This paper introduces a preamble arbitration rule and interference suppression (PARIS) method for ultra-wideband (UWB) in-vehicle networks. Advancements in the automotive technology have led to increased reliance on wire harnesses, resulting in higher costs, electronic integration challenges, and adverse environmental effects. To address these problems, we explored the use of UWB wireless networks, which are characterized by low transmission power and superior signal penetration capabilities. A significant challenge associated with implementing UWB in automotive environments is the increased frame error rate (FER) caused by UWB interference. Our experiments indicate that vehicles equipped with identical UWB networks exhibit an FER of approximately 6% when positioned closely. This level of FER is problematic for automotive applications, where reliable communication is paramount. To mitigate this problem, we developed an PARIS communication algorithm that is robust against interference. As identified in this study, PARIS leverages two key characteristics of UWB. First, it prioritizes the timing of signal reception over radio signal power, enhancing interference suppression by activating the receiver at the optimal moment before the desired frame arrives, thereby minimizing data loss. Second, the algorithm exploits the hierarchical nature of preamble codes in simultaneously received frames, reducing data loss rate to the order of $10^{-5}$ by prioritizing frames from critical communication devices based on the preamble code hierarchy. Implementing the UWB-based PARIS method in wireless vehicle networks can reduce the weight of the wire harnesses by approximately 20%, offering a promising solution to the challenges posed by traditional wiring systems.
本文介绍了一种用于超宽带（UWB）车载网络的前导码仲裁规则和干扰抑制（PARIS）方法。汽车技术的进步使人们越来越依赖线束，导致成本上升、电子集成难题和不利的环境影响。为了解决这些问题，我们探索使用 UWB 无线网络，其特点是传输功率低、信号穿透能力强。在汽车环境中实施 UWB 的一个重大挑战是 UWB 干扰导致的帧误码率（FER）增加。我们的实验表明，配备相同 UWB 网络的车辆在紧密定位时，FER 约为 6%。对于通信可靠性至关重要的汽车应用来说，这种水平的 FER 是个问题。为了缓解这一问题，我们开发了一种具有抗干扰能力的 PARIS 通信算法。正如本研究中所确定的，PARIS 利用了 UWB 的两个关键特性。首先，它优先考虑信号接收时间而不是无线电信号功率，通过在所需帧到达前的最佳时机启动接收器来增强干扰抑制能力，从而最大限度地减少数据丢失。其次，该算法利用了同时接收的帧中前导码的层次性，通过根据前导码层次对来自关键通信设备的帧进行优先级排序，将数据丢失率降低到 10^{-5}$ 的数量级。在无线车载网络中实施基于 UWB 的 PARIS 方法，可将线束重量减轻约 20%，为应对传统布线系统带来的挑战提供了一个前景广阔的解决方案。
{"title":"Preamble Arbitration Rule and Interference Suppression-Based Polling Medium Access Control for In-Vehicle Ultra-Wideband Networks","authors":"Makoto Okuhara;Nobuyuki Kurioka;Shigeki Mitoh;Patrick Finnerty;Chikara Ohta","doi":"10.1109/OJVT.2024.3474430","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3474430","url":null,"abstract":"This paper introduces a preamble arbitration rule and interference suppression (PARIS) method for ultra-wideband (UWB) in-vehicle networks. Advancements in the automotive technology have led to increased reliance on wire harnesses, resulting in higher costs, electronic integration challenges, and adverse environmental effects. To address these problems, we explored the use of UWB wireless networks, which are characterized by low transmission power and superior signal penetration capabilities. A significant challenge associated with implementing UWB in automotive environments is the increased frame error rate (FER) caused by UWB interference. Our experiments indicate that vehicles equipped with identical UWB networks exhibit an FER of approximately 6% when positioned closely. This level of FER is problematic for automotive applications, where reliable communication is paramount. To mitigate this problem, we developed an PARIS communication algorithm that is robust against interference. As identified in this study, PARIS leverages two key characteristics of UWB. First, it prioritizes the timing of signal reception over radio signal power, enhancing interference suppression by activating the receiver at the optimal moment before the desired frame arrives, thereby minimizing data loss. Second, the algorithm exploits the hierarchical nature of preamble codes in simultaneously received frames, reducing data loss rate to the order of \u0000<inline-formula><tex-math>$10^{-5}$</tex-math></inline-formula>\u0000 by prioritizing frames from critical communication devices based on the preamble code hierarchy. Implementing the UWB-based PARIS method in wireless vehicle networks can reduce the weight of the wire harnesses by approximately 20%, offering a promising solution to the challenges posed by traditional wiring systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1518-1531"},"PeriodicalIF":5.3,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524212","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 : 2024-09-30DOI: 10.1109/OJVT.2024.3471252
