DSIC: Deep Learning based Self-Interference Cancellation for In-Band Full Duplex Wireless

TL;DR

This paper introduces a deep learning-based real-time self-interference cancellation method for in-band full duplex wireless, significantly improving spectrum efficiency by nearly canceling self-interference in practical SDR testbed scenarios.

Contribution

It presents a novel deep neural network approach for non-linear self-interference cancellation in IBFD wireless, demonstrated on real hardware with substantial performance gains.

Findings

Achieved 17dB digital self-interference cancellation close to interference power.

Reduced bit error rate to an average of 8.5% across various scenarios.

Validated effectiveness in real-world SDR testbed with multiple modulations.

Abstract

In-band full duplex wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, however, is throttled by its key challenge, namely self-interference. Therefore, effective self-interference cancellation is the key to enable IBFD wireless. This paper proposes a real-time non-linear self-interference cancellation solution based on deep learning. In this solution, a self-interference channel is modeled by a deep neural network (DNN). Synchronized self-interference channel data is first collected to train the DNN of the self-interference channel. Afterwards, the trained DNN is used to cancel the self-interference at a wireless node. This solution has been implemented on a USRP SDR testbed and evaluated in real world in multiple scenarios with various modulations in transmitting information including numbers, texts as well as images. It results in the performance of 17dB in digital cancellation, which is very close to the self-interference power and nearly cancels the self-interference at a SDR node in the testbed. The solution yields an average of 8.5% bit error rate (BER) over many scenarios and different modulation schemes.