Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements

Ankit Gupta, Jinfeng Du, Dmitry Chizhik, Reinaldo A. Valenzuela, Mathini Sellathurai

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
58 Downloads (Pure)


Large bandwidth at millimeter wave (mm-wave) is crucial for fifth generation (5G) and beyond, but the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen in urban canyons, whereas ray tracing, capable of characterizing site-specific features, faces challenges in describing foliage and street clutter and associated reflection/diffraction ray calculation. Machine learning (ML) is promising but faces three key challenges in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to new streets; 3) overwhelmingly complex features/models. We propose an ML-based urban canyon PL prediction model based on extensive 28 GHz measurements from Manhattan where street clutters are modeled via a light detection and ranging (LiDAR) point cloud dataset and buildings by a mesh-grid building dataset. We extract expert knowledge-driven street clutter features from the point cloud and aggressively compress the 3-D building information using a convolutional autoencoder. Using a new street-by-street training and testing procedure to improve generalizability, the proposed model using both clutter and building features achieves a prediction error [root-mean-square error (RMSE)] of 4.8 ± 1.1 dB compared to 10.6 ± 4.4 and 6.5 ± 2.0 dB for 3GPP line of sight (LOS) and slope-intercept prediction, respectively, where the standard deviation indicates street-by-street variation. By only using four most influential clutter features, the RMSE of 5.5 ± 1.1 dB is achieved.

Original languageEnglish
Pages (from-to)4096-4111
Number of pages16
JournalIEEE Transactions on Antennas and Propagation
Issue number6
Early online date28 Feb 2022
Publication statusPublished - Jun 2022


  • Machine learning (ML)
  • Mesh grid
  • Millimeter wave (mm-wave)
  • Path loss (PL) prediction
  • Point cloud
  • Urban street canyon

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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