Multi-Attribute Wireless Interference Identification Under Undersampling: A Multi-Domain Fusion Model Using Domain-Specific Hybrid Sampling

Zehui Zhang

doi:10.1109/TCOMM.2025.3649708

2026-01-01

PRE

DOI：10.1109/TCOMM.2025.3649708

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摘要

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Diverse and complex wireless interference is one of the most critical threats to modern wireless communication systems. The continuous shift of wireless interference toward higher frequency and wider bandwidth imposes sampling-rate limitations on wireless interference identification (WII). Existing compressed sensing-based WII methods designed for undersampling scenarios suffer from high-complexity signal reconstruction and low identification accuracy caused by fixed sampling strategies. To address these challenges, we propose a multi-domain fusion model which employs domain-specific hybrid sampling to extract interference features for multi-attribute WII without signal reconstruction. Given the respective classification advantages of random and uniform undersampling in time-domain sequences and time–frequency images, this paper proposes a dual-branch architecture to exploit their joint benefits. Specifically, we propose a learnable sparse sampler combined with a Transformer to extract time-domain features in the random undersampling branch. We further prove that the Restricted Isometry Property (RIP)-compliant undersampling largely preserves the feature class discriminability, which motivates the introduction of the RIP loss. In parallel, we propose a multi-scale feature extraction module and a cross-fusion module for dimensionality reduction in the uniform undersampling branch. Subsequently, an attribute correlation-driven graph convolution network is introduced to classify the fused features from both branches, further improving WII performance. Finally, we propose an adaptive multi-domain binary cross-entropy loss and prove that the optimal weight of the RIP loss effectively mitigates gradient conflicts. Experimental results show that the proposed model improves precision by 25.1% over the state-of-the-art, while maintaining effective multi-attribute WII performance at undersampling ratios up to 8.

Keyword:

Interference identification

multi-attribute classification

multi-domain fusion

undersampling