On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, Andreas Burg

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

Abstract

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.

Original languageEnglish
Title of host publication2020 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers
Pages969-973
Number of pages5
ISBN (Electronic)9780738131269
DOIs
Publication statusPublished - 3 Jun 2021
Event54th Asilomar Conference on Signals, Systems and Computers (ACSSC 2020) - Pacific Grove, United States
Duration: 1 Nov 20204 Nov 2020

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers (ACSSC 2020)
Country/TerritoryUnited States
CityPacific Grove
Period1/11/204/11/20

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