# Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction

arXiv: https://arxiv.org/abs/2504.03625 · HTML: https://mlsanity.com/paper/2504.03625/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.17752/) (2024)
- [Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks](https://mlsanity.com/paper/2501.07534/) (2025)

## Abstract (arXiv)

Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequency spectrum and minimize unwanted interference. Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. Drive tests primarily represent downlink scenarios, where the Tx is located on a building and the Rx is located on a moving vehicle. Consequently, trained models are frequently reserved for downlink coverage estimation, lacking representation of uplink scenarios. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios, training using only downlink drive test measurements. By adding a small number of synthetic samples representing uplink scenarios to the training set, root mean squared error is reduced by > 8 dB on uplink examples in the test set.