Augmented Envelope Neural Networks on RF-SoC for Digital Self-Interference Cancellation

Abstract

This paper addresses the challenge of self-interference in in-band full-duplex radios, which can double the operational bandwidth and wireless channel capacity in 5G New Radio’s sub-6 GHz spectrum. To achieve high isolation between simultaneously transmitted and received signals, the study explores envelope neural networks for self-interference cancellation. These networks model non-linear artifacts arising from both the transmit power amplifier and the receiver’s low-noise amplifier. Trained model parameters are subsequently applied in real-time via a neural network-based digital signal processor to mitigate self-interference. A real-time prototype operating at 2.4 GHz, featuring direct-RF sampling at 4.096 GS/s and a 20 dBm transmit power through an external PA, was implemented using an AMD-Xilinx ZCU-111 RF-SoC. The system demonstrates digital self-interference cancellation exceeding 30 dB in real-time over a 32 MHz passband bandwidth, utilizing a novel augmented envelope neural network realized as a systolic array architecture.

Keywords

• IBFD
• STAR
• digital cancellation
• machine learning
• neural networks
• RF-SoC