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    Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems

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    Type
    Conference Paper
    Authors
    Wan, Ziwei
    Gao, Zhen
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2020
    Online Publication Date
    2020-07-27
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/664500
    
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    Abstract
    This paper investigates the broadband channel estimation (CE) for intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) massive MIMO systems. The CE for such systems is a challenging task due to the large dimension of both the active massive MIMO at the base station (BS) and passive IRS. To address this problem, this paper proposes a compressive sensing (CS)-based CE solution for IRS-aided mmWave massive MIMO systems, whereby the angular channel sparsity of large-scale array at mmWave is exploited for improved CE with reduced pilot overhead. Specifically, we first propose a downlink pilot transmission framework. By designing the pilot signals based on the prior knowledge that the line-of-sight dominated BS-to-IRS channel is known, the high-dimensional channels for BS-to-user and IRS-to-user can be jointly estimated based on CS theory. Moreover, to efficiently estimate broadband channels, a distributed orthogonal matching pursuit algorithm is exploited, where the common sparsity shared by the channels at different subcarriers is utilized. Additionally, the redundant dictionary to combat the power leakage is also designed for the enhanced CE performance. Simulation results demonstrate the effectiveness of the proposed scheme.
    Citation
    Wan, Z., Gao, Z., & Alouini, M.-S. (2020). Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). doi:10.1109/icc40277.2020.9149146
    Publisher
    IEEE
    Conference/Event name
    ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
    ISBN
    978-1-7281-5090-1
    DOI
    10.1109/ICC40277.2020.9149146
    arXiv
    2002.01629
    Additional Links
    https://ieeexplore.ieee.org/document/9149146/
    https://ieeexplore.ieee.org/document/9149146/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9149146
    http://arxiv.org/pdf/2002.01629
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICC40277.2020.9149146
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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