Related papers: Non-Linear Self-Interference Cancellation via Tensor Completion

Non-Linear Self-Interference Cancellation via Tensor Completion

URL: http://arxiv.org/abs/2010.01868v1
Date: Mon, 5 Oct 2020 09:08:28 GMT
Title: Non-Linear Self-Interference Cancellation via Tensor Completion
Authors: Freek Jochems and Alexios Balatsoukas-Stimming
Abstract summary: We propose a method based on low-rank tensor completion called canonical system identification (CSID) Our results show that CSID is very effective in modeling cancelling non-linear SI signal and can have lower computational complexity than existing methods.
Score: 7.9264657672268894
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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