A Novel 6G Continuous-Space Channel Model Based on Electromagnetic Information Theory

被引:0
|
作者
Ma, Qingyin [1 ,2 ]
Wang, Cheng-Xiang [1 ,2 ]
Huang, Jie [1 ,2 ]
Yang, Yue [1 ,2 ]
机构
[1] Southeast Univ, Sch Informat Sci & Engn, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
[2] Purple Mt Labs, Nanjing 211111, Peoples R China
基金
中国国家自然科学基金;
关键词
Continuous-space EM channel modeling; EM information theory; antenna response; mutual coupling; small-scale fading; CAPACITY;
D O I
10.1109/WCNC57260.2024.10570937
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In the sixth generation (6G) wireless communication systems, the communication coverage range becomes broader, where transmitters (Tx) and receivers (Rx) can be randomly distributed in continuous space. The continuous-space electromagnetic (EM) channel is different from the conventional propagation channel. Its input is continuous current density and its output is continuous electric field so that channel characteristics at any point can be given under the guidance of EM information theory (EIT). EIT is a theory integrating traditional theories applied in communications, such as EM theory and information theory. In this paper, a novel 6G continuous-space EM channel model jointly considering antenna response (AR), mutual coupling (MC), and small-scale fading is proposed. AR at the Tx and Rx sides is modeled using an EM calculation method. MC is obtained using antenna theory. Small-scale fading is described using a geometry-based stochastic model (GBSM). Important statistical properties and channel capacity are analyzed to reveal characteristics of the proposed channel model. The results illustrate that the antenna array has non-negligible impacts on statistical properties of the channel model and channel capacity.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] A 3D Continuous-Space Electromagnetic Channel Model for 6G Tri-Polarized Multi-User Communications
    Yang, Yue
    Wang, Cheng-Xiang
    Huang, Jie
    Thompson, John
    Poor, H. Vincent
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (11) : 17354 - 17367
  • [2] Capacity of the continuous-space electromagnetic channel
    Jensen, Michael A.
    Wallace, Jon W.
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2008, 56 (02) : 524 - 531
  • [3] Electromagnetic Information Theory: Fundamentals and Applications for 6G Wireless Communication Systems
    Wang, Cheng-Xiang
    Yang, Yue
    Huang, Jie
    Gao, Xiqi
    Cui, Tie Jun
    Hanzo, Lajos
    IEEE WIRELESS COMMUNICATIONS, 2024, 31 (05) : 279 - 286
  • [4] A Novel GBSM and a RT Channel Model for 6G ISAC Systems
    Wang, Ruoyu
    Chang, Hengtai
    Yang, Runruo
    Wang, Cheng-Xiang
    You, Xiaohu
    2024 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC, 2024,
  • [5] A Novel Scatterer Density-Based Predictive Channel Model for 6G Wireless Communications
    Li, Zheao
    Wang, Cheng-Xiang
    Huang, Chen
    Yu, Long
    Li, Junling
    Qian, Zhongyu
    2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING, 2023,
  • [6] Agent-based continuous-space particle pedestrian model
    Chen, Feng
    Wang, Zi-jia
    Zhu, Ya-di
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT, 2015, 168 (04) : 336 - 345
  • [7] THz Channel Model for 6G Communications
    Hossain, Zahed
    Li, Qian
    Ying, Dawei
    Wu, Geng
    Xiong, Cong
    2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2021,
  • [8] A Novel 6G ISAC Channel Model Combining Forward and Backward Scattering
    Yang, Runruo
    Wang, Cheng-Xiang
    Huang, Jie
    Aggoune, El-Hadi M.
    Hao, Yang
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2023, 22 (11) : 8050 - 8065
  • [9] 6G SAGIN Information Transmission Model
    Zhang, Yuexia
    Wang, Xinyi
    Gang, Yuanshuo
    Wang, Jian
    Wu, Sheng
    Zhang, Peiying
    Shi, Yuanming
    IEEE COMMUNICATIONS MAGAZINE, 2025,
  • [10] 6G Cognitive Information Theory: A Mailbox Perspective
    Hao, Yixue
    Miao, Yiming
    Chen, Min
    Gharavi, Hamid
    Leung, Victor C. M.
    BIG DATA AND COGNITIVE COMPUTING, 2021, 5 (04)