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.

翻译：大规模MIMO（mMIMO）系统对于满足5G/6G网络的高吞吐量与高可靠性需求至关重要，其中基于机器学习（ML）的技术，特别是自编码器（AEs），在实际部署中展现出潜力。然而，标准自编码器在噪声信道条件下表现不佳，限制了其有效性。本文提出一种基于矢量量化的生成式自编码器模型（VQ-VAE），用于实现鲁棒的大规模MIMO跨天线信道预测。我们比较了生成式与预测式自编码器模型，证明生成式模型（尤其在噪声环境中）优于预测式模型。所提出的VQ-VAE相较于标准自编码器实现了高达15 [dB]的归一化均方误差增益，相较于变分自编码器（VAEs）增益约为9 [dB]。此外，我们分析了自编码器模型与扩散模型的复杂度，揭示了精度与计算效率之间的权衡关系。