Non-Linear Self-Interference Cancellation via Tensor Completion

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.

PIM Detection in Wireless Networks as an Anomaly Detection Problem

“…Some studies focus on utilizing Machine Learning (ML) models for detecting PIM problems automatically, without requiring manual labor. One such study by Jochems et al [11] suggests using a newly developed ML method, called canonical system identification, for cancelling self-interference signals, which include the PIM problem. Mismar et al propose an ML-based algorithm utilizing supervised learning for detecting PIM problems in Beyond 5G and 6G networks [12].…”