e-space
Manchester Metropolitan University's Research Repository

    Path loss modelling at 60 GHz mmWave based on cognitive 3D ray tracing algorithm in 5G

    Kamboh, UR, Ullah, U, Khalid, S, Raza, U ORCID logoORCID: https://orcid.org/0000-0002-9810-1285, Chakraborty, C and Al-Turjman, F (2021) Path loss modelling at 60 GHz mmWave based on cognitive 3D ray tracing algorithm in 5G. Peer-to-Peer Networking and Applications, 14 (5). pp. 3181-3197. ISSN 1936-6450

    [img]
    Preview
    Accepted Version
    Download (54MB) | Preview

    Abstract

    The objective of the study is to consider the foremost high-tech issue of mobile radio propagation i.e. path loss for an outdoor and indoor environment for mmWave in a densely populated area.60 [GHz] mmWave is a win-win for the 5th Generation radio network. Several measurements and simulations are performed using the simulator “Smart Cognitive 3D Ray Tracer” build in MATLAB. Two of the main parameters (pathloss and received signal strength (RSS)) of the radio propagation are obtained in this study. To compute the pathloss and RSS, 5G 3GPP mobile propagation model is selected due to its flexibility of scenario and conditions beyond 6 GHz frequency. For indoor simulations, we again chose 5G 3GPP mobile propagation model. It is evident from the recent previous studies that there is still not enough findings in the ray tracing specially cognitive 3D ray tracing. The suggested alternative cognitive algorithm here deals with less iterations and effective use of resources. The conclusions of this work also comprise that the path loss is reliant on separation distance of base station and receiver. The above mentioned frequency and interconnected distance reported here provide better knowledge of mobile radio channel attributes and can be also used to design and estimate the performance of the future generation (5G) mobile networks.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    44Downloads
    6 month trend
    41Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record