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    Rician K-Factor-Based Analysis of XLOS Service Probability in 5G Outdoor Ultra-Dense Networks

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    Type
    Article
    Authors
    Chergui, Hatim
    Benjillali, Mustapha
    Alouini, Mohamed-Slim cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2018-10-09
    Preprint Posting Date
    2018-04-22
    Online Publication Date
    2018-10-09
    Print Publication Date
    2019-04
    Permanent link to this record
    http://hdl.handle.net/10754/629969
    
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    Abstract
    In this letter, we introduce the concept of Rician K-factor-based radio resource and mobility management for fifth generation (5G) ultra-dense networks (UDN), where the information on the gradual visibility between the new radio node B (gNB) and the user equipment (UE)—dubbed X-line-of-sight (XLOS)—would be required. We therefore start by presenting the XLOS service probability as a new performance indicator; taking into account both the UE serving and neighbor cells. By relying on a lognormal K-factor model, a parametric expression of the XLOS service probability in a 5G outdoor UDN is derived, where the link between network parameters and the availability of a XLOS condition is established. The obtained formula is given in terms of the multivariate Fox H-function, wherefore we develop a fast graphical processing unit (GPU)-enebled MATLAB code. Residue theory is then applied to infer the relevant asymptotic behavior and show its practical implications. Finally, numerical results are provided for various network configurations, and underpinned by extensive Monte-Carlo simulations.
    Citation
    Chergui H, Benjillali M, Alouini M-S (2018) Rician K-Factor-Based Analysis of XLOS Service Probability in 5G Outdoor Ultra-Dense Networks. IEEE Wireless Communications Letters: 1–1. Available: http://dx.doi.org/10.1109/LWC.2018.2874654.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Wireless Communications Letters
    DOI
    10.1109/LWC.2018.2874654
    arXiv
    arXiv:1804.08101
    Additional Links
    https://ieeexplore.ieee.org/document/8486668
    ae974a485f413a2113503eed53cd6c53
    10.1109/LWC.2018.2874654
    Scopus Count
    Collections
    Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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