On the Learning of Digital Self-Interference Cancellation in Full-Duplex Radios

TL;DR

This paper reviews digital self-interference cancellation techniques in full-duplex radios, highlighting the robustness of model-based methods over data-driven approaches in practical scenarios.

Contribution

It compares model-based and model-free SIC methods, demonstrating the superior robustness and efficiency of model-based approaches in real-world conditions.

Findings

Model-based SIC offers better robustness to channel variations.

Model-based methods achieve faster convergence.

Experimental validation confirms effectiveness in IEEE 802.11a scenarios.

Abstract

Full-duplex communication systems have the potential to achieve significantly higher data rates and lower latency compared to their half-duplex counterparts. This advantage stems from their ability to transmit and receive data simultaneously. However, to enable successful full-duplex operation, the primary challenge lies in accurately eliminating strong self-interference (SI). Overcoming this challenge involves addressing various issues, including the nonlinearity of power amplifiers, the time-varying nature of the SI channel, and the non-stationary transmit data distribution. In this article, we present a review of recent advancements in digital self-interference cancellation (SIC) algorithms. Our focus is on comparing the effectiveness of adaptable model-based SIC methods with their model-free counterparts that leverage data-driven machine learning techniques. Through our comparison study under practical scenarios, we demonstrate that the model-based SIC approach offers a more robust solution to the time-varying SI channel and the non-stationary transmission, achieving optimal SIC performance in terms of the convergence rate while maintaining low computational complexity. To validate our findings, we conduct experiments using a software-defined radio testbed that conforms to the IEEE 802.11a standards. The experimental results demonstrate the robustness of the model-based SIC methods, providing practical evidence of their effectiveness.