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Full-duplex (FD) systems have been introduced to provide high data rates forbeyond fifth-generation wireless networks through simultaneous transmission ofinformation over the same frequency resources. However, the operation of FDsystems is practically limited by the self-interference (SI), and efficient SIcancelers are sought to make the FD systems realizable. Typically,polynomial-based cancelers are employed to mitigate the SI; nevertheless, theysuffer from high complexity. This article proposes two novel hybrid-layersneural network (NN) architectures to cancel the SI with low complexity. Thefirst architecture is referred to as hybrid-convolutional recurrent NN (HCRNN),whereas the second is termed as hybrid-convolutional recurrent dense NN(HCRDNN). In contrast to the state-of-the-art NNs that employ dense orrecurrent layers for SI modeling, the proposed NNs exploit, in a novel manner,a combination of different hidden layers (e.g., convolutional, recurrent,and/or dense) in order to model the SI with lower computational complexity thanthe polynomial and the state-of-the-art NN-based cancelers. The key idea behindusing hybrid layers is to build an NN model, which makes use of thecharacteristics of the different layers employed in its architecture. Morespecifically, in the HCRNN, a convolutional layer is employed to extract theinput data features using a reduced network scale. Moreover, a recurrent layeris then applied to assist in learning the temporal behavior of the input signalfrom the localized feature map of the convolutional layer. In the HCRDNN, anadditional dense layer is exploited to add another degree of freedom foradapting the NN settings in order to achieve the best compromise between thecancellation performance and computational complexity. Complexity analysis andnumerical simulations are provided to prove the superiority of the proposedarchitectures.

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