Pub Date : 2024-12-19DOI: 10.1109/jsac.2024.3513768
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/jsac.2024.3513768","DOIUrl":"https://doi.org/10.1109/jsac.2024.3513768","url":null,"abstract":"","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"72 1","pages":""},"PeriodicalIF":16.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-11-08DOI: 10.1109/jsac.2024.3492699
Junyu Liu, Chengyi Zhou, Min Sheng, Haojun Yang, Xinyu Huang, Jiandong Li
{"title":"Resource Allocation for Adaptive Beam Alignment in UAV-assisted Integrated Sensing and Communication Networks","authors":"Junyu Liu, Chengyi Zhou, Min Sheng, Haojun Yang, Xinyu Huang, Jiandong Li","doi":"10.1109/jsac.2024.3492699","DOIUrl":"https://doi.org/10.1109/jsac.2024.3492699","url":null,"abstract":"","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"13 1","pages":""},"PeriodicalIF":16.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1109/jsac.2024.3460034
Amr S. Matar, Xuemin Shen
{"title":"Joint Optimization of User Association, Power Control, and Dynamic Spectrum Sharing for Integrated Aerial-Terrestrial Network","authors":"Amr S. Matar, Xuemin Shen","doi":"10.1109/jsac.2024.3460034","DOIUrl":"https://doi.org/10.1109/jsac.2024.3460034","url":null,"abstract":"","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"17 1","pages":""},"PeriodicalIF":16.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-13DOI: 10.1109/jsac.2024.3460061
Anal Paul, Keshav Singh, Aryan Kaushik, Chih-Peng Li, Octavia A. Dobre, Marco Di Renzo, Trung Q. Duong
{"title":"Quantum-Enhanced DRL Optimization for DoA Estimation and Task Offloading in ISAC Systems","authors":"Anal Paul, Keshav Singh, Aryan Kaushik, Chih-Peng Li, Octavia A. Dobre, Marco Di Renzo, Trung Q. Duong","doi":"10.1109/jsac.2024.3460061","DOIUrl":"https://doi.org/10.1109/jsac.2024.3460061","url":null,"abstract":"","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"323 1","pages":""},"PeriodicalIF":16.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-13DOI: 10.1109/jsac.2024.3460057
Jiachen Sun, Xu Chen, Chunxiao Jiang, Song Guo
{"title":"Distributionally Robust Optimization of On-Orbit Resource Scheduling for Remote Sensing in Space-Air-Ground Integrated 6G Networks","authors":"Jiachen Sun, Xu Chen, Chunxiao Jiang, Song Guo","doi":"10.1109/jsac.2024.3460057","DOIUrl":"https://doi.org/10.1109/jsac.2024.3460057","url":null,"abstract":"","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"9 1","pages":""},"PeriodicalIF":16.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Semantically-Disentangled Progressive Image Compression for Deep Space Communications: Exploring the Ultra-low Rate Regime","authors":"Weicheng Zhang, Yajing Liu, Lingyu Chen, Jianghong Shi, Xuemin Hong, Xianbin Wang","doi":"10.1109/jsac.2024.3365886","DOIUrl":"https://doi.org/10.1109/jsac.2024.3365886","url":null,"abstract":"","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"16 1 1","pages":""},"PeriodicalIF":16.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-02-01Epub Date: 2023-08-07DOI: 10.1007/s12070-023-03999-5
Mahmoud Ahmed Shawky, Mohamed Ahmed Shawky, Nada Zakaria Zakaria
Augmentation rhinoplasty or commonly known as "nose jobs" is one of the most common plastic surgical procedures aimed to improve cosmetic appearance. This procedure is considerably safer, less time consuming with faster recovery and immediate cosmetic effect. This procedure needs of highly experienced and well-trained plastic surgeon. According to facial analysis you can select the type of rhinoplasty. Open discussion with the patient to select appropriate surgical technique and its possible risks with your plastic surgeon to ensure the highest level of safety and satisfaction. Autologous grafting materials are safe, efficient and also the first choice for rhinoplasty due to it can survive without a vascular supply, the resorption rate of cartilage is much lower than that of a bone graft. Autologous grafting materials are stable and resistant to infection and extrusion over time so, they are successfully used for dorsal augmentation. To perform successful augmentation rhinoplasty, surgeons should be highly experienced and well-trained and augmentation materials that are currently available and understand their risks, benefits and uses. Autologous cartilage graft regarded as the graft of choice in augmentation rhinoplasty because of their lower rate of infection, rejection, resorption, extrusion, donor site morbidity, easy reshaping.
