Reading Radio from Camera: Visually-Grounded, Lightweight, and Interpretable RSSI Prediction

TL;DR

This paper introduces a physics-guided, lightweight vision-based framework that predicts wireless RSSI from camera images, achieving high accuracy, robustness to interference, and suitability for real-time edge deployment.

Contribution

The authors propose a novel, interpretable, and computationally efficient model that decomposes RSSI into physical components, outperforming existing vision-based methods in accuracy and robustness.

Findings

Reduces RSSI prediction RMSE by 50.3% under normal conditions

Achieves 11.5% lower RMSE than previous benchmarks with interference

Uses a MobileNet-based model up to 19 times smaller than competitors

Abstract

Accurate, real-time wireless signal prediction is essential for next-generation networks. However, existing vision-based frameworks often rely on computationally intensive models and are also sensitive to environmental interference. To overcome these limitations, we propose a novel, physics-guided and light-weighted framework that predicts the received signal strength indicator (RSSI) from camera images. By decomposing RSSI into its physically interpretable components, path loss and shadow fading, we significantly reduce the model's learning difficulty and exhibit interpretability. Our approach establishes a new state-of-the-art by demonstrating exceptional robustness to environmental interference, a critical flaw in prior work. Quantitatively, our model reduces the prediction root mean squared error (RMSE) by 50.3% under conventional conditions and still achieves an 11.5% lower RMSE than the previous benchmark's interference-eliminated results. This superior performance is achieved with a remarkably lightweight framework, utilizing a MobileNet-based model up to 19 times smaller than competing solutions. The combination of high accuracy, robustness to interference, and computational efficiency makes our framework highly suitable for real-time, on-device deployment in edge devices, paving the way for more intelligent and reliable wireless communication systems.