  Self-interference (SI) is considered as a main challenge in full-duplex (FD)
systems. Therefore, efficient SI cancelers are required for the influential
deployment of FD systems in beyond fifth-generation wireless networks. Existing
methods for SI cancellation have mostly considered the polynomial
representation of the SI signal at the receiver. These methods are shown to
operate well in practice while requiring high computational complexity.
Alternatively, neural networks (NNs) are envisioned as promising candidates for
modeling the SI signal with reduced computational complexity. Consequently, in
this paper, two novel low complexity NN structures, referred to as the
ladder-wise grid structure (LWGS) and moving-window grid structure (MWGS), are
proposed. The core idea of these two structures is to mimic the non-linearity
and memory effect introduced to the SI signal in order to achieve proper SI
cancellation while exhibiting low computational complexity. The simulation
results reveal that the LWGS and MWGS NN-based cancelers attain the same
cancellation performance of the polynomial-based canceler while providing
49.87% and 34.19% complexity reduction, respectively.
