  The evolution of wireless systems has led to a continuous increase in the
demand for radio frequency spectrum. To address this issue, a technology that
has received a lot of attention is In-Band Full-Duplex (IBFD). The interest in
IBFD systems stems from its capability to simultaneously transmit and receive
data in the same frequency. Cancelling the self interference (SI) from the
transmitter to the collocated receiver plays a pivotal role in the performance
of the system. There are two types of SI cancellation (SIC) approaches, passive
and active. In this research, the focus is on active cancellation and, in
particular, SIC in the digital domain. Among the direct and backscattered SI,
the former has been studied for a long time; therefore, the backscatter is
considered in this research and two SIC approaches are analyzed. The first
achieves SIC through beamforming. This requires knowing the angle of the
received SI to put the beam null-space in this direction. The second method
removes SI by employing an Artificial Neural Networks (ANNs). Using an ANN,
there is no need to know the direction of the SI. The neural network is trained
with pilots which results in the network being able to separate the desired
signal from the SI at the receiver. Bayesian Neural Networks show the
importance of the weights and assign a parameter that facilitates ignoring the
less significant ones. Through comparative simulations we demonstrate that the
ANN-based SIC achieves equivalent bit error rate performance as two beamforming
methods.
