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
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
  • [21] Beamspace Channel Estimation for Millimeter Wave Massive MIMO System With Hybrid Precoding and Combining
    Ma, Wenyan
    Qi, Chenhao
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, 66 (18) : 4839 - 4853
  • [22] Channel Estimation and Performance Analysis of Beamspace MIMO Systems
    Zhou, Lin
    Ratnarajah, Tharm
    Xue, Jiang
    2014 EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS (EUCNC), 2014,
  • [23] Wideband Channel Estimation for Millimeter Wave Beamspace MIMO
    Cheng, Xiantao
    Deng, Jin
    Li, Shaoqian
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (07) : 7221 - 7225
  • [24] Index Detection based Channel Estimation for Hybrid Massive MIMO MmWave Systems
    Fan, Dian
    Gao, Feifei
    Wang, Gongpu
    Zhong, Zhangdui
    Sidhu, Guftaar Ahmad Sardar
    Nallanathan, Arumugam
    ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
  • [25] Block Sparse Recovery for Wideband Channel Estimation in Hybrid mmWave MIMO Systems
    Zhang, Ruoyu
    Zhang, Jiayan
    Zhao, Tianyu
    Zhao, Honglin
    2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2018,
  • [26] Super-Resolution Channel Estimation for MmWave Massive MIMO With Hybrid Precoding
    Hu, Chen
    Dai, Linglong
    Mir, Talha
    Gao, Zhen
    Fang, Jun
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (09) : 8954 - 8958
  • [27] 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
  • [28] Channel Estimation for Hybrid Multi-Carrier mmWave MIMO Systems Using 3-D Unitary Tensor-ESPRIT in DFT beamspace
    Rakhimov, Damir
    Zhang, Jianshu
    de Almeida, Andre
    Nadeev, Adel
    Haardt, Martin
    CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, 2019, : 447 - 451
  • [29] 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
  • [30] 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)