Xianhao Lu;Longjun Wang
With the increasing integration of renewable energy into power grids, ensuring the stability and reliability of power grids has become crucial. The intermittency of renewable energy poses a challenge for the frequency and voltage control of power grids. As an adjustable flexible load, electric vehicles (EVs) have emerged as an important solution for grid frequency and voltage control. A joint control and optimization strategy for electric vehicle aggregators (EVAs) to participate in grid frequency and voltage regulation based on a cloud-edge collaborative hierarchical scheduling architecture is proposed, and a multi-timescale EV charging pile cluster (EVC) scheduling model is established with the goal of maximizing the EVA profit. The strategy and model are grounded in the ancillary service market process. The EVA forecasts and optimizes to declare the active and reactive power capacities of the EVC to the market before the day and hour and controls the EVC to respond quickly and accurately to the frequency and voltage regulation instructions in the real-time stage. The methods of rolling optimization, model predictive control, evaluation of the feasible energy region and real-time capacity correction are adopted to coordinate the active and reactive power of EVC. The feasibility and effectiveness of the strategy are verified by an example, which provides an important reference for EVAs participating in power grid interactions.
随着可再生能源越来越多地并入电网，确保电网的稳定性和可靠性变得至关重要。可再生能源的间歇性给电网的频率和电压控制带来了挑战。作为一种可调节的灵活负载，电动汽车（EV）已成为电网频率和电压控制的重要解决方案。本文提出了一种基于云边协作分层调度架构的电动汽车聚合器（EVA）参与电网频率和电压调节的联合控制和优化策略，并以电动汽车聚合器利润最大化为目标，建立了一个多时间尺度的电动汽车充电桩集群（EVC）调度模型。该策略和模型以辅助服务市场流程为基础。EVA 通过预测和优化，在日、小时前向市场申报 EVC 的有功和无功功率容量，并在实时阶段控制 EVC 快速、准确地响应频率和电压调节指令。采用滚动优化、模型预测控制、可行能量区域评估和实时容量修正等方法来协调 EVC 的有功和无功功率。通过实例验证了该策略的可行性和有效性，为参与电网互动的 EVA 提供了重要参考。
{"title":"Cloud-Edge Collaboration Control Strategy for Electric Vehicle Aggregators Participating in Frequency and Voltage Regulation","authors":"Xianhao Lu;Longjun Wang","doi":"10.1109/OJVT.2024.3471252","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3471252","url":null,"abstract":"With the increasing integration of renewable energy into power grids, ensuring the stability and reliability of power grids has become crucial. The intermittency of renewable energy poses a challenge for the frequency and voltage control of power grids. As an adjustable flexible load, electric vehicles (EVs) have emerged as an important solution for grid frequency and voltage control. A joint control and optimization strategy for electric vehicle aggregators (EVAs) to participate in grid frequency and voltage regulation based on a cloud-edge collaborative hierarchical scheduling architecture is proposed, and a multi-timescale EV charging pile cluster (EVC) scheduling model is established with the goal of maximizing the EVA profit. The strategy and model are grounded in the ancillary service market process. The EVA forecasts and optimizes to declare the active and reactive power capacities of the EVC to the market before the day and hour and controls the EVC to respond quickly and accurately to the frequency and voltage regulation instructions in the real-time stage. The methods of rolling optimization, model predictive control, evaluation of the feasible energy region and real-time capacity correction are adopted to coordinate the active and reactive power of EVC. The feasibility and effectiveness of the strategy are verified by an example, which provides an important reference for EVAs participating in power grid interactions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1532-1544"},"PeriodicalIF":5.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524183","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}
An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.
