Design and Implementation of a Neural Network Aided Self-Interference Cancellation Scheme for Full-Duplex Radios

Abstract

In-band full-duplex systems are able to transmit and receive information simultaneously on the same frequency band. Due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital selfinterference cancellation is essential. In this work, we present a hardware architecture for a neural network based non-linear selfinterference canceller and we compare it with our own hardware implementation of a conventional polynomial based canceller. We show that, for the same cancellation performance, the neural network canceller has a significantly higher throughput and requires fewer hardware resources.

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

“…In [15], the authors used neural networks (NNs) to perform the self-interference cancellation and found that it could achieve similar performance to polynomial based self-interference cancellation. They later extended the work to create both FPGA and ASIC implementations of the NN-based self-interference canceller and found that, due to the regular structure of the NN and the lower bit-width requirements, it can be implemented to have both a higher throughput and lower resource utilization [16].…”

Full-Duplexing With SDR Devices: Algorithms, FPGA Implementation, and Real-Time Results

“…The works in [8], [17], [26] focused on the power amplifier induced nonlinear distortion and its digital cancellation; [20]- [22] opted to linearize the PA with digital predistortion, and utilized simpler SI models in digital cancellation; in [18], [19], receiver LNA distortions were considered; [15], [27] studied the effects of I/Q mismatches on both TX and RX sides, and proposed cancellation algorithms for them. Some works have also considered multiple impairments [16], [23]- [25]. In [24], the authors considered receiver LNA and baseband nonlinearities, as well as PA nonlinearities, in a full-duplex multiple-input multiple-output (FD-MIMO) system.…”

“…In [16], I/Q imbalances of the transmitter and receiver, as well as baseband nonlinearities of the transmitter, were considered in SI cancellation. A neural network based model was proposed in [25], along with a field-programmable gate array (FPGA) implementation of the prediction part of the model. This model can effectively account for arbitrary nonlinearities in the SI signal, but is plagued by a high learning/fitting complexity, which is generally unsuitable for real-time implementation.…”

Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios

“…However, the polynomial models have Local Oscillator Fig. 1: Simplified wireless FD transceiver block diagram [11]. a high implementation complexity as the number of estimated parameters grows rapidly with the maximum considered nonlinearity order.…”

“…a high implementation complexity as the number of estimated parameters grows rapidly with the maximum considered nonlinearity order. As an alternative to polynomial models, neural networks (NNs) have recently been proposed for SI cancellation [10], [11] where it was shown that NNs can achieve similar SI cancellation performance with a polynomial model with significantly lower computational complexity.…”