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Machine Learning-Based Urban Canyon Path Loss Prediction Using 28 GHz Manhattan Measurements.

Authors :
Gupta, Ankit
Du, Jinfeng
Chizhik, Dmitry
Valenzuela, Reinaldo A.
Sellathurai, Mathini
Source :
IEEE Transactions on Antennas & Propagation. Jun2022, Vol. 70 Issue 6, p4096-4111. 16p.
Publication Year :
2022

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0018926X
Volume :
70
Issue :
6
Database :
Academic Search Index
Journal :
IEEE Transactions on Antennas & Propagation
Publication Type :
Academic Journal
Accession number :
157490572
Full Text :
https://doi.org/10.1109/TAP.2022.3152776