An Augmented Nonlinear LMS for Digital Self-Interference Cancellation in Full-Duplex Direct-Conversion Transceivers

TL;DR

This paper introduces an augmented nonlinear LMS algorithm for effective digital self-interference cancellation in full-duplex transceivers, addressing hardware imperfections and nonlinearities to improve SINR in wireless communications.

Contribution

It proposes a novel augmented nonlinear LMS method that jointly cancels linear and nonlinear self-interference components, outperforming conventional solutions in complex hardware scenarios.

Findings

The proposed method achieves higher SINR in simulations.

It effectively cancels both linear and nonlinear SI components.

Performance gains are validated through rigorous analysis and OFDM simulations.

Abstract

In future full-duplex communications, the cancellation of self-interference (SI) arising from hardware non-idealities will play an important role in the design of mobile-scale devices. To this end, we introduce an optimal digital SI cancellation solution for shared-antenna-based direct-conversion transceivers. To establish that the underlying widely linear signal model is not adequate for strong transmit signals, the impact of various circuit imperfections, including power amplifier (PA) distortion, frequency-dependent I/Q imbalance, quantization noise and thermal noise, on the performance of the conventional augmented least mean square (LMS) based SI canceller, is analyzed. In order to achieve a sufficient signal-to-interference-plus-noise ratio (SINR) when the nonlinear SI components are not negligible, we propose an augmented nonlinear LMS based SI canceller for a joint cancellation of both the linear and nonlinear SI components by virtue of a widely nonlinear model fit. A rigorous mean and mean square performance evaluation is conducted to justify the performance advantages of the proposed scheme over the conventional augmented LMS solution. Simulations on orthogonal frequency division multiplexing (OFDM)-based wireless local area network (WLAN) standard compliant waveforms support the analysis.