Kernel-Based Signal Processing for Simultaneous Transmit-Receive Systems

The purpose of the paper is to present an experimental investigation of digital self-interference cancellation (DSIC) in the presence of hardware impairments of a wireless transceiver operating in in-band full-duplex mode. The experiment will present a passive polarization-based antenna suppression followed by DSIC utilizing kernel-based methods such as the adaptive projected subgradient method (APSM) and support vector regression (SVR) as well as a combination of those. They employ various positive-definite kernels to estimate the linear and nonlinear parts of the effective self-interference (SI) channel collectively. These data-driven algorithms provide us with sparse representation of the SI signal, which not only cancels out the interference but also may be utilized to extract information from dynamic wireless channels. Besides, the adoption of these methods for classification tasks is straightforward.