用于信道估计的变分自编码器：真实世界测量洞见
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
AI总结：
本文提出仅用含噪观测训练的变分自编码器信道估计器，通过学习条件一阶和二阶矩近似MSE最优估计，在真实测量中显著优于现有方法，并发现合成数据预训练可保持性能且减少实测数据需求。
摘要：
本工作利用变分自编码器进行信道估计，并在真实世界测量数据上对其进行评估。该估计器仅在含噪信道观测上进行训练，通过学习依赖于观测的条件一阶矩和二阶矩，对均方误差最优估计器的近似进行参数化。所提出的估计器在真实世界测量数据上显著优于相关的最先进估计器。我们研究了使用合成数据进行预训练的效果，发现如果所提出的估计器在合成数据上训练并在测量数据上评估，其表现出与相关估计器相当的结果。此外，在合成数据上进行预训练还有助于减少所需的测量训练数据集规模。
英文摘要：
This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
