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Until recently, researchers used machine learning methods to compensate forhardware imperfections at the symbol level, indicating that optimumradio-frequency transceiver performance is possible. Nevertheless, suchapproaches neglect the error correcting codes used in wireless networks, whichinspires machine learning (ML)-approaches that learn and minimise hardwareimperfections at the bit level. In the present work, we evaluate a graph neuralnetwork (GNN)-based intelligent detector's in-phase and quadrature imbalance(IQI) mitigation capabilities. We focus on a high-frequency, high-directionalwireless system where IQI affects both the transmitter (TX) and the receiver(RX). The TX uses a GNN-based decoder, whilst the RX uses a linear errorcorrecting algorithm. The bit error rate (BER) is computed using appropriateMonte Carlo simulations to quantify performance. Finally, the outcomes arecompared to both traditional systems using conventional detectors and wirelesssystems using belief propagation based detectors. Due to the utilization ofgraph neural networks, the proposed algorithm is highly scalable with fewtraining parameters and is able to adapt to various code parameters.

Can graph neural network-based detection mitigate the impact of hardware imperfections?