Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios

Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

In-band full-duplex systems allow for more efficient use of temporal and spectral resources by transmitting and receiving information at the same time and on the same frequency. However, this creates a strong self-interference signal at the receiver, making the use of self-interference cancellation critical. Recently, neural networks have been used to perform digital self-interference with lower computational complexity compared to a traditional polynomial model. In this paper, we examine the use of advanced neural networks, such as recurrent and complex-valued neural networks, and we perform an in-depth network architecture exploration. Our neural network architecture exploration reveals that complex-valued neural networks can significantly reduce both the number of floating-point operations and parameters compared to a polynomial model, whereas the real-valued networks only reduce the number of floating-point operations. For example, at a digital self-interference cancellation of 44.51 dB, a complex-valued neural network requires 33.7% fewer floating-point operations and 26.9% fewer parameters compared to the polynomial model.

Original languageEnglish
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1149-1153
Number of pages5
ISBN (Electronic)9781728143002
DOIs
Publication statusPublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: 3 Nov 20196 Nov 2019

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period3/11/196/11/19

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