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.

翻译：灵活双工（FD）技术，包括动态时分双工（D-TDD）和动态频分双工（D-FDD），被认为是实现5G-Advanced或6G移动通信系统中更灵活上下行传输的前景方案。然而，该技术可能引入严重的交叉链路干扰（CLI）。为更好地缓解CLI的影响，我们首先提出了一种更真实的基站间信道模型，该模型融入了射频（RF）链特性，而RF链特性呈现与硬件相关的非线性特征，因此传统信道建模的准确性不足以支撑CLI消除。随后，我们提出了一种基于信道参数估计的多项式CLI消除器，以及两种基于机器学习（ML）的CLI消除器，后者采用轻量级前馈神经网络（FNN）。仿真结果与分析表明，与多项式CLI消除器相比，基于ML的CLI消除器实现了显著的性能提升并大幅降低了计算复杂度。