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arXiv · 2010.01868

Non-Linear Self-Interference Cancellation via Tensor Completion

Freek Jochems·Alexios Balatsoukas-Stimming
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Abstract

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.

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Freek Jochems, Alexios Balatsoukas-Stimming. 2020-10-05. Non-Linear Self-Interference Cancellation via Tensor Completion. https://arxiv.org/abs/2010.01868

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