{"title":"A Physics-Informed Cold-Start Capability for xEV Charging Recommender System","authors":"Raik Orbay;Aditya Pratap Singh;Johannes Emilsson;Michele Becciani;Evelina Wikner;Victor Gustafson;Torbjörn Thiringer","doi":"10.1109/OJVT.2024.3469577","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3469577","url":null,"abstract":"An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1457-1469"},"PeriodicalIF":5.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10697286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450898","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 : 2024-09-25DOI: 10.1109/OJVT.2024.3468164
Mohammed Mujib Alshahrani
The Social Internet of Vehicles (SIoV) connects cars that are nearby and uses different types of infrastructure to connect people with shared interests. A public, open tool, such as the cloud, is used to share information about things like tolls, traffic, weather, and more. When people share social information, the risks of data leaks and trustworthiness are still not dealt with. This article presents a Verifiable Discrete Trust Model (VDTM) that uses Congruent Federated Learning (CFL) to make social information-sharing tools more trustworthy. The proposed trust model ensures pre- and post-sharing trust verification of the communicating vehicles. Trust is verified as a global identity factor due to the inconsistency between sharing occasions. The CFL is accountable of checking forward and backward trust between the times before and after sharing. In this learning, the congruency is zero-variance detection on both occasions of information sharing. The learning does this check over and over to make sure there is discrete trust in information-sharing times between vehicles, between vehicles and infrastructure, or between vehicles and platforms. The identified trust is valid within the specific interval broadcasted during request initializations. Depending on the trust level, the sharing interval is authenticated using forward and reverse private keys. Therefore, the vehicle's trust results from the maximum information integrity observed in the pre-and post-sharing interval. For the maximum vehicles considered, the proposed model leverages the trust index by 8%, information sharing by 7.15%, and reducing key overhead by 9.35% and time consumption by 7.76%.
{"title":"A Verifiable Discrete Trust Model (VDTM) Using Congruent Federated Learning (CFL) for Social Internet of Vehicles","authors":"Mohammed Mujib Alshahrani","doi":"10.1109/OJVT.2024.3468164","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3468164","url":null,"abstract":"The Social Internet of Vehicles (SIoV) connects cars that are nearby and uses different types of infrastructure to connect people with shared interests. A public, open tool, such as the cloud, is used to share information about things like tolls, traffic, weather, and more. When people share social information, the risks of data leaks and trustworthiness are still not dealt with. This article presents a Verifiable Discrete Trust Model (VDTM) that uses Congruent Federated Learning (CFL) to make social information-sharing tools more trustworthy. The proposed trust model ensures pre- and post-sharing trust verification of the communicating vehicles. Trust is verified as a global identity factor due to the inconsistency between sharing occasions. The CFL is accountable of checking forward and backward trust between the times before and after sharing. In this learning, the congruency is zero-variance detection on both occasions of information sharing. The learning does this check over and over to make sure there is discrete trust in information-sharing times between vehicles, between vehicles and infrastructure, or between vehicles and platforms. The identified trust is valid within the specific interval broadcasted during request initializations. Depending on the trust level, the sharing interval is authenticated using forward and reverse private keys. Therefore, the vehicle's trust results from the maximum information integrity observed in the pre-and post-sharing interval. For the maximum vehicles considered, the proposed model leverages the trust index by 8%, information sharing by 7.15%, and reducing key overhead by 9.35% and time consumption by 7.76%.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1441-1456"},"PeriodicalIF":5.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442990","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 : 2024-09-24DOI: 10.1109/OJVT.2024.3466858
Sree Krishna Das;Ratna Mudi;Md. Siddikur Rahman;Khaled M. Rabie;Xingwang Li
The increasing popularity of Internet of Things (IoT)-based wireless services highlights the urgent need to upgrade fifth-generation (5G) wireless networks and beyond to accommodate these services. Although 5G networks currently support a variety of wireless services, they might not fully meet the high computational and communication resource demands of new applications. Issues such as latency, energy consumption, network congestion, signaling overhead, and potential privacy breaches contribute to this limitation. Machine learning (ML) frequently offers solutions to these problems. As a result, sixth-generation (6G) wireless technologies are being developed to address the deficiencies of 5G networks. Traditional ML methods are generally centralized. However, the vast amount of wireless data generated, growing privacy concerns, and the increasing computational capabilities of edge devices have led to a shift towards optimizing system performance in a distributed manner. This paper provides a thorough analysis of distributed learning techniques, including federated learning (FL), multi-agent reinforcement learning (MARL), and the multi-agent federated reinforcement learning (FRL) framework. It explains how these techniques can be effectively and efficiently implemented in wireless networks. These methods offer potential solutions to the challenges faced by current wireless networks, promising to create a more robust, capable, and versatile network that meets the growing demands of IoT and other emerging applications. Implementing the FRL framework can significantly improve the learning efficiency of wireless networks. To tackle the challenges posed by rapidly changing radio channels, we propose a robust FRL framework that enables local users to perform distributed power allocation, bandwidth allocation, interference mitigation, and communication mode selection. Finally, the paper outlines several future research directions aimed at effectively integrating the FRL framework into wireless networks.
