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

Autor:	Mitsiou, Lamprini, Trevlakis, Stylianos, Tsiolas, Argiris, Vergados, Dimitrios J., Michalas, Angelos, Boulogeorgos, Alexandros-Apostolos A.
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Until recently, researchers used machine learning methods to compensate for hardware imperfections at the symbol level, indicating that optimum radio-frequency transceiver performance is possible. Nevertheless, such approaches neglect the error correcting codes used in wireless networks, which inspires machine learning (ML)-approaches that learn and minimise hardware imperfections at the bit level. In the present work, we evaluate a graph neural network (GNN)-based intelligent detector's in-phase and quadrature imbalance (IQI) mitigation capabilities. We focus on a high-frequency, high-directional wireless 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 error correcting algorithm. The bit error rate (BER) is computed using appropriate Monte Carlo simulations to quantify performance. Finally, the outcomes are compared to both traditional systems using conventional detectors and wireless systems using belief propagation based detectors. Due to the utilization of graph neural networks, the proposed algorithm is highly scalable with few training parameters and is able to adapt to various code parameters. Comment: 5 pages, 2 figures, BalkanCom 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.04612 Zobrazit plný text záznamu View this record from Arxiv