Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910645868298240 |
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| author | Teixeira, Luiz Fernando Moreira Luiz, Vinicius Henrique Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares |
| author_facet | Teixeira, Luiz Fernando Moreira Luiz, Vinicius Henrique Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels Teixeira, Luiz Fernando Moreira Luiz, Vinicius Henrique Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares Signal Processing 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. |
| title | Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.08186 |