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.

本研究提出了一种更加现实的基站之间的信道模型以及基于多项式和轻量级前馈神经网络的机器学习交叉链路干扰抵消方法，并通过模拟结果表明，采用机器学习方法实现的交叉链路干扰抵消器可以有效降低计算复杂度并提高性能。