Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems

Full-duplex (FD) systems have been introduced to provide high data rates for beyond fifth-generation wireless networks through simultaneous transmission of information over the same frequency resources. However, the operation of FD systems is practically limited by self-interference (SI), and efficient SI cancelers are sought to make the FD systems realizable. Typically, polynomial-based cancelers are employed to mitigate the SI; nevertheless, they suffer from high complexity. This article proposes two novel hybrid-layers neural network (NN) architectures to cancel the SI with low complexity. The first architecture is referred to as hybrid-convolutional recurrent NN (HCRNN), whereas the second is termed as hybrid-convolutional recurrent dense NN (HCRDNN).In the HCRNN, a convolutional layer is employed to extract the input data features using a reduced network scale. Moreover, a recurrent layer is then applied to assist in learning the temporal behavior of the input signal from the localized feature map of the convolutional layer. In the HCRDNN, an additional dense layer is exploited to add another degree of freedom for adapting the NN settings in order to achieve the best compromise between the cancellation performance and computational complexity. The complexity analysis of the proposed NN architectures is provided, and the optimum settings for their training are selected. The simulation results demonstrate that the proposed HCRNN and HCRDNN-based cancelers attain the same cancellation of the polynomial and the state-of-the-art NN-based cancelers with an astounding computational complexity reduction. Furthermore, the proposed cancelers show high design flexibility for hardware implementation, depending on the system demands.

Automated summary

This article proposes two novel hybrid-layer neural network architectures, HCRNN and HCRDNN, for low-complexity self-interference cancellation. These cancelers achieve the same cancellation performance as polynomial and state-of-the-art neural network-based cancelers while reducing computational complexity through the use of convolutional and recurrent layers to extract features and learn temporal behavior.