Digital Self-Interference Cancellation With Robust Multi-layered Total Least Mean Squares Adaptive Filters

TL;DR

This paper introduces a multi-layered M-estimate total least mean squares (m-MTLS) adaptive filter for digital self-interference cancellation in STAR wireless systems, improving robustness and performance over traditional methods.

Contribution

It proposes a novel multi-layered joint estimator that enhances SI cancellation and RT channel estimation, especially under noisy and impulse noise conditions.

Findings

m-MTLS outperforms MMSE and single-layer MTLS in NMSD metrics

Demonstrates robustness against noise contamination and impulse noise

Provides improved channel estimation accuracy in STAR systems

Abstract

In simultaneous transmit and receive (STAR) wireless communications, digital self-interference (SI) cancellation is required before estimating the remote transmission (RT) channel. Considering the inherent connection between SI channel reconstruction and RT channel estimation, we propose a multi-layered M-estimate total least mean squares (m-MTLS) joint estimator to estimate both channels. In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel. Then, it gives the final RT channel estimation based on the weighted sum of the estimation values obtained from each layer. Compared to traditional minimum mean square error (MMSE) estimator and single-layered MTLS estimator, it demonstrates that the m-MTLS estimator has better performance of normalized mean squared difference (NMSD). Besides, the simulation results also show the robustness of m-MTLS estimator even in scenarios where the local reference signal is contaminated with noise, and the received signal is impacted by strong impulse noise.