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Found 9 papers, 2 papers with code
Channel-Adaptive Pilot Design for FDD-MIMO Systems Utilizing Gaussian Mixture Models
Once shared with the mobile terminal (MT), the GMM is utilized to determine a feedback index at the MT in the online phase.
On the Asymptotic Mean Square Error Optimality of Diffusion Models
Diffusion models (DMs) as generative priors have recently shown great potential for denoising tasks but lack theoretical understanding with respect to their mean square error (MSE) optimality.
Wireless Channel Prediction via Gaussian Mixture Models
In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) within the coverage area of a base station (BS) in an offline phase.
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data.
Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses
This work introduces a novel class of positive definiteness ensuring likelihood-based estimators for Toeplitz structured covariance matrices (CMs) and their inverses.
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models
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.
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.
Variational Inference Aided Estimation of Time Varying Channels
One way to improve the estimation of time varying channels is to incorporate knowledge of previous observations.
Model Order Selection with Variational Autoencoding
Classical methods for model order selection often fail in scenarios with low SNR or few snapshots.
