Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems

被引:105
|
作者
Dovelos, Konstantinos [1 ]
Matthaiou, Michail [2 ]
Ngo, Hien Quoc [2 ]
Bellalta, Boris [1 ]
机构
[1] Pompeu Fabra Univ, Dept Informat & Commun Technol, Barcelona 08002, Spain
[2] Queens Univ Belfast, Inst Elect Commun & Informat Technol ECIT, Belfast BT7 1NN, Antrim, North Ireland
基金
英国工程与自然科学研究理事会;
关键词
Beam squint effect; compressive channel estimation; hybrid combining; massive MIMO; planar antenna arrays; wideband~THz communication; WIRELESS COMMUNICATIONS; SIGNAL RECOVERY; MMWAVE;
D O I
10.1109/JSAC.2021.3071851
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
Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies over the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.
引用
收藏
页码:1604 / 1620
页数:17
相关论文
共 50 条
  • [31] TeraMIMO: A Channel Simulator for Wideband Ultra-Massive MIMO Terahertz Communications
    Tarboush, Simon
    Sarieddeen, Hadi
    Chen, Hui
    Loukil, Mohamed Habib
    Jemaa, Hakim
    Alouini, Mohamed-Slim
    Al-Naffouri, Tareq Y.
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (12) : 12325 - 12341
  • [32] Sensing-Aided High-Efficiency Hybrid Precoding for Wideband Terahertz Massive MIMO Systems
    Zhou, Tianhang
    Wang, Yang
    Yang, Chuang
    Peng, Mugen
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 6504 - 6509
  • [33] Energy-Efficient Hybrid Beamforming Design for Wideband Terahertz Ultra-Massive MIMO Systems
    Shan, Shan
    Li, Yong
    Chen, Gaojie
    2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024, 2024,
  • [34] On Hybrid Pilot for Channel Estimation in Massive MIMO Uplink
    Li, Jiaming
    Yuen, Chau
    Li, Dong
    Wu, Xianda
    Zhang, Han
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (07) : 6670 - 6685
  • [35] An enhanced beamspace channel estimation algorithm for wideband millimeter-wave massive MIMO systems
    Yang Liu
    Kaipeng Song
    Yi Luo
    Ding Han
    Yinghui Zhang
    Minglu Jin
    EURASIP Journal on Advances in Signal Processing, 2022
  • [36] An enhanced beamspace channel estimation algorithm for wideband millimeter-wave massive MIMO systems
    Liu, Yang
    Song, Kaipeng
    Luo, Yi
    Han, Ding
    Zhang, Yinghui
    Jin, Minglu
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2022, 2022 (01)
  • [37] Investigation of Channel Correlation in Indoor Wideband Massive MIMO Systems
    Temiz, Murat
    Zhang, Yongwei
    Alsusa, Emad
    Danoon, Laith
    2019 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND USNC-URSI RADIO SCIENCE MEETING, 2019, : 1577 - 1578
  • [38] Deep Learning-Aided Parametric Sparse Channel Estimation for Terahertz Massive MIMO Systems
    Kim, Jinhong
    Ahn, Yongjun
    Kim, Seungnyun
    Shim, Byonghyo
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2024, 10 (06) : 2136 - 2148
  • [39] Simultaneous Channel Estimation and Localization of Terahertz Massive MIMO Systems via Bayesian Tensor Decomposition
    Du, Jianhe
    Dong, Jingyi
    Gao, Feifei
    IEEE COMMUNICATIONS LETTERS, 2023, 27 (02) : 541 - 545
  • [40] Intelligent Near-Field Channel Estimation for Terahertz Ultra-Massive MIMO Systems
    Lee, Anho
    Ju, Hyungyu
    Kim, Seungnyun
    Shim, Byonghyo
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 5390 - 5395