBFD) into 5G. However, self-interference (SI) at a base station (BS) and inter-user interference (IUI) in user equipment (UE) are significant hurdles in implementing FDC and DDC systems. This study proposes an IUI cancellation (IUIC) scheme based on successive interference cancellation tailored to the signal configuration and channel coding of 5G. Additionally, we introduce user scheduling and adaptive modulation algorithms for 5G-DDC. We evaluate the proposed schemes using link- and system-level simulations. The results demonstrate a remarkable 40 dB reduction in IUI with a 3.4 dB decline in reception quality. Furthermore, our IUIC method reduces the IUI of close-distance UE pairs, expands the candidate UE pairs for IBFD operation, and significantly enhances the IBFD application ratio in the downlink slot by 51.0% compared to conventional 5G-DDC. Moreover, the gain of the uplink average throughput increases by 11.4% when the BS and UE transmission powers are at their maximum.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"704-720"},"PeriodicalIF":6.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141182060","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 : 2024-03-05DOI: 10.1109/OJVT.2024.3373721
Yazid M. Khattabi;Yazan H. Al-Badarneh;Mohamed-Slim Alouini
This article considers an interference-based radio-frequency energy harvesting (RF-EH)-empowered wireless dual-hop amplify-and-forward relaying system in which an ambient interferer is beneficially utilized as the solely free power source for EH and detrimentally considered as the dominant factor that corrupts its receivers. Three EH modes are considered and analyzed separately. In mode I, energy is harvested only by the source; in mode II, energy is harvested only by the relay; and in mode III, energy is harvested concurrently by both the source and relay. Under these modes, exact and approximate analytical expressions are derived for the system's outage probability, which are directly used to determine the system's delay-limited throughput as a performance figure of merit. Thorough numerical and simulation results are presented to verify the analytical work and to demonstrate the system's throughput performance under different system and channel parameters. For example, results reveal that for given channel conditions, increasing the interferer's power reduces the throughput in case of modes I and II, and has no effect on it in case of mode III. Also, for given interferer's power, improving the channel conditions between the interferer and a harvesting node, improves the throughput, while improving them between the interferer and a receiving node, degrades the throughput.
本文探讨了一种基于干扰的射频能量收集（RF-EH）供电无线双跳放大和前向中继系统，在该系统中，环境干扰器被用作 EH 的唯一免费电源，并被视为干扰其接收器的不利因素。本文考虑并分别分析了三种 EH 模式。在模式 I 中，能量仅由信号源采集；在模式 II 中，能量仅由中继器采集；在模式 III 中，能量由信号源和中继器同时采集。在这些模式下，得出了系统中断概率的精确和近似分析表达式，这些表达式可直接用于确定系统的延迟限制吞吐量，作为性能参数。为了验证分析结果，并证明系统在不同系统和信道参数下的吞吐量性能，我们给出了详尽的数值和仿真结果。例如，结果显示，在给定信道条件下，增加干扰功率会降低模式 I 和模式 II 的吞吐量，而对模式 III 则没有影响。此外，在给定干扰功率的情况下，改善干扰器与收获节点之间的信道条件可提高吞吐量，而改善干扰器与接收节点之间的信道条件则会降低吞吐量。
{"title":"On the Performance of Interference-Based Energy-Harvesting-Enabled Wireless AF Relaying Communication Systems","authors":"Yazid M. Khattabi;Yazan H. Al-Badarneh;Mohamed-Slim Alouini","doi":"10.1109/OJVT.2024.3373721","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3373721","url":null,"abstract":"This article considers an interference-based radio-frequency energy harvesting (RF-EH)-empowered wireless dual-hop amplify-and-forward relaying system in which an ambient interferer is beneficially utilized as the solely free power source for EH and detrimentally considered as the dominant factor that corrupts its receivers. Three EH modes are considered and analyzed separately. In mode I, energy is harvested only by the source; in mode II, energy is harvested only by the relay; and in mode III, energy is harvested concurrently by both the source and relay. Under these modes, exact and approximate analytical expressions are derived for the system's outage probability, which are directly used to determine the system's delay-limited throughput as a performance figure of merit. Thorough numerical and simulation results are presented to verify the analytical work and to demonstrate the system's throughput performance under different system and channel parameters. For example, results reveal that for given channel conditions, increasing the interferer's power reduces the throughput in case of modes I and II, and has no effect on it in case of mode III. Also, for given interferer's power, improving the channel conditions between the interferer and a harvesting node, improves the throughput, while improving them between the interferer and a receiving node, degrades the throughput.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"440-458"},"PeriodicalIF":6.4,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321718","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 : 2024-03-03DOI: 10.1109/OJVT.2024.3396637
Olivia Nakayima;Mostafa I. Soliman;Kazunori Ueda;Samir A. Elsagheer Mohamed
Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.
