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Adaptive Nonlinear Digital Self-Interference Cancellation for Full-Duplex Wireless Systems Using Hypernetwork-Based Incremental Learning
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DOI：10.1109/TMLCN.2025.3639365.png)
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Abstract
En 中文
Achieving effective self-interference cancellation (SIC) in full-duplex (FD) wireless communication systems under time-varying channel conditions remains a significant challenge. To address this challenge, we propose a novel adaptive SIC solution through leveraging Hyper Neural Networks (HyperNet) and incremental learning (IL). Unlike the existing methods that rely on offline training or lack real-time adaptability, our approach enables autonomous learning and fast adaptation to the complex, nonlinear, and time-varying nature of self-interference (SI) channels. It effectively addresses dynamic adaptation challenges, such as catastrophic forgetting, through the use of experience replay (ER). Our experimental results show that traditional model-based methods exhibit limited adaptability under dynamic channel conditions, while conventional data-driven models fail to maintain consistent performance without the adaptive capabilities provided by IL. In contrast, the proposed HyperNet-based IL model reduces training time by 33% and achieves three times faster convergence compared to a standalone HyperNet trained separately for each static condition. Extensive evaluations using simulated datasets that emulate real-world scenarios demonstrate that our approach consistently achieves SI suppression down to the noise floor. It also delivers significantly lower computational complexity and training time. These improvements collectively enhance the efficiency and reliability of FD communication systems operating in dynamic wireless environments.
Keywords:
Interference cancellation
Adaptation models
Computational modeling
Artificial neural networks
Wireless communication
Mathematical models
Vehicle dynamics
Incremental learning
Full-duplex system
Training
Full-duplex
hyper neural network
incremental learning
self-interference cancellation
time-varying channel
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