Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators
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 toget...
| Authors | Zhijie Su, Shiquan Zhang, Qiaolin He |
|---|---|
| Primary category | cs.IT |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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