English | 繁體

Search in the results

UM

Browse/Search Results:  1-10 of 21 Help

Selected(0)Clear Items/Page:    Sort:
An RFSoC Prototype for Third-party Camera Aided mmWave Communications Journal article
Zhang, Tengyu, Wang, Yucong, Ouyang, Ming, Xing, Ling, Ma, Shaodan, Gao, Feifei. An RFSoC Prototype for Third-party Camera Aided mmWave Communications[J]. IEEE Transactions on Communications, 2024.
Authors:  Zhang, Tengyu;  Wang, Yucong;  Ouyang, Ming;  Xing, Ling;  Ma, Shaodan; et al.
Favorite | TC[Scopus]:0  IF:7.2/6.3 | Submit date：2024/11/05
Third-party Camera  Beam Alignment  Blockage Prediction  Mmwave Communications Prototype  
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory Conference paper
Zhao, Chuanbing, Feng, Yuan, Gao, Feifei, Zhang, Yong, Ma, Shaodan, Poor, H. Vincent. Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 2077-2082.
Authors:  Zhao, Chuanbing;  Feng, Yuan;  Gao, Feifei;  Zhang, Yong;  Ma, Shaodan; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/11/05
Beam Prediction  Environment Sensing  Mmwave  Transfer Learning  Adaptation Models  Costs  Accuracy  Simulation  Training Data  Predictive Models  
User-Centric Association for Dense mmWave Communication Systems with Multi-Connectivity Journal article
Xue, Qing, Xia, Hao, Mu, Jiajun, Xu, Yongjun, Yan, Li, Ma, Shaodan. User-Centric Association for Dense mmWave Communication Systems with Multi-Connectivity[J]. IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2024, 8(1), 177-189.
Authors:  Xue, Qing;  Xia, Hao;  Mu, Jiajun;  Xu, Yongjun;  Yan, Li; et al.
Favorite | TC[WOS]:2 TC[Scopus]:3  IF:5.3/4.5 | Submit date：2024/02/22
Mmwave  Multi-connectivity  Power Allocation  Ris-aided Wireless Communication  User Association  
A Multiband Standing Wave Oscillator With Wave Pattern Tailoring for Selective Mode Excitation Journal article
Kang, Zehui, Yang, Meng, Yin, Jun, Wu, Liang. A Multiband Standing Wave Oscillator With Wave Pattern Tailoring for Selective Mode Excitation[J]. IEEE Microwave and Wireless Technology Letters, 2024, 34(3), 330-333.
Authors:  Kang, Zehui;  Yang, Meng;  Yin, Jun;  Wu, Liang
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:0/0 | Submit date：2024/04/02
Millimeter Wave (mmWave)  Multiband  Multimode  Oscillator  Standing Wave Oscillator (Swo)  Transmission Line (tLine)  Wave Pattern Tailoring (Wpt)  
Vision-aided Ultra-Reliable Low-Latency Communications for Smart Factory Journal article
Feng, Yuan, Gao, Feifei, Tao, Xiaoming, Ma, Shaodan, Poor, H. Vincent. Vision-aided Ultra-Reliable Low-Latency Communications for Smart Factory[J]. IEEE Transactions on Communications, 2024, 72(6), 3439-3453.
Authors:  Feng, Yuan;  Gao, Feifei;  Tao, Xiaoming;  Ma, Shaodan;  Poor, H. Vincent
Favorite | TC[WOS]:1 TC[Scopus]:2  IF:7.2/6.3 | Submit date：2024/05/16
Camera Vision  Cameras  Feature Extraction  Industry 4.0  Millimeter Wave Communication  Mmwave  Production Facilities  Smart Factory  Smart Manufacturing  Ultra Reliable Low Latency Communication  Urllc  Wireless Communication  
Multi-User mmWave Uplink Communications Based on Collaborative Double-RIS: Joint Beamforming and Power Control Journal article
Qing Xue, Renlong Wei, Shaodan Ma, Yongjun Xu, Li Yan. Multi-User mmWave Uplink Communications Based on Collaborative Double-RIS: Joint Beamforming and Power Control[J]. IEEE Communications Letters, 2023, 27(10), 2702-2706.
Authors:  Qing Xue;  Renlong Wei;  Shaodan Ma;  Yongjun Xu;  Li Yan
Favorite | TC[WOS]:5 TC[Scopus]:8  IF:3.7/3.5 | Submit date：2023/11/01
Beamforming  Millimeter Wave (mmWave)  Power Control  Reconfigurable Intelligent Surface (Ris)  
Integrated Sensing and Communications With Joint Beam-Squint and Beam-Split for mmWave/THz Massive MIMO Journal article
Gao, Feifei, Xu, Liangyuan, Ma, Shaodan. Integrated Sensing and Communications With Joint Beam-Squint and Beam-Split for mmWave/THz Massive MIMO[J]. IEEE Transactions on Communications, 2023, 71(5), 2963-2976.
Authors:  Gao, Feifei;  Xu, Liangyuan;  Ma, Shaodan
Favorite | TC[WOS]:26 TC[Scopus]:37  IF:7.2/6.3 | Submit date：2023/06/05
Beam-split  Beam-squint  Integrated Sensing And Communications  Massive Mimo  Mmwave/thz  
Hardware-Impaired RIS-Assisted mmWave Hybrid Systems: Beamforming Design and Performance Analysis Journal article
Gong,Shiqi, Xing,Chengwen, Liu,Heng, Zhao,Xin, Wang,Jintao, An,Jianping, Quek,Tony Q.S.. Hardware-Impaired RIS-Assisted mmWave Hybrid Systems: Beamforming Design and Performance Analysis[J]. IEEE Transactions on Communications, 2023, 71(4), 2317-2334.
Authors:  Gong,Shiqi;  Xing,Chengwen;  Liu,Heng;  Zhao,Xin;  Wang,Jintao; et al.
Favorite | TC[WOS]:8 TC[Scopus]:13  IF:7.2/6.3 | Submit date：2023/08/03
Alternating Optimization (Ao)  Binary-oriented Exact  Hardware Impairments (Hwis)  Millimeter Wave (mmWave)  Reconfigurable Intelligent Surface (Ris)  
Antenna Array Topologies for mmWave Massive MIMO Systems: Spectral Efficiency Analysis Journal article
Tan, Weiqiang, Ma, Shaodan. Antenna Array Topologies for mmWave Massive MIMO Systems: Spectral Efficiency Analysis[J]. IEEE Transactions on Vehicular Technology, 2022, 71(12), 12901-12915.
Authors:  Tan, Weiqiang;  Ma, Shaodan
Favorite | TC[WOS]:12 TC[Scopus]:15  IF:6.1/6.5 | Submit date：2023/01/30
Massive Mimo  Maximum Ratio Transmission  Mmwave Communications  Spectral Efficiency  Uniform Circular Arrays  Uniform Linear Arrays  Uniform Rectangular Arrays  
Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach Journal article
Qing Xue, Yi-Jing Liu, Yao Sun, Wang, Jian, Li Yan, Gang Feng, Shaodan Ma. Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2022, 9(1), 185-197.
Authors:  Qing Xue;  Yi-Jing Liu;  Yao Sun;  Wang, Jian;  Li Yan; et al.
Favorite | TC[WOS]:34 TC[Scopus]:44  IF:7.4/6.9 | Submit date：2023/01/30
Beam Management  Millimeter Wave (mmWave) Communication  Ultra-dense Network  Federated Reinforcement Learning