# Prediction of Acoustic Communication Performance for AUVs using Gaussian Process Classification

arXiv: https://arxiv.org/abs/2411.07933 · HTML: https://mlsanity.com/paper/2411.07933/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [Reinforcement Learning for Relation Classification from Noisy Data](https://mlsanity.com/paper/1808.08013/) (2018)
- [Streaming Sparse Gaussian Process Approximations](https://mlsanity.com/paper/1705.07131/) (2017)
- [Multi-class Gaussian Process Classification with Noisy Inputs](https://mlsanity.com/paper/2001.10523/) (2020)
- [Remote Sensing Image Classification with Large Scale Gaussian Processes](https://mlsanity.com/paper/1710.00575/) (2017)
- [PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration](https://mlsanity.com/paper/2205.01267/) (2022)

## Built on by (most cited first, 1 of 1)

- [Decentralized Gaussian Process Classification and an Application in Subsea Robotics](https://mlsanity.com/paper/2511.15529/) (2025)

## Abstract (arXiv)

Cooperating autonomous underwater vehicles (AUVs) often rely on acoustic communication to coordinate their actions effectively. However, the reliability of underwater acoustic communication decreases as the communication range between vehicles increases. Consequently, teams of cooperating AUVs typically make conservative assumptions about the maximum range at which they can communicate reliably. To address this limitation, we propose a novel approach that involves learning a map representing the probability of successful communication based on the locations of the transmitting and receiving vehicles. This probabilistic communication map accounts for factors such as the range between vehicles, environmental noise, and multi-path effects at a given location. In pursuit of this goal, we investigate the application of Gaussian process binary classification to generate the desired communication map. We specialize existing results to this specific binary classification problem and explore methods to incorporate uncertainty in vehicle location into the mapping process. Furthermore, we compare the prediction performance of the probability communication map generated using binary classification with that of a signal-to-noise ratio (SNR) communication map generated using Gaussian process regression. Our approach is experimentally validated using communication and navigation data collected during trials with a pair of Virginia Tech 690 AUVs.