Abstract-This paper addresses the modeling and digital cancellation of self-interference in in-band full-duplex (FD) transceivers with multiple transmit and receive antennas. The self-interference modeling and the proposed nonlinear spatiotemporal digital canceller structure takes into account, by design, the effects of I/Q modulator imbalances and power amplifier (PA) nonlinearities with memory, in addition to the multipath selfinterference propagation channels and the analog RF cancellation stage. The proposed solution is the first cancellation technique in the literature which can handle such a self-interference scenario. It is shown by comprehensive simulations with realistic RF component parameters and with two different PA models to clearly outperform the current state-of-the-art digital selfinterference cancellers, and to clearly extend the usable transmit power range.
Although full-duplex relaying schemes are appealing in order to improve spectral efficiency, simultaneous reception and transmission in the same frequency results in selfinterference, distorting the retransmitted signal and making the relay prone to oscillation. Current feedback cancellation techniques by means of adaptive filters are hampered by the fact that the useful and interference signals are highly correlated. We present a new adaptive algorithm which effectively and blindly restores the spectral shape of the desired signal. In contrast with previous schemes, the novel adaptive feedback canceller has low complexity, does not introduce additional delay in the relay station, and partly compensates for multipath propagation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.