Combining DoA-based and MMSE Techniques for Wireless Channel Estimation

This paper investigates the combination of parametric channel estimation with minimum mean square error (MMSE) estimation. We propose a two-stage channel estimation technique that utilizes the decomposition of wireless communication channels into a distinct line-of-sight (LoS) path and multiple reflected scattered clusters. Firstly, a direction-of-arrival (DoA)-based estimator is formulated to estimate the LoS component. Afterwards, we utilize a Gaussian mixture model to estimate the conditionally Gaussian distributed random vector, which represents the multipath propagation. The proposed two-stage estimator allows pre-computing the respective estimation filters, tremendously reducing the computational complexity. Numerical simulations with typical channel models depict the superior performance of our proposed two-stage estimation approach as compared to state-of-the-art methods.

Data-Aided Channel Estimation Utilizing Gaussian Mixture Models

In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators.

Unsupervised Parameter Estimation using Model-based Decoder

In this work, we consider the use of a model-based decoder in combination with an unsupervised learning strategy for direction-of-arrival (DoA) estimation. Relying only on unlabeled training data we show in our analysis that we can outperform existing unsupervised machine learning methods and classical methods. This is done by introducing a model-based decoder in an autoencoder architecture with leads to a meaningful representation of the statistical model in the latent space. Our numerical simulation show that the performance of the presented approach is not affected by correlated signals but rather improves slightly. This is due to the fact, that we propose the estimation of the correlation parameters simultaneously to the DoA estimation.

Model Order Selection with Variational Autoencoding

Classical methods for model order selection often fail in scenarios with low SNR or few snapshots. Deep learning based methods are promising alternatives for such challenging situations as they compensate lack of information in observations with repeated training on large datasets. This manuscript proposes an approach that uses a variational autoencoder (VAE) for model order selection. The idea is to learn a parameterized conditional covariance matrix at the VAE decoder that approximates the true signal covariance matrix. The method itself is unsupervised and only requires a small representative dataset for calibration purposes after training of the VAE. Numerical simulations show that the proposed method clearly outperforms classical methods and even reaches or beats a supervised approach depending on the considered snapshots.