Multi-Domain Self-Interference Cancellation Methods Considering RF Imperfections

In recent years, the utilization of In-Band Full-Duplex (IBFD) technology has gained considerable attention as an effective solution to address limited spectrum resources. However, achieving an IBFD system necessitates the utilization of both passive suppression and active cancellation techniques to completely eliminate strong self-interference (SI) signals in the local receiver. This research paper aims to tackle this challenge by introducing the Multi-Domain Intelligent Cancellation (MDIC) method, which integrates analog-domain and digital-domain cancellation techniques through comprehensive nonlinear system modeling. Moreover, this study presents an extensive analysis of the underlying principles of MDIC, while evaluating the limits of SI suppression performance under the assumption of perfect channel estimation and no phase noise. The channel parameter estimation is conducted using the time-domain least squares method, without considering the effects of Gaussian white noise and phase noise. To enhance the performance of SI cancellation (SIC), a deep learning network (DLN) is introduced in the digital domain. Simulation results demonstrate that the proposed MDIC method achieves a substantial SIC performance of approximately 60 dB, which closely approaches the theoretical limit. Furthermore, experimental data indicates that the proposed deep learning approach exhibits superior SIC performance and broader adaptability to broadband signals compared to traditional methods.