{"title":"Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks","authors":"Olivia Nakayima;Mostafa I. Soliman;Kazunori Ueda;Samir A. Elsagheer Mohamed","doi":"10.1109/OJVT.2024.3396637","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3396637","url":null,"abstract":"Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"721-736"},"PeriodicalIF":6.4,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10518068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286700","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}
Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.
{"title":"Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges","authors":"Ruhul Amin Khalil;Ziad Safelnasr;Naod Yemane;Mebruk Kedir;Atawulrahman Shafiqurrahman;NASIR SAEED","doi":"10.1109/OJVT.2024.3369691","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3369691","url":null,"abstract":"Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"397-427"},"PeriodicalIF":6.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10444919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291185","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}
Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency, and energy conservation through the communication of vehicles with their surrounding entities such as other vehicles, pedestrians, roadside infrastructure, and networks. Among these, traffic signal control (TSC) plays a significant role in roadside infrastructure for V2X. However, most existing works on TSC design assume that real-time traffic flow information is accessible, which does not hold in real-world deployment. This study proposes a two-stage framework to address this issue. In the first stage, a scene prediction module and a scene context encoder are utilized to process historical and current traffic data to generate preliminary traffic signal actions. In the second stage, an action refinement module, informed by human-defined traffic rules and real-time traffic metrics, adjusts the preliminary actions to account for the latency in observations. This modular design allows device deployment with varying computational resources while facilitating system customization, ensuring both adaptability and scalability, particularly in edge-computing environments. Through extensive simulations on the SUMO platform, the proposed framework demonstrates robustness and superior performance in diverse traffic scenarios under varying communication delays. The related code is available at https://github.com/Traffic-Alpha/TSC-DelayLight .
{"title":"Scalable Reinforcement Learning Framework for Traffic Signal Control Under Communication Delays","authors":"Aoyu Pang;Maonan Wang;Yirong Chen;Man-On Pun;Michael Lepech","doi":"10.1109/OJVT.2024.3368693","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3368693","url":null,"abstract":"Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency, and energy conservation through the communication of vehicles with their surrounding entities such as other vehicles, pedestrians, roadside infrastructure, and networks. Among these, traffic signal control (TSC) plays a significant role in roadside infrastructure for V2X. However, most existing works on TSC design assume that real-time traffic flow information is accessible, which does not hold in real-world deployment. This study proposes a two-stage framework to address this issue. In the first stage, a scene prediction module and a scene context encoder are utilized to process historical and current traffic data to generate preliminary traffic signal actions. In the second stage, an action refinement module, informed by human-defined traffic rules and real-time traffic metrics, adjusts the preliminary actions to account for the latency in observations. This modular design allows device deployment with varying computational resources while facilitating system customization, ensuring both adaptability and scalability, particularly in edge-computing environments. Through extensive simulations on the SUMO platform, the proposed framework demonstrates robustness and superior performance in diverse traffic scenarios under varying communication delays. The related code is available at \u0000<uri>https://github.com/Traffic-Alpha/TSC-DelayLight</uri>\u0000.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"330-343"},"PeriodicalIF":6.4,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123338","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 : 2024-02-21DOI: 10.1109/OJVT.2024.3368240
Annu;P. Rajalakshmi
The advent of 6G marks a transformative phase in wireless communication, ushering in a hyperconnected experience. This paper explores optimizing sidelink technologies in Vehicle-to-Everything (V2X) communication through integrating 6G capabilities. Emphasizing challenges in sidelink resource allocation, the study introduces mathematical solutions. The survey further investigates the evolution of Cellular-V2X (C-V2X) and sidelink standardization within the 6G V2X Sidelink context, highlighting key features and applications. Additionally, it examines inter-UE coordination, resource re-evaluation, and pre-emption operations in 5G-V2X Sidelink, addressing associated challenges and mathematical formulations. The paper focuses on power-based resource allocation in 5G-V2X, addressing challenges and proposing solutions for the 6G V2X Sidelink landscape. Encompassing direct communication, collision issues, spectrum compliance, resource fairness, uncertainty, and interference management, the survey comprehensively explores challenges and solutions in current sidelink resource allocation. It evaluates traditional and emerging techniques, such as cognitive radio, cooperative communication, power control, dynamic spectrum access, ML-aided allocation, blockchain-enabled allocation, and edge computing-driven allocation. The resource allocation requirements for diverse V2X services in 6G V2X Sidelink are outlined, explicitly focusing on Vehicular to Vehicular (V2V), Vehicular to Infrastructure (V2I), Vehicular to Pedestrian (V2P), and other V2X services, addressing their specific needs.
