Generative vs. Predictive Models in Massive MIMO Channel Prediction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915035247280128 |
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| author | Lee, Ju-Hyung Lee, Joohan Molisch, Andreas F. |
| author_facet | Lee, Ju-Hyung Lee, Joohan Molisch, Andreas F. |
| contents | Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard AEs struggle under noisy channel conditions, limiting their effectiveness. This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction. We compare Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments. The proposed VQ-VAE achieves up to 15 [dB] NMSE gains over standard AEs and about 9 [dB] over VAEs. Additionally, we present a complexity analysis of AE-based models alongside a diffusion model, highlighting the trade-off between accuracy and computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16971 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative vs. Predictive Models in Massive MIMO Channel Prediction Lee, Ju-Hyung Lee, Joohan Molisch, Andreas F. Information Theory Networking and Internet Architecture Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard AEs struggle under noisy channel conditions, limiting their effectiveness. This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction. We compare Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments. The proposed VQ-VAE achieves up to 15 [dB] NMSE gains over standard AEs and about 9 [dB] over VAEs. Additionally, we present a complexity analysis of AE-based models alongside a diffusion model, highlighting the trade-off between accuracy and computational efficiency. |
| title | Generative vs. Predictive Models in Massive MIMO Channel Prediction |
| topic | Information Theory Networking and Internet Architecture |
| url | https://arxiv.org/abs/2411.16971 |