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.
@article{arxiv.2210.17177,
title = {Variational Inference Aided Estimation of Time Varying Channels},
author = {Benedikt Böck and Michael Baur and Valentina Rizzello and Wolfgang Utschick},
journal= {arXiv preprint arXiv:2210.17177},
year = {2022}
}