论文标题
变异推理有助于时间变化通道的估计
Variational Inference Aided Estimation of Time Varying Channels
论文作者
论文摘要
提高时间变化渠道估计的一种方法是结合先前观察结果的知识。在这种情况下，动态VAE（DVAE）建立了一个有希望的深度学习（DL）框架，非常适合学习时间序列数据的分布。我们介绍了一种新的DVAE架构，称为K-Memorymarkovevae（K-MMVAE），其稀疏性可以通过附加的内存参数来控制。在[1]中的方法之后，我们得出了K-MMVAE辅助通道估计器，该估计量考虑了连续观察的时间相关性。通过Quadriga在模拟通道上评估了结果，并表明K-MMVAE辅助通道估计器显然优于其他机器学习（ML）辅助估计器，这些估计量是无记忆或天真延伸到没有主要适应性的时间变化的通道。
One way to improve the estimation of time varying channels is to incorporate knowledge of previous observations. In this context, Dynamical VAEs (DVAEs) build a promising deep learning (DL) framework which is well suited to learn the distribution of time series data. We introduce a new DVAE architecture, called k-MemoryMarkovVAE (k-MMVAE), whose sparsity can be controlled by an additional memory parameter. Following the approach in [1] we derive a k-MMVAE aided channel estimator which takes temporal correlations of successive observations into account. The results are evaluated on simulated channels by QuaDRiGa and show that the k-MMVAE aided channel estimator clearly outperforms other machine learning (ML) aided estimators which are either memoryless or naively extended to time varying channels without major adaptions.
