Multi-Beam Design for Near-Field Extremely Large-Scale RIS-Aided Wireless Communications

被引:14
|
作者
Shen, Decai [1 ,2 ]
Dai, Linglong [1 ,2 ]
Su, Xin [3 ]
Suo, Shiqiang [3 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
[3] CICT Mobile Commun Technol Co Ltd, Innovat Ctr, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Reconfigurable intelligent surface (RIS); constant modulus constraint; multi-beam design; MASSIVE MIMO SYSTEMS; INTELLIGENT;
D O I
10.1109/TGCN.2023.3259579
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
As the energy-saving array composed of passive elements, reconfigurable intelligent surface (RIS) will evolve to the extremely large-scale RIS (XL-RIS) to overcome serious path loss. This change leads to the near-field propagation becoming dominant. There are some works to explore the near-field beam design via beam training. Unfortunately, due to the constant modulus constraint for XL-RIS, most of works in the near-field scenario focus on single-beam design. For massive connectivity requirement scenario, these works will face a serious loss of beam gains, resulting in a decrease in transmission rate. To solve this problem, we propose a block coordinate descent-based scheme with majorization-minimization (MM) algorithm for multi-beam design. The proposed scheme handles constant modulus constraint from two aspects. Firstly, under this constraint, the multi-beam design is an intractable non-convex quadratic programming problem. We utilize MM algorithm to solve this problem as several iterative sub-problems which are easily to be solved. Secondly, the solution space for multi-beam optimization is confined to a limited space due to this constraint, so we introduce the phases for beam gains as an extra optimizable variable to enrich the degree of freedom for optimization. Simulation results show that the proposed scheme could achieve a superior rate 50% higher than the existing schemes.
引用
收藏
页码:1542 / 1553
页数:12
相关论文
共 50 条
  • [41] Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain
    Liu, Wang
    Ren, Hong
    Pan, Cunhua
    Wang, Jiangzhou
    IEEE COMMUNICATIONS LETTERS, 2023, 27 (01) : 170 - 174
  • [42] Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?
    Cui, Mingyao
    Dai, Linglong
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (04) : 2663 - 2677
  • [43] Near-Field Modeling and Performance Analysis for Multi-User Extremely Large-Scale MIMO Communication
    Lu, Haiquan
    Zeng, Yong
    IEEE COMMUNICATIONS LETTERS, 2022, 26 (02) : 277 - 281
  • [44] Near-Field Channel Estimation for Extremely Large-scale MIMO with Hybrid Precoding
    Cui, Mingyao
    Dai, Linglong
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [45] Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A Model-Based Deep Learning Approach
    Zhang, Xiangyu
    Wang, Zening
    Zhang, Haiyang
    Yang, Luxi
    IEEE COMMUNICATIONS LETTERS, 2023, 27 (04) : 1155 - 1159
  • [46] QoS-Aware Resource Allocation of RIS-Aided Multi-User MISO Wireless Communications
    Gao, Ya
    Lu, Chengzhuang
    Lian, Yuhang
    Li, Xingwang
    Chen, Gaojie
    da Costa, Daniel Benevides
    Nallanathan, Arumugam
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (02) : 2872 - 2877
  • [47] PARAFAC Decomposition based Channel Estimation for RIS-aided Multi-User MISO Wireless Communications
    Beldi, Chaima
    Dziri, Ali
    Abdelkefi, Fatma
    Shaiek, Hmaied
    2023 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2023, : 1537 - 1542
  • [48] Large-Scale RIS Enabled Air-Ground Channels: Near-Field Modeling and Analysis
    Jiang, Hao
    Shi, Wangqi
    Zhang, Zaichen
    Pan, Cunhua
    Wu, Qingqing
    Shu, Feng
    Liu, Ruiqi
    Chen, Zhen
    Wang, Jiangzhou
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2025, 24 (02) : 1074 - 1088
  • [49] Near-field RIS-aided Localization Under Channel Non-Stationarity: A Mismatched Model Approach
    Sun, Bo
    Keskint, Musa Furkan
    Rahalt, Moustafa
    Chen, Hui
    Talvitie, Jukka
    Wymeerscht, Henk
    Valkama, Mikko
    2024 IEEE 25TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS, SPAWC 2024, 2024, : 271 - 275
  • [50] Beam Training and Tracking for Extremely Large-Scale MIMO Communications
    Chen, Kangjian
    Qi, Chenhao
    Wang, Cheng-Xiang
    Li, Geoffrey Ye
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (05) : 5048 - 5062