  The flexible duplex (FD) technique, including dynamic time-division duplex
(D-TDD) and dynamic frequency-division duplex (D-FDD), is regarded as a
promising solution to achieving a more flexible uplink/downlink transmission in
5G-Advanced or 6G mobile communication systems. However, it may introduce
serious cross-link interference (CLI). For better mitigating the impact of CLI,
we first present a more realistic base station (BS)-to-BS channel model
incorporating the radio frequency (RF) chain characteristics, which exhibit a
hardware-dependent nonlinear property, and hence the accuracy of conventional
channel modelling is inadequate for CLI cancellation. Then, we propose a
channel parameter estimation based polynomial CLI canceller and two machine
learning (ML) based CLI cancellers that use the lightweight feedforward neural
network (FNN). Our simulation results and analysis show that the ML based CLI
cancellers achieve notable performance improvement and dramatic reduction of
computational complexity, in comparison with the polynomial CLI canceller.
