Pub Date : 2023-03-19DOI: 10.26599/TST.2023.9010001
Zijian Zhang;Linglong Dai
Thanks to the recent advances in metamaterials, Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology for future 6G wireless communications. Benefiting from its high array gain, low cost, and low power consumption, RISs are expected to greatly enlarge signal coverage, improve system capacity, and increase energy efficiency. In this article, we systematically overview the emerging RIS technology with the focus on its key basics, nine fundamental issues, and one critical problem. Specifically, we first explain the RIS basics, including its working principles, hardware structures, and potential benefits for communications. Based on these basics, nine fundamental issues of RISs, such as “What's the differences between RISs and massive MIMO?” and “Is RIS really intelligent?”, are explicitly addressed to elaborate its technical features, distinguish it from existing technologies, and clarify some misunderstandings in the literature. Then, one critical problem of RISs is revealed that, due to the “multiplicative fading” effect, existing passive RISs can hardly achieve visible performance gains in many communication scenarios with strong direct links. To address this critical problem, a potential solution called active RISs is introduced, and its effectiveness is demonstrated by numerical simulations.
{"title":"Reconfigurable Intelligent Surfaces for 6G: Nine Fundamental Issues and One Critical Problem","authors":"Zijian Zhang;Linglong Dai","doi":"10.26599/TST.2023.9010001","DOIUrl":"https://doi.org/10.26599/TST.2023.9010001","url":null,"abstract":"Thanks to the recent advances in metamaterials, Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology for future 6G wireless communications. Benefiting from its high array gain, low cost, and low power consumption, RISs are expected to greatly enlarge signal coverage, improve system capacity, and increase energy efficiency. In this article, we systematically overview the emerging RIS technology with the focus on its key basics, nine fundamental issues, and one critical problem. Specifically, we first explain the RIS basics, including its working principles, hardware structures, and potential benefits for communications. Based on these basics, nine fundamental issues of RISs, such as “What's the differences between RISs and massive MIMO?” and “Is RIS really intelligent?”, are explicitly addressed to elaborate its technical features, distinguish it from existing technologies, and clarify some misunderstandings in the literature. Then, one critical problem of RISs is revealed that, due to the “multiplicative fading” effect, existing passive RISs can hardly achieve visible performance gains in many communication scenarios with strong direct links. To address this critical problem, a potential solution called active RISs is introduced, and its effectiveness is demonstrated by numerical simulations.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"28 5","pages":"929-939"},"PeriodicalIF":6.6,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10130021/10130029.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68018826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.26599/TST.2022.9010047
Yonglong Jiang;Liangliang Li;Jiahe Zhu;Yuan Xue;Hongbing Ma
Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement. DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively. The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.
{"title":"DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement","authors":"Yonglong Jiang;Liangliang Li;Jiahe Zhu;Yuan Xue;Hongbing Ma","doi":"10.26599/TST.2022.9010047","DOIUrl":"https://doi.org/10.26599/TST.2022.9010047","url":null,"abstract":"Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement. DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively. The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"28 4","pages":"743-753"},"PeriodicalIF":6.6,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10011153/10011162.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67893896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.26599/TST.2022.9010042
Ying Jin;Yi Zhang;Yiwen Zhang
{"title":"Neighbor Library-Aware Graph Neural Network for Third Party Library Recommendation","authors":"Ying Jin;Yi Zhang;Yiwen Zhang","doi":"10.26599/TST.2022.9010042","DOIUrl":"https://doi.org/10.26599/TST.2022.9010042","url":null,"abstract":"","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"28 4","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10011153/10011160.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67893897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units","authors":"Guangjian Zeng;Jinhu Zhuang;Haofan Huang;Mu Tian;Yi Gao;Yong Liu;Xiaxia Yu","doi":"10.26599/TST.2022.9010027","DOIUrl":"https://doi.org/10.26599/TST.2022.9010027","url":null,"abstract":"","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"28 4","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10011153/10011176.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67893982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad","authors":"Fenfang Li;Hui Lv;Yiming Gao;Dolha;Yan Li;Qingguo Zhou","doi":"10.26599/TST.2022.9010055","DOIUrl":"https://doi.org/10.26599/TST.2022.9010055","url":null,"abstract":"","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"28 6","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10197185/10197207.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68015866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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