RF-Prox: Radio-Based Proximity Estimation of Nondirectly Connected Devices

Yuchong Gao, Guoxuan Chi, Zheng Yang, Shijie Cheng, Zhiqing Wei

Abstract: Recent years have witnessed an increasing number of mobile devices, posing a more diversified demand for device localization solutions. Existing methods can locate connected devices but fail to address the spatial proximity between devices lacking direct communication links. This limitation impedes numerous emerging applications, such as implicit control of IoT device and proximity-based autonomous aerial vehicles scheduling. In response to this technical challenge, we introduce RF-Prox, the pioneering system designed for the proximity estimation of nondirectly connected devices. RF-Prox determines the proximity between devices by extracting and analyzing the spatiotemporal correlation between two signals. RF-Prox introduces a multiresolution spatiotemporal encoder (MRSTE) that extracts multiscale features from complex-valued wireless signals, capturing both spatial and dynamic temporal characteristics. Additionally, the proximity metric adaptation network (PMAN) bridges the gap between high-dimensional signal characteristics and physical proximity. To enhance scalability, we leverage a transfer learning framework, significantly reducing the need for extensive data collection and retraining. Extensive experiments demonstrate RF-Prox’s outstanding performance across Wi-Fi and cellular networks, achieving fine-tuned accuracy rates of 98.6% indoors and 91.3% outdoors. Even without fine-tuning, the pretrained model achieves strong zero-shot performance, showcasing its exceptional performance in both proximity estimation accuracy and domain generalizability.

External IDs:dblp:journals/iotj/GaoCYCW25