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

Lookup NU author(s): Professor Raj Ranjan

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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 metadata

Author(s): Su J, Jiang Y, Ye Y, Wen Z, Li T, He S, Zhang X, Ranjan R

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Mobile Computing

Year: 2025

Pages: Epub ahead of print

Online publication date: 17/11/2025

Acceptance date: 02/04/2018

Date deposited: 10/12/2025

ISSN (print): 1536-1233

ISSN (electronic): 1558-0660

Publisher: IEEE

URL: https://doi.org/10.1109/TMC.2025.3633263

DOI: 10.1109/TMC.2025.3633263

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