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    Mobile millimeter wave channel tracking: A bayesian beamforming framework against DOA uncertainty

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    Type
    Conference Paper
    Authors
    Yang, Yan
    Dang, Shuping
    Wen, Miaowen
    Mumtaz, Shahid
    Guizani, Mohsen
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-02-28
    Permanent link to this record
    http://hdl.handle.net/10754/662366
    
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    Abstract
    A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.
    Citation
    Yang, Y., Dang, S., Wen, M., Mumtaz, S., & Guizani, M. (2019). Mobile Millimeter Wave Channel Tracking: A Bayesian Beamforming Framework against DOA Uncertainty. 2019 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/globecom38437.2019.9013620
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2019 IEEE Global Communications Conference, GLOBECOM 2019
    DOI
    10.1109/GLOBECOM38437.2019.9013620
    Additional Links
    https://ieeexplore.ieee.org/document/9013620/
    ae974a485f413a2113503eed53cd6c53
    10.1109/GLOBECOM38437.2019.9013620
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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