Wireless communication systems can be enhanced at the link level, in medium access, and at the network level when transceivers are equipped with full-duplex capability: the transformative ability to simultaneously transmit and receive over the same frequency spectrum. Effective methods to cancel self-interference are required to facilitate full-duplex operation, which we overview herein in the context of traditional radios, along with those in next-generation wireless networks. We highlight advances in self-interference cancellation that leverage machine learning, and we summarize key considerations and recent progress in full-duplex millimeter-wave systems and their application in integrated access and backhaul. We present example design problems and noteworthy findings from recent experimental research to introduce and motivate the advancement of full-duplex millimeter-wave systems. We conclude this chapter by forecasting the future of full-duplex and outlining important research directions that warrant further study.