George C. Alexandropoulos, Nir Shlezinger, Idban Alamzadeh, Mohammadreza F. Imani, Haiyang Zhang, Yonina C. Eldar
{"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}
{"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}
Xinran Zhang, Jingyuan Liu, T. Hu, Zheng Chang, Yanru Zhang, Geyong Min
{"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}
{"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}
{"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}