Pub Date : 2023-12-11DOI: 10.1109/mvt.2023.3332580
George C. Alexandropoulos, Nir Shlezinger, Idban Alamzadeh, Mohammadreza F. Imani, Haiyang Zhang, Yonina C. Eldar
The latest discussions on upcoming 6G wireless communications are envisioning future networks as a unified communications, sensing, and computing platform. The recently conceived concept of the smart radio environment, enabled by reconfigurable intelligent surfaces (RISs), contributes toward this vision, offering programmable propagation of information-bearing signals. Typical RIS implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways. However, this solely reflective operation induces significant challenges for RIS optimization from the wireless network orchestrator. For example, RISs lack information to locally tune their reflection pattern, which can be acquired only by other network entities and then shared with the RIS controller. Furthermore, channel estimation, which is essential for coherent RIS-empowered communications, is challenging with the available RIS designs. This article reviews the emerging concept of hybrid reflecting and sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while giving rise to potentially computationally autonomous and self-configuring metasurfaces. We discuss a hardware design for HRISs and detail a full-wave electromagnetic (EM) proof of concept. The distinctive properties of HRISs, in comparison to their solely reflective counterparts, are highlighted, and a simulation study evaluating HRISs’ capability for performing full and parametric channel estimation is presented. Future research challenges and opportunities arising from the HRIS concept are also included.
{"title":"Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications","authors":"George C. Alexandropoulos, Nir Shlezinger, Idban Alamzadeh, Mohammadreza F. Imani, Haiyang Zhang, Yonina C. Eldar","doi":"10.1109/mvt.2023.3332580","DOIUrl":"https://doi.org/10.1109/mvt.2023.3332580","url":null,"abstract":"The latest discussions on upcoming 6G wireless communications are envisioning future networks as a unified communications, sensing, and computing platform. The recently conceived concept of the smart radio environment, enabled by reconfigurable intelligent surfaces (RISs), contributes toward this vision, offering programmable propagation of information-bearing signals. Typical RIS implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways. However, this solely reflective operation induces significant challenges for RIS optimization from the wireless network orchestrator. For example, RISs lack information to locally tune their reflection pattern, which can be acquired only by other network entities and then shared with the RIS controller. Furthermore, channel estimation, which is essential for coherent RIS-empowered communications, is challenging with the available RIS designs. This article reviews the emerging concept of hybrid reflecting and sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while giving rise to potentially computationally autonomous and self-configuring metasurfaces. We discuss a hardware design for HRISs and detail a full-wave electromagnetic (EM) proof of concept. The distinctive properties of HRISs, in comparison to their solely reflective counterparts, are highlighted, and a simulation study evaluating HRISs’ capability for performing full and parametric channel estimation is presented. Future research challenges and opportunities arising from the HRIS concept are also included.","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/mvt.2023.3317525
J. P. Trovão
{"title":"Exploring Current Automotive Industry Trends [Automotive Electronics]","authors":"J. P. Trovão","doi":"10.1109/mvt.2023.3317525","DOIUrl":"https://doi.org/10.1109/mvt.2023.3317525","url":null,"abstract":"","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139194437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/mvt.2023.3297793
Xinran Zhang, Jingyuan Liu, T. Hu, Zheng Chang, Yanru Zhang, Geyong Min
Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally aggregate the results, in the vehicular edge computing (VEC) system for a vision of connected and autonomous vehicles. In this article, we review and present the concept of FL and introduce a general architecture of FL-assisted VEC to advance development of FL in the vehicular network. The enabling technologies for designing such a system are discussed and, with a focus on the vehicle selection algorithm, performance evaluations are conducted. Recommendations on future research directions are highlighted as well.
{"title":"Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions","authors":"Xinran Zhang, Jingyuan Liu, T. Hu, Zheng Chang, Yanru Zhang, Geyong Min","doi":"10.1109/mvt.2023.3297793","DOIUrl":"https://doi.org/10.1109/mvt.2023.3297793","url":null,"abstract":"Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally aggregate the results, in the vehicular edge computing (VEC) system for a vision of connected and autonomous vehicles. In this article, we review and present the concept of FL and introduce a general architecture of FL-assisted VEC to advance development of FL in the vehicular network. The enabling technologies for designing such a system are discussed and, with a focus on the vehicle selection algorithm, performance evaluations are conducted. Recommendations on future research directions are highlighted as well.","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62715057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/mvt.2023.3338494
J. Gozálvez
{"title":"Welcome to the December 2023 Issue [From the Editor]","authors":"J. Gozálvez","doi":"10.1109/mvt.2023.3338494","DOIUrl":"https://doi.org/10.1109/mvt.2023.3338494","url":null,"abstract":"","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/mvt.2023.3325506
Elisabeth Uhlemann
{"title":"Always Look on the Bright Side of Life [Connected and Automated Vehicles]","authors":"Elisabeth Uhlemann","doi":"10.1109/mvt.2023.3325506","DOIUrl":"https://doi.org/10.1109/mvt.2023.3325506","url":null,"abstract":"","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139189580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/mvt.2023.3328048
Gordon L. Stüber
{"title":"VTS Awards Presented at VTC2023-Fall in Hong Kong [Awards]","authors":"Gordon L. Stüber","doi":"10.1109/mvt.2023.3328048","DOIUrl":"https://doi.org/10.1109/mvt.2023.3328048","url":null,"abstract":"","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139194231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/mvt.2023.3333444
Pengyuan Zhou, Lik-Hang Lee, Zhi Liu, Hang Qiu, Tristan Braud, Aaron Yi Ding, S. Tarkoma, Pan Hui
To achieve this goal, the metaverse heavily relies on extended reality (XR), IoT, and communication technologies. With such an overlap in supporting technologies, we expect a convergence between connected and automated vehicle applications and the metaverse. Connected and automated vehicles are mobile platforms equipped with significant sensing and computing capabilities that can augment the metaverse. On the other hand, immersive metaverse applications can improve the en route entertainment and driving experience of the driver and passenger. Meanwhile, the richer information collected and created from the metaverse has created new challenges, such as information filtering, object positioning, vision transformation, etc. These challenges are often computation-intensive and bring considerable additional delay to the connected and automated vehicles which demand near real-time reactions. Researchers have thus proposed edge and cloud computing, machine learning, and computer vision solutions to tackle such challenges.
{"title":"Metaverse for Connected and Automated Vehicles and Intelligent Transportation Systems [From the Guest Editors]","authors":"Pengyuan Zhou, Lik-Hang Lee, Zhi Liu, Hang Qiu, Tristan Braud, Aaron Yi Ding, S. Tarkoma, Pan Hui","doi":"10.1109/mvt.2023.3333444","DOIUrl":"https://doi.org/10.1109/mvt.2023.3333444","url":null,"abstract":"To achieve this goal, the metaverse heavily relies on extended reality (XR), IoT, and communication technologies. With such an overlap in supporting technologies, we expect a convergence between connected and automated vehicle applications and the metaverse. Connected and automated vehicles are mobile platforms equipped with significant sensing and computing capabilities that can augment the metaverse. On the other hand, immersive metaverse applications can improve the en route entertainment and driving experience of the driver and passenger. Meanwhile, the richer information collected and created from the metaverse has created new challenges, such as information filtering, object positioning, vision transformation, etc. These challenges are often computation-intensive and bring considerable additional delay to the connected and automated vehicles which demand near real-time reactions. Researchers have thus proposed edge and cloud computing, machine learning, and computer vision solutions to tackle such challenges.","PeriodicalId":55004,"journal":{"name":"IEEE Vehicular Technology Magazine","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139194346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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