Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels

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 neu...

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Bibliographic Details
Main Authors:	Teixeira, Luiz Fernando Moreira, Luiz, Vinicius Henrique, Soares, Jonathan Aguiar, Mayer, Kayol Soares, Arantes, Dalton Soares
Format:	Journal Article
Language:	English
Published:	15-08-2024
Online Access:	Get full text
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Summary:	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.
DOI:	10.48550/arxiv.2408.08186