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

arXiv: https://arxiv.org/abs/2202.05107 · HTML: https://mlsanity.com/paper/2202.05107/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2022.

## Built on by (most cited first, 8 of 8)

- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Learning Radio Environments by Differentiable Ray Tracing](https://mlsanity.com/paper/2311.18558/) (2023)
- [Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps](https://mlsanity.com/paper/2310.04570/) (2023)
- [Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.17752/) (2024)
- [Site-specific Deep Learning Path Loss Models based on the Method of Moments](https://mlsanity.com/paper/2302.01052/) (2023)
- [AIRMap: AI-Generated Radio Maps for Wireless Digital Twins](https://mlsanity.com/paper/2511.05522/) (2025)
- [A Robust Machine Learning Approach for Path Loss Prediction in 5G Networks with Nested Cross Validation](https://mlsanity.com/paper/2310.01030/) (2023)
- [Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction](https://mlsanity.com/paper/2402.01969/) (2024)

## Abstract (arXiv)

Large bandwidth at mm-wave is crucial for 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 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 3D-building information using 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 (RMSE) of 4.8 ± 1.1 dB compared to 10.6 ± 4.4 dB and 6.5 ± 2.0 dB for 3GPP LOS and slope-intercept prediction, respectively, where the standard deviation indicates street-by-street variation. By only using four most influential clutter features, RMSE of 5.5± 1.1 dB is achieved.