  In-band full-duplex systems allow for more efficient use of temporal and
spectral resources by transmitting and receiving information at the same time
and on the same frequency. However, this creates a strong self-interference
signal at the receiver, making the use of self-interference cancellation
critical. Recently, neural networks have been used to perform digital
self-interference with lower computational complexity compared to a traditional
polynomial model. In this paper, we examine the use of advanced neural
networks, such as recurrent and complex-valued neural networks, and we perform
an in-depth network architecture exploration. Our neural network architecture
exploration reveals that complex-valued neural networks can significantly
reduce both the number of floating-point operations and parameters compared to
a polynomial model, whereas the real-valued networks only reduce the number of
floating-point operations. For example, at a digital self-interference
cancellation of 44.51 dB, a complex-valued neural network requires 33.7 % fewer
floating-point operations and 26.9 % fewer parameters compared to the
polynomial model.