{"title":"Federated Reinforcement Learning for Wireless Networks: Fundamentals, Challenges and Future Research Trends","authors":"Sree Krishna Das;Ratna Mudi;Md. Siddikur Rahman;Khaled M. Rabie;Xingwang Li","doi":"10.1109/OJVT.2024.3466858","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3466858","url":null,"abstract":"The increasing popularity of Internet of Things (IoT)-based wireless services highlights the urgent need to upgrade fifth-generation (5G) wireless networks and beyond to accommodate these services. Although 5G networks currently support a variety of wireless services, they might not fully meet the high computational and communication resource demands of new applications. Issues such as latency, energy consumption, network congestion, signaling overhead, and potential privacy breaches contribute to this limitation. Machine learning (ML) frequently offers solutions to these problems. As a result, sixth-generation (6G) wireless technologies are being developed to address the deficiencies of 5G networks. Traditional ML methods are generally centralized. However, the vast amount of wireless data generated, growing privacy concerns, and the increasing computational capabilities of edge devices have led to a shift towards optimizing system performance in a distributed manner. This paper provides a thorough analysis of distributed learning techniques, including federated learning (FL), multi-agent reinforcement learning (MARL), and the multi-agent federated reinforcement learning (FRL) framework. It explains how these techniques can be effectively and efficiently implemented in wireless networks. These methods offer potential solutions to the challenges faced by current wireless networks, promising to create a more robust, capable, and versatile network that meets the growing demands of IoT and other emerging applications. Implementing the FRL framework can significantly improve the learning efficiency of wireless networks. To tackle the challenges posed by rapidly changing radio channels, we propose a robust FRL framework that enables local users to perform distributed power allocation, bandwidth allocation, interference mitigation, and communication mode selection. Finally, the paper outlines several future research directions aimed at effectively integrating the FRL framework into wireless networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1400-1440"},"PeriodicalIF":5.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10691666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450916","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}
IEEE 802.15.4 smart utility network (SUN) frequency-shift keying (FSK) has attracted considerable attention as a wireless communication standard designed for use in essential applications required by Internet of Things (IoT) systems. However, longer transmission distances in highly mobile environments are required to support various applications in next-generation IoT systems, such as vehicle-to-everything, automated driving, and drone control systems. Although research on wide-area, highly mobile communications has been conducted via computer simulations, an experimental evaluation platform for further research has not been developed. In this study, we developed an experimental evaluation platform for SUN FSK in very high frequency bands. The developed platform comprises a signal generator-based transmitter and a software-defined radio-based receiver. It was proven to be capable of transmitting a power of ≥5 W through a power amplifier and was suitable for laboratory and field experiments. In addition, we developed received signal processing methods, including a packet detection method and a channel estimation method, which were designed to achieve wide-area, highly mobile communication. In laboratory experiments, the packet error rate characteristics required by IEEE 802.15.4 were achieved even at a transmission distance of >10 km at vehicular speeds of several tens of km/h.
