Hybrid Convolutional Beamspace Method for mmWave MIMO Channel Estimation

被引:0
|
作者
Chen, Po-Chih [1 ]
Vaidyanathan, P. P. [1 ]
机构
[1] CALTECH, Dept Elect Engn, Pasadena, CA 91125 USA
来源
FIFTY-SEVENTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, IEEECONF | 2023年
关键词
Convolutional beamspace; millimeter wave MIMO channel estimation; hybrid precoding; DOA estimation; sparse arrays; MASSIVE MIMO; ANGLE ESTIMATION; ARRAYS;
D O I
10.1109/IEEECONF59524.2023.10477082
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Millimeter-wave (mmWave) MIMO channel estimation is studied. To reduce hardware cost, hybrid analog and digital processing is used. Hybrid convolutional beamspace (CBS) method is proposed for estimation of the channel. This method is especially attractive for large arrays, which have received more attention recently. In particular, a nonuniform scheme of CBS is proposed. The receiver combiner is a CBS filter followed by a nonuniform decimator, and the transmitter precoder is a nonuniform expander followed by a CBS filter. Although the analog precoder and analog combiner should have unit-modulus entries, it is shown that any CBS filter coefficients are realizable. The nonuniform decimation or expansion corresponds to antenna locations of a virtual sparse array, dilated by an integer factor. Thus, given a small number of RF chains, meaning low hardware complexity, a significant number of paths can still be estimated with difference coarray methods. More importantly, due to the dilation and sparse array structure, a larger coarray aperture is achieved, resulting in better estimation performance. The advantages of the proposed method are shown by simulations.
引用
收藏
页码:1293 / 1297
页数:5
相关论文
共 50 条
  • [11] Training Beam Design for Channel Estimation in Hybrid mmWave MIMO Systems
    Ge, Xiaochun
    Shen, Wenqian
    Xing, Chengwen
    Zhao, Lian
    An, Jianping
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (09) : 7121 - 7134
  • [12] Wideband Channel Estimation for Millimeter Wave Beamspace MIMO
    Cheng, Xiantao
    Deng, Jin
    Li, Shaoqian
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (07) : 7221 - 7225
  • [13] Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems
    Gonzalez-Coma, Jose P.
    Rodriguez-Fernandez, Javier
    Gonzalez-Prelcic, Nuria
    Castedo, Luis
    Heath, Robert W., Jr.
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2018, 12 (02) : 353 - 367
  • [14] Over-sampled Beamspace Channel Estimation for Millimeter Wave Massive MIMO
    Ma, Wenyan
    Qi, Chenhao
    2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2018,
  • [15] Hybrid Precoding for mmWave Massive Beamspace MIMO System with Limited Resolution Overlapped Phase Shifters Network
    Ding, Ting
    Zhu, Jiandong
    Yang, Jing
    Jiang, Xingmeng
    Liu, Chengcheng
    IEICE TRANSACTIONS ON ELECTRONICS, 2024, E107C (10) : 355 - 363
  • [16] Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO
    Osinsky, Alexander
    Ivanov, Andrey
    Lakontsev, Dmitry
    Yarotsky, Dmitry
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (02) : 311 - 314
  • [17] Signal Shaping for Non-Uniform Beamspace Modulated mmWave Hybrid MIMO Communications
    Guo, Shuaishuai
    Zhang, Haixia
    Zhang, Peng
    Dang, Shuping
    Xu, Chengcheng
    Alouini, Mohamed-Slim
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (10) : 6660 - 6674
  • [18] A beamspace channel estimation based on deep convolutional reconstruction networks
    Fei, Teng
    Zhu, Zhengyu
    Zhang, Jingyu
    Liu, Lanxue
    Yang, Xinzong
    INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2025, 47 (02) : 88 - 97
  • [19] A fast gridless mmWave full-dimensional MIMO channel estimation method
    Ma, Tao
    Fan, Xiangning
    Wu, Xiaohuan
    DIGITAL SIGNAL PROCESSING, 2022, 129
  • [20] Switch-Based Hybrid Analog/Digital Channel Estimation for mmWave Massive MIMO
    Poulin, Alec
    Morsali, Alireza
    Champagne, Benoit
    2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL), 2020,