Joint Interference Detection and Identification via Adversarial Multi-task Learning
Summary: arXiv:2604.08607v1 Announce Type: cross
In the ever-evolving landscape of wireless communication systems, the ability to precisely detect and identify interference is paramount for enhancing the survivability of these systems, particularly in non-cooperative environments. Traditional approaches have utilized deep learning (DL) techniques, yet many of these rely on single-task learning (STL), which often overlooks the correlations inherent among various tasks. Moreover, while multi-task learning (MTL) has emerged as a promising avenue, many existing methods lack a robust theoretical framework for quantifying and modeling these task relationships.
Introduction to the Theoretical Framework
This study proposes a comprehensive MTL framework aimed at facilitating joint interference detection, modulation identification, and interference identification. The researchers begin by deriving an upper bound for the weighted expected loss in MTL frameworks. This upper bound serves to explicitly link MTL performance with task similarity, which is quantified through the Wasserstein distance alongside learnable task relation coefficients. This theoretical foundation is critical in ensuring that the learning process accurately reflects the relationships between the various tasks involved.
The AMTIDIN Model
Building on this theoretical groundwork, the research introduces an innovative model known as the Adversarial Multi-task Interference Detection and Identification Network (AMTIDIN). This model employs adversarial training techniques to minimize distributional discrepancies across the different tasks involved in interference detection and identification. Furthermore, it utilizes adaptive coefficients, enabling the dynamic modeling of task correlations during the learning process.
Insights on Task Similarity
One of the most intriguing discoveries made during this research was a quantitative analysis of task similarity, which unveiled intrinsic relationships among the tasks. Specifically, it was found that modulation identification and interference identification share a significant feature overlap, which is distinct from the interference detection task. This insight is essential for understanding how to better structure multi-task learning approaches in this field.
Experimental Results
To validate the effectiveness of AMTIDIN, the researchers conducted extensive comparative experiments. The results indicated that AMTIDIN significantly outperforms both the traditional task-specific STL baseline as well as state-of-the-art MTL baselines. Notably, the model demonstrated superior robustness and generalization capabilities, particularly in challenging scenarios characterized by:
- Limited training data
- Short signal lengths
- Low signal-to-noise ratios (SNRs)
Conclusion
The findings from this research highlight the potential of adversarial multi-task learning frameworks in the field of wireless communication. By effectively modeling task correlations and leveraging theoretical foundations, AMTIDIN sets a new benchmark for interference detection and identification, paving the way for more resilient communication systems in increasingly challenging environments.