IEEE 802.15.4 智能公用事业网络（SUN）频移键控（FSK）作为物联网（IoT）系统所需的基本应用而设计的无线通信标准引起了广泛关注。然而，要支持下一代物联网系统中的各种应用，如车对物、自动驾驶和无人机控制系统，就需要在高度移动的环境中实现更远的传输距离。虽然有关广域高移动通信的研究已通过计算机模拟进行，但用于进一步研究的实验评估平台尚未开发出来。在本研究中，我们开发了一个用于超高频段 SUN FSK 的实验评估平台。开发的平台包括一个基于信号发生器的发射器和一个基于软件定义无线电的接收器。实验证明，该平台能够通过功率放大器发射功率≥5 W 的信号，适用于实验室和现场实验。此外，我们还开发了接收信号处理方法，包括数据包检测方法和信道估计方法，旨在实现广域高移动通信。在实验室实验中，即使在传输距离大于 10 千米、车速为几十千米/小时的情况下，也能达到 IEEE 802.15.4 所要求的数据包错误率特性。
{"title":"Software-Defined Radio-Based IEEE 802.15.4 SUN FSK Evaluation Platform for Highly Mobile Environments","authors":"Jaeseok Lim;Keito Nakura;Shota Mori;Hiroshi Harada","doi":"10.1109/OJVT.2024.3464349","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3464349","url":null,"abstract":"IEEE 802.15.4 smart utility network (SUN) frequency-shift keying (FSK) has attracted considerable attention as a wireless communication standard designed for use in essential applications required by Internet of Things (IoT) systems. However, longer transmission distances in highly mobile environments are required to support various applications in next-generation IoT systems, such as vehicle-to-everything, automated driving, and drone control systems. Although research on wide-area, highly mobile communications has been conducted via computer simulations, an experimental evaluation platform for further research has not been developed. In this study, we developed an experimental evaluation platform for SUN FSK in very high frequency bands. The developed platform comprises a signal generator-based transmitter and a software-defined radio-based receiver. It was proven to be capable of transmitting a power of ≥5 W through a power amplifier and was suitable for laboratory and field experiments. In addition, we developed received signal processing methods, including a packet detection method and a channel estimation method, which were designed to achieve wide-area, highly mobile communication. In laboratory experiments, the packet error rate characteristics required by IEEE 802.15.4 were achieved even at a transmission distance of >10 km at vehicular speeds of several tens of km/h.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1637-1649"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579187","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 : 2024-09-17DOI: 10.1109/OJVT.2024.3462599
Yuki Kuraya;Hideki Ochiai
We propose a new physical layer security scheme for a wiretap channel in polar-coded OFDM-based wireless communication systems. Our approach is based on the adaptive bit channel selection , where the input bit channels of polar code are selected according to the frequency selectivity of the main channel. Specifically, the polar code is constructed by the legitimate receiver based on its observed channel state information (CSI), and the receiver informs the transmitter of the resulting code structure. Since the proposed scheme attempts to improve the block error rate (BLER) performance exclusively for the main channel, it provides a significant performance gain over the wiretap channel, as long as the channel of the eavesdropper is not highly correlated with that of the legitimate receiver. On the assumption that the wiretap channel is uncorrelated with the main channel, simulation results demonstrate that the main channel can achieve significant performance gains over the wiretap channel, even under the worst-case scenario where the selected polar code structure (i.e., a set of the bit channels selected by the legitimate receiver for information transmission) is completely known to the eavesdropper. We also consider the case where the main channel and wiretap channel are correlated and reveal that our approach is effective even in the presence of mild channel correlation. Finally, the effect of the channel estimation error on the resulting BLER is also examined, pointing out the importance of accurate CSI acquisition at the receiver side.
{"title":"A New Physical Layer Security Scheme Based on Adaptive Bit Channel Selection for Polar-Coded OFDM","authors":"Yuki Kuraya;Hideki Ochiai","doi":"10.1109/OJVT.2024.3462599","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3462599","url":null,"abstract":"We propose a new physical layer security scheme for a wiretap channel in polar-coded OFDM-based wireless communication systems. Our approach is based on the \u0000<italic>adaptive bit channel selection</i>\u0000, where the input bit channels of polar code are selected according to the frequency selectivity of the main channel. Specifically, the polar code is constructed by the legitimate receiver based on its observed channel state information (CSI), and the receiver informs the transmitter of the resulting code structure. Since the proposed scheme attempts to improve the block error rate (BLER) performance exclusively for the main channel, it provides a significant performance gain over the wiretap channel, as long as the channel of the eavesdropper is not highly correlated with that of the legitimate receiver. On the assumption that the wiretap channel is uncorrelated with the main channel, simulation results demonstrate that the main channel can achieve significant performance gains over the wiretap channel, even under the worst-case scenario where the selected polar code structure (i.e., a set of the bit channels selected by the legitimate receiver for information transmission) is completely known to the eavesdropper. We also consider the case where the main channel and wiretap channel are correlated and reveal that our approach is effective even in the presence of mild channel correlation. Finally, the effect of the channel estimation error on the resulting BLER is also examined, pointing out the importance of accurate CSI acquisition at the receiver side.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1336-1347"},"PeriodicalIF":5.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376538","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 : 2024-09-13DOI: 10.1109/OJVT.2024.3460979
Varvara V. Elesina;Carla E. Reinhardt;Lennart Thielecke;Tobias Doeker;Thomas Kürner
This paper present an initial approach to the analysis of the stationarity of time-variant channels in industrial environments, focusing on three distinct scenarios: 1) communication between a static access point (AP) and a sensor node (SN) mounted on a moving machine within a comprehensive industrial workspace, 2) communication between two static sensor node (SN) with a moving metal plate object between them, and 3) communication between two static robotic manipulators with a moving obstacle with varying movement speeds between them. The assumptions of the wide-sense stationary (WSS) and uncorrelated scatering (US), fundamental to channel modeling, are examined using local scattering function (LSF) collinearity metrics in both time and frequency domains. In blockage scenarios, where we compared the effects of two different types of obstacles – a metal plate and a robotic arm – the channel behavior can be divided into three distinct regions: fully stationary before and after the blockage, non-stationary during the transition periods, and either conditionally stationary or fully non-stationary during partial or full blockage, respectively. These distinctions were influenced by the type of blockage object and whether the scenario involved non-line-of-sight (NLOS) or obstructed-line-of-sight (OLOS) conditions. Notably, the speed of moving obstacles affects the duration and nature of non-stationary regions, with higher speeds leading to shorter and less distinct transition periods. The US assumption was found to be generally valid in the blockage scenarios but not in the AP scenario.
