arXiv：cs.LG· Zhijie Su, Shiquan Zhang, Qiaolin He·· 10 小时前
基于 Transformer 神经算子的带内全双工系统数字自干扰消除
Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators
AI 导读
该研究将带内全双工系统的数字域自干扰消除重构为算子学习问题，采用 Transformer 神经算子从历史观测中学习残余自干扰信道的时序演化，结合已知本地发射信号重建当前残余自干扰，并在无期望信号条件下完成训练。在 30 dB SIR 条件下，该方法相较所对比的传统方法将消除性能提升 5–15 dB。
正文
Abstract:Digital-domain self-interference cancellation is essential in in-band full-duplex radios after passive suppression and analog cancellation. It is particularly challenging when the desired signal is strong relative to the residual self-interference. In this regime, conventional iterative digital cancellers update their coefficients from desired-signal-contaminated received samples and may suffer substantial performance degradation. This paper reformulates digital-domain self-interference cancellation as an operator-learning problem. The proposed framework learns the temporal evolution of the residual self-interference channel from historical observations and uses the learned operator together with the known local transmit signal to reconstruct the current residual self-interference. A Transformer neural operator is used to realize this framework. The resulting method is trained in a desired-signal-free setting and then applied in the presence of a desired signal. A continuity-based analysis characterizes the sensitivity of the learned operator to the resulting input perturbation. Numerical experiments in static and time-varying channels, including time-varying SIR conditions, demonstrate stable cancellation performance over a wide range of interference levels. At 30 dB SIR, the proposed method improves the cancellation performance by 5--15 dB over the considered conventional methods.
| Comments: | 24 pages, 13 figures |
| Subjects: | Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19490 [cs.IT] |
| (or arXiv:2610.19490v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19490 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhijie Su [view email]
[v1]
Wed, 14 Oct 2026 03:11:47 UTC (598 KB)
来源：arXiv：cs.LG · arxiv.org