SICNet - Low Complexity Sample Adaptive Neural Network-Based Self-Interference Cancellation in LTE-A/5G Mobile Transceivers

Abstract

The limited transmitter-to-receiver stop-band isolation of the duplexers in long term evolution (LTE) and 5G/NR frequency division duplex transceivers induces leakage signals from the transmitter(s) (Tx) into the receiver(s) (Rx). These leakage signals are the root cause of a multitude of self-interference (SI) problems in the receiver path(s) diminishing a receiver's sensitivity. Traditionally, these effects are counteracted by the use of various different SI cancellation (SIC) architectures which typically solely target one specific problem. In this paper, we propose two novel neural networks based architectures that can handle a variety of different SI effects without the need for a different architecture for each effect. We additionally show the suitability of the proposed architecture on SI effects occurring in in-band full duplex transceivers. Further, we introduce two novel low-cost training algorithms to enable online adaptation (as opposed to offline training currently proposed in literature). The combination of these two concepts is shown to not only beat existing algorithms in their cancellation performance, but also to provide sufficiently low computational complexity allowing on-chip implementations.