This chapter works on designing an efficient channel estimator for hybrid mmWave massive multiple-input multiple-output (mMIMO) systems. The proposed doubly-sparse approach relies on a judiciously designed training pattern to decouple the convoluted channel. By doing so, it becomes convenient to exploit the under-investigated channel sparsity in the delay domain together with the well-known beamspace sparsity. Furthermore, dedicated probing strategies are accordingly developed to ensure compatibility with the hybrid structure while utilizing double sparsity. Compared with existing alternatives, the proposed mmWave channel estimator works exceptionally in doubly-selective (frequency-time) channels and can hugely reduce the training overhead, storage demand, and computational complexity thanks to the exploitation of double (delay-beamspace) sparsity.


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    Title :

    Millimeter-Wave Vehicular Channel Estimation


    Additional title:

    Wireless Networks


    Contributors:
    Cheng, Xiang (author) / Gao, Shijian (author) / Yang, Liuqing (author)


    Publication date :

    2022-05-03


    Size :

    23 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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