Highly accurate millimeter wave channel estimation in massive MIMO system

被引:1
|
作者
Zhang, Beibei [1 ]
Xu, Peng [1 ]
Qiao, Bo [2 ]
Wei, Ziping [2 ]
Li, Bin [2 ]
Zhao, Chenglin [2 ]
机构
[1] Jiangsu Automat Res Inst, Lianyungang, Jiangsu, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
channel estimation; millimetre waves; CELLULAR WIRELESS; NETWORKS; FEEDBACK;
D O I
10.1049/cmu2.12569
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
Accurate channel state information (CSI) is extremely crucial to realize high-accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low-rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low-rank property of massive multiple input multiple output (MIMO) channel, one non-convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non-convex problem is reformulated as two simple sub-problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low-rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub-problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme.
引用
收藏
页码:670 / 680
页数:11
相关论文
共 50 条
  • [31] Wideband Channel Estimation for Millimeter Wave Beamspace MIMO
    Cheng, Xiantao
    Deng, Jin
    Li, Shaoqian
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (07) : 7221 - 7225
  • [32] Millimeter-wave massive MIMO channel estimation based on majorization-minimization approach
    Raj, S. Merlin Gilbert
    Bala, G. Josemin
    PHYSICAL COMMUNICATION, 2021, 47
  • [33] Partially Coherent Compressive Phase Retrieval for Millimeter-Wave Massive MIMO Channel Estimation
    Hu, Chen
    Wang, Xiaodong
    Dai, Linglong
    Ma, Junjie
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 : 1673 - 1687
  • [34] On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System
    Srivastav, Prateek Saurabh
    Chen, Lan
    Wahla, Arfan Haider
    ENTROPY, 2020, 22 (10) : 1 - 18
  • [35] Millimeter Wave MIMO Channel Estimation With One-Bit Receivers
    Zhou, Runxin
    Du, Huiqin
    Zhang, Duoying
    IEEE COMMUNICATIONS LETTERS, 2022, 26 (01) : 158 - 162
  • [36] An optimized deep learning model for a highly accurate DOA and channel estimation for massive MIMO systems
    Pabbati, Omkar H.
    Joshi, Rutvij C.
    INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2024, 37 (16)
  • [37] A Tensor-Based High Resolution Millimeter Wave Massive MIMO Channel Parameters Estimation Scheme
    Hong, Junkang
    Wang, Jianhao
    Liu, Congjie
    Sun, Jian
    Zhang, Wensheng
    Wang, Cheng-Xiang
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 5060 - 5065
  • [38] Low-Complexity Downlink Channel Estimation for Millimeter-Wave FDD Massive MIMO Systems
    Wu, Xianda
    Yang, Guanghua
    Hou, Fen
    Ma, Shaodan
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2019, 8 (04) : 1103 - 1107
  • [39] Clustered Sparse Bayesian Learning Based Channel Estimation for Millimeter-Wave Massive MIMO Systems
    Wu, Xianda
    Ma, Shaodan
    Yang, Xi
    Yang, Guanghua
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (12) : 12749 - 12764
  • [40] Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL
    Hou, Shuai
    Wang, Yafeng
    Li, Chao
    SENSORS, 2021, 21 (14)