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.
Cite
@article{arxiv.2010.01868,
title = {Non-Linear Self-Interference Cancellation via Tensor Completion},
author = {Freek Jochems and Alexios Balatsoukas-Stimming},
journal= {arXiv preprint arXiv:2010.01868},
year = {2020}
}
Comments
To be presented at the 2020 Asilomar Conference for Signals, Systems, and Computers