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Bibliographic Details
Main Authors: Knaepper, Niklas, Enzner, Gerald, Chinaev, Aleksej
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2503.08357
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author Knaepper, Niklas
Enzner, Gerald
Chinaev, Aleksej
author_facet Knaepper, Niklas
Enzner, Gerald
Chinaev, Aleksej
contents Wireless systems with inband full-duplex transceiver typically require multiple lines of defense against the effect of harsh self-interference, specifically, to avoid saturation of the analog-to-digital converter (ADC) in the receiver. We may unite the typical tandem operation of successive analog and digital self-interference cancellation (SIC) stages by means of digitally-assisted analog SIC. In this case, the ADC in the receive path requires considerable attention due its possibly overloaded operation outside the intended range. Using neural-network-based architectures of the transmitter nonlinearity, we therefore describe and compare four system options for SIC model optimization with different treatment of the receiver ADC in the learning process. We find that omitting the ADC in the backwards path via a so-called straight-through estimation approximation barely impedes model learning, thus providing an efficient alternative to the classical approach of automatic gain control.
format Preprint
id arxiv_https___arxiv_org_abs_2503_08357
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On Digital Optimization of Analog Self-Interference Cancellation for Full-Duplex Wireless Systems
Knaepper, Niklas
Enzner, Gerald
Chinaev, Aleksej
Signal Processing
Wireless systems with inband full-duplex transceiver typically require multiple lines of defense against the effect of harsh self-interference, specifically, to avoid saturation of the analog-to-digital converter (ADC) in the receiver. We may unite the typical tandem operation of successive analog and digital self-interference cancellation (SIC) stages by means of digitally-assisted analog SIC. In this case, the ADC in the receive path requires considerable attention due its possibly overloaded operation outside the intended range. Using neural-network-based architectures of the transmitter nonlinearity, we therefore describe and compare four system options for SIC model optimization with different treatment of the receiver ADC in the learning process. We find that omitting the ADC in the backwards path via a so-called straight-through estimation approximation barely impedes model learning, thus providing an efficient alternative to the classical approach of automatic gain control.
title On Digital Optimization of Analog Self-Interference Cancellation for Full-Duplex Wireless Systems
topic Signal Processing
url https://arxiv.org/abs/2503.08357