Lightweight Machine Learning for Digital Cross-Link Interference Cancellation with RF Chain Characteristics in Flexible Duplex MIMO Systems
This addresses interference mitigation for 5G-Advanced or 6G mobile communication systems, representing an incremental advance with domain-specific impact.
The paper tackles cross-link interference in flexible duplex MIMO systems by incorporating RF chain nonlinearities into channel modeling and proposing lightweight machine learning cancellers, achieving notable performance improvement and dramatic computational complexity reduction compared to polynomial methods.
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.