Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models

This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative models, specifically Gaussian mixture models (GMMs), mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These models effectively learn the unknown channel distribution inherent in radio propagation scenarios, providing valuable prior information. Conditioning on the latent variable of these generative models yields a locally Gaussian channel distribution, thus enabling the application of the well-known Bussgang decomposition. By exploiting the resulting conditional Bussgang decomposition, we derive parameterized linear minimum mean square error (MMSE) estimators for the considered generative latent variable models. In this context, we explore leveraging model-based structural features to reduce memory and complexity overhead associated with the proposed estimators. Furthermore, we devise necessary training adaptations, enabling direct learning of the generative models from quantized pilot observations without requiring ground-truth channel samples during the training phase. Through extensive simulations, we demonstrate the superiority of our introduced estimators over existing state-of-the-art methods for coarsely quantized systems, as evidenced by significant improvements in mean square error (MSE) and achievable rate metrics

Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses

When only few data samples are accessible, utilizing structural prior knowledge is essential for estimating covariance matrices and their inverses. One prominent example is knowing the covariance matrix to be Toeplitz structured, which occurs when dealing with wide sense stationary (WSS) processes. This work introduces a novel class of positive definiteness ensuring likelihood-based estimators for Toeplitz structured covariance matrices (CMs) and their inverses. In order to accomplish this, we derive positive definiteness enforcing constraint sets for the Gohberg-Semencul (GS) parameterization of inverse symmetric Toeplitz matrices. Motivated by the relationship between the GS parameterization and autoregressive (AR) processes, we propose hyperparameter tuning techniques, which enable our estimators to combine advantages from state-of-the-art likelihood and non-parametric estimators. Moreover, we present a computationally cheap closed-form estimator, which is derived by maximizing an approximate likelihood. Due to the ensured positive definiteness, our estimators perform well for both the estimation of the CM and the inverse covariance matrix (ICM). Extensive simulation results validate the proposed estimators' efficacy for several standard Toeplitz structured CMs commonly employed in a wide range of applications

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.Comment: Submitted to IEEE for possible publicatio

Reverse Ordering Techniques for Attention-Based Channel Prediction

This work aims to predict channels in wireless communication systems based on noisy observations, utilizing sequence-to-sequence models with attention (Seq2Seq-attn) and transformer models. Both models are adapted from natural language processing to tackle the complex challenge of channel prediction. Additionally, a new technique called reverse positional encoding is introduced in the transformer model to improve the robustness of the model against varying sequence lengths. Similarly, the encoder outputs of the Seq2Seq-attn model are reversed before applying attention. Simulation results demonstrate that the proposed ordering techniques allow the models to better capture the relationships between the channel snapshots within the sequence, irrespective of the sequence length, as opposed to existing methods.Comment: Submitted to IEEE for publicatio

Serum biomarker panel diagnostics in pancreatic ductal adenocarcinoma: the clinical utility of soluble interleukins, IFN-gamma, TNF-alpha and PD-1/PD-L1 in comparison to established serum tumor markers

Purpose Novel biomarkers to better predict outcome and select the best therapeutic strategy for the individual patient are necessary for pancreatic ductal adenocarcinoma (PDAC). Methods Using a panel assay, multiple biomarkers (IFN-gamma, IL-10, IL-6, IL-8, TNF-alpha, CEA, CA 19-9, CYFRA 21-1, HE4, PD-1 and PD-L1 levels) were measured in serum samples of 162 patients with resected, locally advanced and metastatic PDAC in this retrospective single-center study. Optimal cut-off values to differentiate prognostic subgroups with significantly different overall survival (OS) were determined by receiver operator characteristics and Youden Index analysis. Marker levels were assessed before the start of chemotherapy and correlated with OS by univariate and multivariate Cox analysis. Results Median OS for resected patients was 28.2 months, for locally advanced patients 17.9 months and for patients with metastatic disease 8.6 months. CYFRA 21-1 and IL-8 discriminated metastatic from locally advanced patients best (AUC 0.85 and AUC 0.81, respectively). In univariate analyses, multiple markers showed prognostic relevance in the various subgroups. However, multivariate Cox models comprised only CYFRA 21-1 in the resected group (HR 1.37, p = 0.015), IL-10 in locally advanced PDAC (HR 10.01, p = 0.014), as well as CYFRA 21-1 and CA 19-9 in metastatic PDAC (p = 0.008 and p = 0.010) as an independent prognostic marker for overall survival. Conclusion IL-10 levels may have independent prognostic value in locally advanced PDAC, whereas CYFRA 21-1 levels are prognostic after PDAC surgery. CYFRA 21-1 and IL-8 have been identified to best discriminate metastatic from locally advanced patients