Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz

Jakob Thrane, Darko Zibar, Henrik Lehrmann Christiansen

    Research output: Contribution to journalJournal articleResearchpeer-review

    703 Downloads (Pure)

    Abstract

    Accurate channel models are essential to evaluate mobile communication system performance and optimize coverage for existing deployments. The introduction of various transmission frequencies for 5G imposes new challenges for accurate radio performance prediction. This paper compares traditional channel models to a channel model obtained using Deep Learning (DL)-techniques utilizing satellite images aided by a simple path loss model. Experimental measurements are gathered and compose the training and test set. This paper considers path loss modelling techniques offered by state-of-the-art stochastic models and a ray-tracing model for comparison and evaluation. The results show that 1) the satellite images offer an increase in predictive performance by ≈ 0.8 dB, 2) The model-aided technique offers an improvement of ≈ 1 dB, and 3) that the proposed DL model is capable of improving path loss prediction at unseen locations for 811 MHz with ≈ 1 dB and ≈ 4.7 dB for 2630 MHz.
    Original languageEnglish
    Article number8950164
    JournalIEEE Access
    Volume8
    Pages (from-to)7925 - 7936
    ISSN2169-3536
    DOIs
    Publication statusPublished - 2020

    Keywords

    • 5G Mobile Communication
    • Channel models
    • Wireless communication
    • Computer vision
    • Machine learning
    • Supervised learning

    Fingerprint

    Dive into the research topics of 'Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz'. Together they form a unique fingerprint.

    Cite this