{"title":"Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions","authors":"Annu;P. Rajalakshmi","doi":"10.1109/OJVT.2024.3368240","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3368240","url":null,"abstract":"The advent of 6G marks a transformative phase in wireless communication, ushering in a hyperconnected experience. This paper explores optimizing sidelink technologies in Vehicle-to-Everything (V2X) communication through integrating 6G capabilities. Emphasizing challenges in sidelink resource allocation, the study introduces mathematical solutions. The survey further investigates the evolution of Cellular-V2X (C-V2X) and sidelink standardization within the 6G V2X Sidelink context, highlighting key features and applications. Additionally, it examines inter-UE coordination, resource re-evaluation, and pre-emption operations in 5G-V2X Sidelink, addressing associated challenges and mathematical formulations. The paper focuses on power-based resource allocation in 5G-V2X, addressing challenges and proposing solutions for the 6G V2X Sidelink landscape. Encompassing direct communication, collision issues, spectrum compliance, resource fairness, uncertainty, and interference management, the survey comprehensively explores challenges and solutions in current sidelink resource allocation. It evaluates traditional and emerging techniques, such as cognitive radio, cooperative communication, power control, dynamic spectrum access, ML-aided allocation, blockchain-enabled allocation, and edge computing-driven allocation. The resource allocation requirements for diverse V2X services in 6G V2X Sidelink are outlined, explicitly focusing on Vehicular to Vehicular (V2V), Vehicular to Infrastructure (V2I), Vehicular to Pedestrian (V2P), and other V2X services, addressing their specific needs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"344-383"},"PeriodicalIF":6.4,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161136","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 : 2024-02-16DOI: 10.1109/OJVT.2024.3366772
Hugo Hawkins;Chao Xu;Lie-Liang Yang;Lajos Hanzo
Index Modulated Orthogonal Frequency-Division Multiplexing (IM-OFDM) based Integrated Sensing and Communication (ISAC) is potentially capable of outperforming Orthogonal Frequency-Division Multiplexing (OFDM) ISAC, since Index Modulation (IM) concentrates increased power on the activated subcarriers. This has been confirmed by authoritative publications for the IM-OFDM communication component. However, no evidence is found in the open literature that IM-OFDM sensing is capable of outperforming OFDM sensing, because the blank subcarriers impair the system's sensing functionality. The existing solutions either insert a radar signal into the deactivated subcarriers, thereby using a radar signal for sensing, or employ compressed sensing, which leads to a lower sensing performance than OFDM ISAC. Hence, a novel low complexity algorithm is proposed for ensuring that an IM-OFDM ISAC system outperforms its OFDM ISAC counterpart for both communication and sensing. The algorithm collects observations of the received signal to “fill in” the blank subcarriers in the sensing data created by IM-OFDM, whilst taking advantage of the increased subcarrier power attained by activating fewer subcarriers. This occurs over multiple transmit frames, which inevitably delays the target estimation. As OFDM sensing assumes low target velocities, this delay is shown to have a negligible impact on the sensing performance of IM-OFDM. The simulation results show that IM-OFDM ISAC is indeed capable of outperforming its OFDM ISAC counterpart for both sensing and communication. The impact of block interleaving and of the modulation type on the sensing performance is also discussed.
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Pub Date : 2024-02-14DOI: 10.1109/OJVT.2024.3358319
{"title":"IEEE Open Journal of Vehicular Technology Information for Authors","authors":"","doi":"10.1109/OJVT.2024.3358319","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3358319","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"C4-C4"},"PeriodicalIF":6.4,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10436553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738994","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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