  In-band full-duplex systems promise to further increase the throughput of
wireless systems, by simultaneously transmitting and receiving on the same
frequency band. However, concurrent transmission generates a strong
self-interference signal at the receiver, which requires the use of
cancellation techniques. A wide range of techniques for analog and digital
self-interference cancellation have already been presented in the literature.
However, their evaluation focuses on cases where the underlying physical
parameters of the full-duplex system do not vary significantly. In this paper,
we focus on adaptive digital cancellation, motivated by the fact that physical
systems change over time. We examine some of the different cancellation methods
in terms of their performance and implementation complexity, considering the
cost of both cancellation and training. We then present a comparative analysis
of all these methods to determine which perform better under different system
performance requirements. We demonstrate that with a neural network approach,
the reduction in arithmetic complexity for the same cancellation performance
relative to a state-of-the-art polynomial model is several orders of magnitude.
