Non-Linear Self-Interference Cancellation via Tensor Completion
This addresses a fundamental problem in full-duplex communications, but it is incremental as it adapts an existing method to a specific domain.
The paper tackled non-linear self-interference cancellation in full-duplex communications by applying a low-rank tensor completion method called CSID, resulting in effective modeling and cancellation with lower computational complexity but increased memory requirements.
Non-linear self-interference (SI) cancellation constitutes a fundamental problem in full-duplex communications, which is typically tackled using either polynomial models or neural networks. In this work, we explore the applicability of a recently proposed method based on low-rank tensor completion, called canonical system identification (CSID), to non-linear SI cancellation. Our results show that CSID is very effective in modeling and cancelling the non-linear SI signal and can have lower computational complexity than existing methods, albeit at the cost of increased memory requirements.