STILL WATERS RUN DEEP: EXTEND THZ COVERAGE WITH NON-INTELLIGENT REFLECTING SURFACE

被引：2
作者：
Han, Chong [1 ]
Li, Yuanbo [1 ]
Wang, Yiqin [1 ]
Yu, Ziming [2 ]
机构：
[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
[2] Huawei Technol Co Ltd, Shenzhen, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS | 2024年 / 31卷 / 01期
基金：
中国国家自然科学基金;
关键词：
COMMUNICATION; SYSTEMS;
D O I：
10.1109/MWC.004.2300332
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Large reflection and diffraction losses in the Terahertz (THz) band give rise to degraded coverage abilities in non-line-of-sight (NLoS) areas. To overcome this, a non-intelligent reflecting surface (NIRS) can be used, which is essentially a rough surface made by metal materials. NIRS is not only able to enhance received power in large NLoS areas through rich reflections and scattering, but also costless and super-easy to fabricate and implement. In this article, we first thoroughly compare NIRS with the lively discussed intelligent reflecting surface (IRS) and point out their advantages and disadvantages. Furthermore, experimental results are elaborated to show the effectiveness of NIRS in improving coverage. Last but not least, open problems and future directions are highlighted to inspire future research efforts on NIRS.
引用
收藏
页码：41 / 47
页数：7
相关论文
共 16 条
  • [1] Blind Beamforming for Coverage Enhancement With Intelligent Reflecting Surface
    Xu, Fan
    Yao, Jiawei
    Lai, Wenhai
    Shen, Kaiming
    Li, Xin
    Chen, Xin
    Luo, Zhi-Quan
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (11) : 15736 - 15752
  • [2] Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning
    Wang, Wei
    Zhang, Wei
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2022, 40 (08) : 2335 - 2346
  • [3] Deep Reinforcement Learning-Based Relay Selection in Intelligent Reflecting Surface Assisted Cooperative Networks
    Huang, Chong
    Chen, Gaojie
    Gong, Yu
    Wen, Miaowen
    Chambers, Jonathon A.
    [J]. IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (05) : 1036 - 1040
  • [4] The Far-/Near-Field Beam Squint and Solutions for THz Intelligent Reflecting Surface Communications
    Hao, Wanming
    You, Xiaobei
    Zhou, Fuhui
    Chu, Zheng
    Sun, Gangcan
    Xiao, Pei
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (08) : 10107 - 10118
  • [5] Beamforming Optimization for Intelligent Reflecting Surface Assisted MISO: A Deep Transfer Learning Approach
    Ge, Yimeng
    Fan, Jiancun
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (04) : 3902 - 3907
  • [6] Deep Reinforcement Learning-Based Secure Standalone Intelligent Reflecting Surface Operation
    Omid, Yasaman
    Deng, Yansha
    Nallanathan, Arumugam
    [J]. 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 5329 - 5334
  • [7] Intelligent Reflecting Surface-Assisted Interference Mitigation With Deep Reinforcement Learning for Radio Astronomy
    Peng, Junhui
    Cao, Hailin
    Ali, Zahid
    Wu, Xiaodong
    Fan, Jin
    [J]. IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2022, 21 (09): : 1757 - 1761
  • [8] A Secure-Transmission Maximization Scheme for SWIPT Systems Assisted by an Intelligent Reflecting Surface and Deep Learning
    Huynh Thanh Thien
    Pham-Viet Tuan
    Koo, Insoo
    [J]. IEEE ACCESS, 2022, 10 : 31851 - 31867
  • [9] Deep Reinforcement Learning Based Resource Allocation for Intelligent Reflecting Surface Assisted Dynamic Spectrum Sharing
    Guo, Jianxin
    Wang, Zhe
    Li, Jun
    Zhang, Jie
    [J]. 2022 14TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING, WCSP, 2022, : 1178 - 1183
  • [10] Channel Estimation for Intelligent Reflecting Surface Assisted Massive MIMO Systems-A Deep Learning Approach
    Mao, Zhendong
    Liu, Xiqing
    Peng, Mugen
    [J]. IEEE COMMUNICATIONS LETTERS, 2022, 26 (04) : 798 - 802
12