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arXiv · 2408.08186

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

Luiz Fernando Moreira Teixeira·Vinicius Henrique Luiz·Jonathan Aguiar Soares·Kayol Soares Mayer·Dalton Soares Arantes
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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.

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Luiz Fernando Moreira Teixeira, Vinicius Henrique Luiz, Jonathan Aguiar Soares, Kayol Soares Mayer, Dalton Soares Arantes. 2024-08-15. Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels. https://doi.org/10.14209/sbrt.2024.1571036775

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