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.
翻译:改进对不同时间渠道的估计的一种方法是将以往观测的知识纳入其中。在这方面,动态VAEs(DVAEs)建立了一个很有希望的深层次学习(DL)框架,非常适合学习时间序列数据的分布。我们引入了一个新的DVAE结构,称为k-Memory MarkovVAE(k-MMVAE),其宽度可以通过额外的记忆参数控制。在[1]中的方法之后,我们产生了一个k-MMVAE辅助的频道估计仪,该仪将连续观测的时间相关性考虑在内。由QuadriGa在模拟频道上对结果进行评估,并表明K-MMVAE辅助的频道估计仪明显优于其他机器学习(ML),这些天真无记忆,或者无重大调整,可以天真地延伸至不同时期的频道。