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The evolution of wireless systems has led to a continuous increase in thedemand for radio frequency spectrum. To address this issue, a technology thathas received a lot of attention is In-Band Full-Duplex (IBFD). The interest inIBFD systems stems from its capability to simultaneously transmit and receivedata in the same frequency. Cancelling the self interference (SI) from thetransmitter to the collocated receiver plays a pivotal role in the performanceof the system. There are two types of SI cancellation (SIC) approaches, passiveand active. In this research, the focus is on active cancellation and, inparticular, SIC in the digital domain. Among the direct and backscattered SI,the former has been studied for a long time; therefore, the backscatter isconsidered in this research and two SIC approaches are analyzed. The firstachieves SIC through beamforming. This requires knowing the angle of thereceived SI to put the beam null-space in this direction. The second methodremoves 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 trainedwith pilots which results in the network being able to separate the desiredsignal from the SI at the receiver. Bayesian Neural Networks show theimportance of the weights and assign a parameter that facilitates ignoring theless significant ones. Through comparative simulations we demonstrate that theANN-based SIC achieves equivalent bit error rate performance as two beamformingmethods.

Self Interference Management in In-Band Full-Duplex Systems