{"title":"Safety and Efficacy of Autologous Cartilage Graft in Augmentation Rhinoplasty.","authors":"Mahmoud Ahmed Shawky, Mohamed Ahmed Shawky, Nada Zakaria Zakaria","doi":"10.1007/s12070-023-03999-5","DOIUrl":"10.1007/s12070-023-03999-5","url":null,"abstract":"<p><p>Augmentation rhinoplasty or commonly known as \"nose jobs\" is one of the most common plastic surgical procedures aimed to improve cosmetic appearance. This procedure is considerably safer, less time consuming with faster recovery and immediate cosmetic effect. This procedure needs of highly experienced and well-trained plastic surgeon. According to facial analysis you can select the type of rhinoplasty. Open discussion with the patient to select appropriate surgical technique and its possible risks with your plastic surgeon to ensure the highest level of safety and satisfaction. Autologous grafting materials are safe, efficient and also the first choice for rhinoplasty due to it can survive without a vascular supply, the resorption rate of cartilage is much lower than that of a bone graft. Autologous grafting materials are stable and resistant to infection and extrusion over time so, they are successfully used for dorsal augmentation. To perform successful augmentation rhinoplasty, surgeons should be highly experienced and well-trained and augmentation materials that are currently available and understand their risks, benefits and uses. Autologous cartilage graft regarded as the graft of choice in augmentation rhinoplasty because of their lower rate of infection, rejection, resorption, extrusion, donor site morbidity, easy reshaping.</p>","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"24 1","pages":"19-25"},"PeriodicalIF":0.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10908760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84350736","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 : 2023-11-01DOI: 10.1109/jsac.2023.3310089
Mi Li, Cen Chen, Xulei Yang, Joey Tianyi Zhou, Tao Zhang, Yangfan Li
Digital twin technology has recently gathered pace in engineering communities as it allows for the convergence of the real structure and its digital counterpart. 3D point cloud data is a more effective way to describe the real world and to reconstruct the digital counterpart than the conventional 2D images or 360-degree images. Large-scale, e.g., city-scale digital twins, typically collect point cloud data via internet-of-things (IoT) devices and transmit it over wireless networks. However, the existing wireless transmission technology can not carry real-time point cloud transmission for digital twin reconstruction due to mass data volume, high processing overheads, and low delay-tolerance. We propose a novel artificial intelligence (AI) powered end-to-end framework, termed AIRec, for efficient digital twin communication from point cloud compression, wireless channel coding, and digital twin reconstruction. AIRec adopts the encoder-decoder architecture. In the encoder, a novel importance-aware pooling scheme is designed to adaptively select important points with learnable thresholds to reduce the transmission volume. We also design a novel noise-aware joint source and channel coding is proposed to adaptively adjust the transmission strategy based on SNR and map the features to error-resilient channel symbols for wireless transmission to achieve a good tradeoff between the transmission rate and reconstruction quality. The decoder can accurately reconstruct the digital twins from the received symbols. Extensive experiments of typical datasets and comparison with baselines show that we achieve a good reconstruction quality under $24times $ compression ratio.
{"title":"Towards Communication-efficient Digital Twin via AI-powered Transmission and Reconstruction","authors":"Mi Li, Cen Chen, Xulei Yang, Joey Tianyi Zhou, Tao Zhang, Yangfan Li","doi":"10.1109/jsac.2023.3310089","DOIUrl":"https://doi.org/10.1109/jsac.2023.3310089","url":null,"abstract":"Digital twin technology has recently gathered pace in engineering communities as it allows for the convergence of the real structure and its digital counterpart. 3D point cloud data is a more effective way to describe the real world and to reconstruct the digital counterpart than the conventional 2D images or 360-degree images. Large-scale, e.g., city-scale digital twins, typically collect point cloud data via internet-of-things (IoT) devices and transmit it over wireless networks. However, the existing wireless transmission technology can not carry real-time point cloud transmission for digital twin reconstruction due to mass data volume, high processing overheads, and low delay-tolerance. We propose a novel artificial intelligence (AI) powered end-to-end framework, termed AIRec, for efficient digital twin communication from point cloud compression, wireless channel coding, and digital twin reconstruction. AIRec adopts the encoder-decoder architecture. In the encoder, a novel importance-aware pooling scheme is designed to adaptively select important points with learnable thresholds to reduce the transmission volume. We also design a novel noise-aware joint source and channel coding is proposed to adaptively adjust the transmission strategy based on SNR and map the features to error-resilient channel symbols for wireless transmission to achieve a good tradeoff between the transmission rate and reconstruction quality. The decoder can accurately reconstruct the digital twins from the received symbols. Extensive experiments of typical datasets and comparison with baselines show that we achieve a good reconstruction quality under $24times $ compression ratio.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"1 1","pages":"3624-3635"},"PeriodicalIF":16.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62352333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.
{"title":"Digital Twin-enabled Federated Learning in Mobile Networks: From the Perspective of Communication-assisted Sensing","authors":"Junsheng Mu, Wenjia Ouyang, Tao Hong, Weijie Yuan, Yuanhao Cui, Zexuan Jing","doi":"10.1109/jsac.2023.3310082","DOIUrl":"https://doi.org/10.1109/jsac.2023.3310082","url":null,"abstract":"With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"25 1","pages":"3230-3241"},"PeriodicalIF":16.4,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62352164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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