本文提出了一种分析工业环境中时变信道静止性的初步方法，重点关注三种不同的场景：1) 在一个综合工业工作区内，一个静态接入点（AP）和一个安装在移动机器上的传感器节点（SN）之间的通信；2) 两个静态传感器节点（SN）之间的通信，它们之间有一个移动的金属板物体；3) 两个静态机器人机械手之间的通信，它们之间有一个移动的障碍物，移动速度各不相同。利用时域和频域的局部散射函数（LSF）共线性指标，对信道建模的基本假设--广义静止（WSS）和非相关散射（US）进行了检验。在阻塞情况下，我们比较了金属板和机械臂这两种不同类型障碍物的影响，信道行为可分为三个不同的区域：阻塞前后完全静止、过渡期间非静止、部分或完全阻塞期间分别为有条件静止或完全非静止。这些区别受到障碍物类型以及情景是否涉及非视线（NLOS）或视线受阻（OLOS）条件的影响。值得注意的是，移动障碍物的速度会影响非稳态区域的持续时间和性质，速度越快，过渡时间越短，越不明显。研究发现，US 假设在阻塞情况下基本有效，但在 AP 情况下无效。
{"title":"Investigating the WSSUS Assumption in 300 GHz Time-Variant Channels in Industrial Environments","authors":"Varvara V. Elesina;Carla E. Reinhardt;Lennart Thielecke;Tobias Doeker;Thomas Kürner","doi":"10.1109/OJVT.2024.3460979","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3460979","url":null,"abstract":"This paper present an initial approach to the analysis of the stationarity of time-variant channels in industrial environments, focusing on three distinct scenarios: 1) communication between a static access point (AP) and a sensor node (SN) mounted on a moving machine within a comprehensive industrial workspace, 2) communication between two static sensor node (SN) with a moving metal plate object between them, and 3) communication between two static robotic manipulators with a moving obstacle with varying movement speeds between them. The assumptions of the wide-sense stationary (WSS) and uncorrelated scatering (US), fundamental to channel modeling, are examined using local scattering function (LSF) collinearity metrics in both time and frequency domains. In blockage scenarios, where we compared the effects of two different types of obstacles – a metal plate and a robotic arm – the channel behavior can be divided into three distinct regions: fully stationary before and after the blockage, non-stationary during the transition periods, and either conditionally stationary or fully non-stationary during partial or full blockage, respectively. These distinctions were influenced by the type of blockage object and whether the scenario involved non-line-of-sight (NLOS) or obstructed-line-of-sight (OLOS) conditions. Notably, the speed of moving obstacles affects the duration and nature of non-stationary regions, with higher speeds leading to shorter and less distinct transition periods. The US assumption was found to be generally valid in the blockage scenarios but not in the AP scenario.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1374-1385"},"PeriodicalIF":5.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408735","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 : 2024-09-10DOI: 10.1109/OJVT.2024.3457499
Mamunur Rashid;Tarek Elfouly;Nan Chen
The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. In the literature, extensive studies have been conducted on model-based and probabilistic EV charging demand forecasting schemes. The studies provide a solid research foundation but result in complicated models with limited scalability. Meanwhile, emerging machine learning techniques bring promising prospects, yet exhibit suboptimal performance with insufficient data. Additionally, existing studies often overlook several critical areas such as overcoming data scarcity, security and privacy concerns, managing the inherent stochasticity of demand data, selecting forecasting methods for a specific feature, and developing standardized performance metrics. Considering the impact of the research topic, EV charging demand forecasting demands careful study. In this paper, we present a comprehensive survey of EV charging demand forecasting, focusing on both probabilistic and learning algorithms. First, we introduce the general procedure of EV charging demand forecasting, encompassing data sources, data pre-processing, and the key EV features. We then provide a taxonomy of existing EV charging demand forecasting techniques, followed by a critical analysis and comparative study of state-of-the-art research. Finally, we discuss open issues, which offer useful insights and future direction for various stakeholders.
