On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

TL;DR

This paper evaluates various digital self-interference cancellation techniques for full-duplex wireless systems, highlighting the trade-offs in performance and complexity, and demonstrating a neural network approach that significantly reduces arithmetic complexity.

Contribution

It provides a comprehensive comparison of cancellation methods considering implementation complexity and introduces a neural network-based approach that greatly reduces computational costs.

Findings

Neural network approach reduces arithmetic complexity by several orders of magnitude.

Performance of cancellation methods varies with system requirements.

Adaptive digital cancellation is crucial for changing physical system parameters.

Abstract

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.