Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels
Abstract
This paper presents an online method for joint channel estimation and decoding in massive MIMO-OFDM systems using complex-valued neural networks (CVNNs). The study evaluates the performance of various CVNNs, such as the complex-valued feedforward neural network (CVFNN), split-complex feedforward neural network (SCFNN), complex radial basis function (C-RBF), fully-complex radial basis function (FC-RBF) and phase-transmittance radial basis function (PT-RBF), in realistic 5G communication scenarios. Results demonstrate improvements in mean squared error (MSE), convergence, and bit error rate (BER) accuracy. The C-RBF and PT-RBF architectures show the most promising outcomes, suggesting that RBF-based CVNNs provide a reliable and efficient solution for complex and noisy communication environments. These findings have potential implications for applying advanced neural network techniques in next-generation wireless systems.
Cite
@article{arxiv.2408.08186,
title = {Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels},
author = {Luiz Fernando Moreira Teixeira and Vinicius Henrique Luiz and Jonathan Aguiar Soares and Kayol Soares Mayer and Dalton Soares Arantes},
journal= {arXiv preprint arXiv:2408.08186},
year = {2024}
}
Comments
XLII Simp\'osio Brasileiro de Telecomunica\c{c}\~oes e Processamento de Sinais (SBrT 2024)