Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing

TL;DR

This paper introduces RF-CMG, a diffusion-based method that synthesizes high-fidelity RF data for scarce modalities like mmWave and RFID using abundant WiFi data, improving generative quality and downstream tasks.

Contribution

RF-CMG decouples cross-modal generation into high-frequency guidance and low-frequency constraints, enabling high-quality synthesis with limited target modality data.

Findings

RF-CMG outperforms existing generative models in RFID and mmWave signal synthesis.

Synthetic data from RF-CMG improves gesture recognition accuracy.

The method effectively suppresses structural biases during generation.

Abstract

AIGC has shown remarkable success in CV and NLP, and has recently demonstrated promising potential in the wireless domain. However, significant data imbalance exists across RF modalities, with abundant WiFi data but scarce mmWave and RFID data due to high acquisition cost. This makes it difficult to train high-quality generative models for these data-scarce modalities. In this work, we propose RF-CMG, a diffusion-based cross-modal generative method that leverages data-rich WiFi signals to synthesize high-fidelity RF data for scarce modalities including mmWave and RFID. The key insight of RF-CMG is to decouple cross-modal generation into high-frequency guidance and low-frequency constraint, which respectively learn high-frequency distribution from limited target modality data and preserve the underlying physical structure via low-frequency constraints during generation. On this basis, we introduce a Modality-Guided Embedding (MGE) module to steer the reverse diffusion trajectory toward the target high-frequency distribution, and a Low-Frequency Modality Consistency (LFMC) module to progressively enforce low-frequency constraints to suppress the accumulation of source-modality structural biases during inference, enabling high-quality target-modality generation. Performance comparison with several prevalent generative models demonstrates that RF-CMG achieves superior performance in synthesizing RFID and mmWave signals. We further showcase the effectiveness of the data generated by RF-CMG in gesture recognition tasks, and analyze the impact of the proportion of synthetic data on downstream performance.