User and Passive Beam Scheduling Scheme for Liquid Crystal IRS-assisted mmWave Communications

被引:0
|
作者
Yoshikawa, Keiji [1 ]
Ohto, Takuya [1 ]
Hayashi, Takahiro [1 ]
机构
[1] KDDI Res Inc, 2-1-15 Ohara, Fujimino, Saitama 3568502, Japan
来源
2024 18TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION, EUCAP | 2024年
关键词
Intelligent reflecting surface; liquid crystal; user scheduling; beam scheduling; millimeter wave; RESOURCE-ALLOCATION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Intelligent reflecting surfaces (IRS) have been considered a strong solution for coverage holes in millimeter wave communication. By controlling the reflection phase of the IRS, radio waves can be reflected toward user equipment (UE) within the coverage hole, enabling communication with the base station (BS). We focus on liquid crystal (LC) IRS, which uses liquid crystals to control the reflection phase. Compared to other IRSs, LC IRS has the advantage of lower power consumption. However, it has a longer response time to change the reflection direction than the symbol length in new radio (NR). Generally, user scheduling at an NR BS is performed on a time-division basis, assuming instantaneous beam switching. However, during the response time of the LC IRS, the radio waves are not reflected in the desired direction, resulting in a decrease in throughput. This paper proposes a UE selection and reflection direction control method to improve the throughput reduction in an environment with an LC IRS. The proposed method formulates the problem to optimize the reflection pattern and switching timing, considering the loss due to switching. This can reduce the number of switches while addressing multiple UEs. Simulation evaluations demonstrate the improvement in throughput in an environment with an LC IRS using the proposed method.
引用
收藏
页数:5
相关论文
共 50 条
  • [21] Hybrid Precoding for IRS-assisted Secure mmWave Communication System with SWIPT
    Xue, Jianghao
    Zhou, Xin
    Wang, Chao
    Wang, Danyang
    Zhao, Yue
    Li, Zan
    2020 INTERNATIONAL CONFERENCE ON SPACE-AIR-GROUND COMPUTING (SAGC 2020), 2020, : 82 - 86
  • [22] Max-Min Throughput Optimization in WPCNs: A Hybrid Active/Passive IRS-Assisted Scheme
    Hameed, Iqra
    Koo, Insoo
    IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2024, 5 : 1123 - 1140
  • [23] Resource Allocation for IRS Assisted mmWave Wireless Powered Sensor Networks with User Cooperation
    Lin, Yonghui
    Zhu, Zhengyu
    CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2024, 138 (01): : 663 - 677
  • [24] Multi-IRS-Aided Multi-User MIMO in mmWave/THz Communications: A Space-Orthogonal Scheme
    Ning, Boyu
    Wang, Peilan
    Li, Lingxiang
    Chen, Zhi
    Fang, Jun
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (12) : 8138 - 8152
  • [25] Deep reinforcement learning for IRS-assisted UAV covert communications
    Bi, Songjiao
    Hu, Langtao
    Liu, Quanjin
    Wu, Jianlan
    Yang, Rui
    Wu, Lei
    CHINA COMMUNICATIONS, 2023, 20 (12) : 131 - 141
  • [26] Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System
    Shukla, Vidya Bhasker
    Krejcar, Ondrej
    Choi, Kwonhue
    Bhatia, Vimal
    Mishra, Ambuj Kumar
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2024, 10 (06) : 2224 - 2235
  • [27] Joint Location Sensing and Demodulation for IRS-Assisted ISAC mmWave MIMO Systems
    Peng, Xingyu
    Hu, Xiaoling
    Gan, Xu
    Zhong, Caijun
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2024, 72 (04) : 2470 - 2484
  • [28] Block Sparsity Based Channel Estimation for IRS-Assisted mmWave MIMO Systems
    Guo, Fang
    Zhou, Zhenhua
    Liao, Bin
    2024 IEEE 13RD SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP, SAM 2024, 2024,
  • [29] Physics-Based Channel Modeling for IRS-Assisted mmWave Communication Systems
    Lian, Zhuxian
    Zhang, Wendi
    Wang, Yajun
    Su, Yinjie
    Zhang, Bibo
    Jin, Biao
    Wang, Biao
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2024, 72 (05) : 2687 - 2700
  • [30] Robust Secure Transmission Design for IRS-Assisted mmWave Cognitive Radio Networks
    Wu, Xuewen
    Ma, Jingxiao
    Gu, Chenwei
    Xue, Xiaoping
    Zeng, Xin
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (08) : 8441 - 8456