SCIENCE@home

Patch Matter: Dual Modality Patch Contrastive for Non-Stationary Radio Signals

Jie Su,Yuting Jiang,Yuheng Ye,Zhenyu Wen,Taotao Li,Shibo He,Xiaoqin Zhang,R. Ranjan

Published 2026 in IEEE Transactions on Mobile Computing

ABSTRACT

The emergence of abundant non-stationary radio signal (NSRS) data presents significant opportunities for applications in wireless communications, radar systems, remote sensing, and healthcare. While deep learning models have shown promise in capturing sequence dependencies, deriving generic and fine-grained representations of NSRS data remains challenging due to its complex, dynamic nature and the scarcity of labeled data. The NSRS data are often frequency-sensitive and exhibit minuscule inter-class distances, posing significant challenges for precise classification. To address these issues, we propose a novel Dual Modality Patch Contrastive (DMPC) framework. This framework leverages a stochastic patching paradigm for diverse local pattern extraction and a time-frequency cross-view optimization for frequency-sensitive feature mining. Furthermore, an Attentive Patch Aggregation (APA) mechanism enhances fine-grained inference under few-shot conditions through patch-level feature voting. Extensive experiments demonstrate the effectiveness of our approach in addressing the unique challenges of NSRS data.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-41 of 41 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1