A Neural Network Approach for the Cancellation of the Second-Order-Intermodulation Distortion in 4G/5G Transceivers
Sprache des Vortragstitels:
Asilomar Conference on Signals, Systems, and Computers
Sprache des Tagungstitel:
The limited transmitter-to-receiver stop-band isolation of the duplexers in long term evolution (LTE) and 5G 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. This work proposes a novel architecture combating the second-order intermodulation distortion (IMD2), arising from the TX leakage signal in combination with a coupling between the RF- and local oscillator (LO)-ports of the RX IQ-mixer. In contrast to traditional adaptive filter solutions, the presented work relies on a neural network based approach for estimating the transmitter induced IMD2 SI signal used to cancel the interference in the receiver. The proposed architecture outperforms existing work based on least mean squares (LMS), recursive least squares (RLS) and Volterra kernel algorithms while maintaining comparable complexity.