Robust Low Complexity Digital Self Interference Cancellation for Multi Channel Full Duplex Systems

Shachar Shayovitz, Dan Raphaeli

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

Abstract

Self interference in a communications system occurs when there is electromagnetic coupling between the transmission (TX) and reception (RX) radio frequency (RF) chains or antennas. This coupling degrades the system's RX sensitivity to incoming signals. In this paper a low complexity technique for self interference cancellation in multi channel systems is presented. In this scenario, multiple carriers at overlapping arbitrary bandwidths and powers are simultaneously received and transmitted by the system. Traditional algorithms for self-interference mitigation based on Recursive Least Squares (RLS) and Least Mean Squares (LMS), fail to provide sufficient rejection since the incoming signal is not spectrally white, which is critical for their performance. The proposed algorithm mitigates the interference by modeling the incoming multi carrier signal as an Auto-Regressive (AR) process and jointly estimates the AR parameters and self interference. The resulting algorithm can be implemented using a low complexity architecture comprised of only two RLS modules. The main advantage of the proposed technique over RLS and LMS is the robustness to the spectrum of arbitrary incoming signals and improved rejection levels of over 10dB. All of this is achieved while not compromising on low latency constraints.

Original languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-25
Number of pages5
ISBN (Print)9781538615706
DOIs
StatePublished - 29 Aug 2018
Event20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018

Publication series

Name2018 IEEE Statistical Signal Processing Workshop, SSP 2018

Conference

Conference20th IEEE Statistical Signal Processing Workshop, SSP 2018
Country/TerritoryGermany
CityFreiburg im Breisgau
Period10/06/1813/06/18

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