{"title":"A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques","authors":"Mamunur Rashid;Tarek Elfouly;Nan Chen","doi":"10.1109/OJVT.2024.3457499","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3457499","url":null,"abstract":"The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. In the literature, extensive studies have been conducted on model-based and probabilistic EV charging demand forecasting schemes. The studies provide a solid research foundation but result in complicated models with limited scalability. Meanwhile, emerging machine learning techniques bring promising prospects, yet exhibit suboptimal performance with insufficient data. Additionally, existing studies often overlook several critical areas such as overcoming data scarcity, security and privacy concerns, managing the inherent stochasticity of demand data, selecting forecasting methods for a specific feature, and developing standardized performance metrics. Considering the impact of the research topic, EV charging demand forecasting demands careful study. In this paper, we present a comprehensive survey of EV charging demand forecasting, focusing on both probabilistic and learning algorithms. First, we introduce the general procedure of EV charging demand forecasting, encompassing data sources, data pre-processing, and the key EV features. We then provide a taxonomy of existing EV charging demand forecasting techniques, followed by a critical analysis and comparative study of state-of-the-art research. Finally, we discuss open issues, which offer useful insights and future direction for various stakeholders.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1348-1373"},"PeriodicalIF":5.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376539","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 : 2024-09-09DOI: 10.1109/OJVT.2024.3456035
Jose Matute;Sergio Diaz;Ali Karimoddini
Achieving robust path tracking is essential for efficiently operating autonomous driving systems, particularly in unpredictable environments. This paper introduces a novel path-tracking control methodology utilizing a variable second-order Sliding Mode Control (SMC) approach. The proposed control strategy addresses the challenges posed by uncertainties and disturbances by reconfiguring and expanding the state-space matrix of a kinematic bicycle model guaranteeing Lyapunov stability and convergence of the system. A state prediction is integrated into the developed SMC to mitigate response time delays. Furthermore, the controller integrates adaptive mechanisms to adjust time-varying parameters within the control formulation based on longitudinal velocity, thereby enhancing path-tracking performance and reducing chattering phenomena. The effectiveness of the proposed approach is comprehensively evaluated through simulations and experiments encompassing challenging driving scenarios characterized by high-curvature paths, varying altitudes, and sensor disturbances, typical in rural driving environments. Results demonstrate that disturbances have varying impacts depending on the type of sensor affected. Real-world tests validate these findings, offering practical insights for automated vehicle path-tracking implementation.
{"title":"Sliding Mode Control for Robust Path Tracking of Automated Vehicles in Rural Environments","authors":"Jose Matute;Sergio Diaz;Ali Karimoddini","doi":"10.1109/OJVT.2024.3456035","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3456035","url":null,"abstract":"Achieving robust path tracking is essential for efficiently operating autonomous driving systems, particularly in unpredictable environments. This paper introduces a novel path-tracking control methodology utilizing a variable second-order Sliding Mode Control (SMC) approach. The proposed control strategy addresses the challenges posed by uncertainties and disturbances by reconfiguring and expanding the state-space matrix of a kinematic bicycle model guaranteeing Lyapunov stability and convergence of the system. A state prediction is integrated into the developed SMC to mitigate response time delays. Furthermore, the controller integrates adaptive mechanisms to adjust time-varying parameters within the control formulation based on longitudinal velocity, thereby enhancing path-tracking performance and reducing chattering phenomena. The effectiveness of the proposed approach is comprehensively evaluated through simulations and experiments encompassing challenging driving scenarios characterized by high-curvature paths, varying altitudes, and sensor disturbances, typical in rural driving environments. Results demonstrate that disturbances have varying impacts depending on the type of sensor affected. Real-world tests validate these findings, offering practical insights for automated vehicle path-tracking implementation.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1314-1325"},"PeriodicalIF":5.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377112","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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