DRFC

Xihua Sheng, Peilin Chen, Shiqi Wang, Dapeng Oliver Wu

Abstract: Radio frequency (RF) signals have gained widespread adoption in intelligent perception systems due to their unique advantages, including non-line-of-sight propagation capability, robustness in low-light environments, and inherent privacy preservation. However, their substantial data volumes, generated by the dual-polarization direction characteristic, result in significant challenges to data storage and transmission. To address this, we propose the first end-to-end deep dynamic RF signal compression (DRFC) framework, which primarily focuses on exploiting cross-directional correlation in dynamic RF signals. The proposed framework incorporates four key innovations: (1) a mask-guided RF motion estimation module that leverages Doppler shifts and electromagnetic noise characteristics to identify regions of significant motion using a threshold-based mask, significantly improving motion estimation accuracy; (2) a cross-directional RF motion entropy model that utilizes cross-directional RF motion latent priors to refine the probability distribution for motion entropy coding; (3) a cross-directional RF context mining module that predicts RF contexts from temporal and cross-directional reference signals, adaptively fusing these contexts with confidence maps to maximize complementary information utilization; and (4) a cross-directional RF contextual entropy model that incorporates cross-directional RF contextual latent priors to optimize contextual entropy modeling. Experimental results demonstrate the superiority of our framework over existing codecs. Our DRFC framework achieves significant bitrate savings on benchmark datasets, establishing a strong baseline for future research in this field. © 1991-2012 IEEE.

External IDs:doi:10.1109/tcsvt.2025.3596840