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Machine Learning-Based Urban Canyon Path Loss Prediction Using 28 GHz Manhattan Measurements.
- Source :
-
IEEE Transactions on Antennas & Propagation . Jun2022, Vol. 70 Issue 6, p4096-4111. 16p. - Publication Year :
- 2022
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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