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Found 44 papers, 9 papers with code
Enhancing Channel Estimation in Quantized Systems with a Generative Prior
Channel estimation in quantized systems is challenging, particularly in low-resolution systems.
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.
An Efficient Rate Splitting Precoding Approach in Multi-User MISO FDD Systems
In this work, we develop an efficient precoding strategy for a multi-user multiple-input-single output (MU MISO) system operating in frequency-division-duplex (FDD) mode, where rate splitting multiple access (RSMA) is implemented.
Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation
This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models.
On the Asymptotic Mean Square Error Optimality of Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs) have recently shown great potential for denoising tasks.
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.
DoA-Aided MMSE Channel Estimation for Wireless Communication Systems
This paper investigates the combination of parametric channel estimation with minimum mean square error (MMSE) estimation.
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.
Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI
In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode.
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.
Unsupervised high-throughput segmentation of cells and cell nuclei in quantitative phase images
In the effort to aid cytologic diagnostics by establishing automatic single cell screening using high throughput digital holographic microscopy for clinical studies thousands of images and millions of cells are captured.
Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders
In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems.
Design of a Single-User RIS-Aided MISO System Based on Statistical Channel Knowledge
Reconfigurable intelligent surface (RIS) is considered a prospective technology for beyond fifth-generation (5G) networks to improve the spectral and energy efficiency at a low cost.
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.
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.
Physically Consistent Modelling of Wireless Links with Reconfigurable Intelligent Surfaces Using Multiport Network Analysis
The vast majority of research publications on RIS are focussing on system-level optimization and are based on very simplistic models ignoring basic physical laws.
Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain
This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios.
Leveraging Variational Autoencoders for Parameterized MMSE Estimation
We propose three estimator variants that differ in their access to ground-truth data during the training and estimation phases.
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.
On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle.
A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways.
Low-Rank Structured MMSE Channel Estimation with Mixtures of Factor Analyzers
This work proposes a generative modeling-aided channel estimator based on mixtures of factor analyzers (MFA).
Gradient Derivation for Learnable Parameters in Graph Attention Networks
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
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.
Asymptotic Behavior of Zero-Forcing Precoding based on Imperfect Channel Knowledge for Massive MISO FDD Systems
Although the LMMSE channel estimate exhibits a better quality in terms of the MSE due to the exploitation of the channel statistics, we show that in the case of contaminated channel observations, zero-forcing based on the LMMSE is unable to eliminate the inter-user interference in the asymptotic limit of high DL transmit powers.
Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation
In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i. e., the training data are solely comprised of noisy and sparsely allocated pilot observations.
Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems
In order to illuminate the additional cascaded channel as compared to systems without a RIS, commonly an unaffordable amount of pilot sequences has to be transmitted over different phase allocations at the RIS.
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.
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.
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
The latent space so formed is used for successful clustering and novel scenario type detection.
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios
The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction.
Learning Representations for CSI Adaptive Quantization and Feedback
In this work, we propose an efficient method for channel state information (CSI) adaptive quantization and feedback in frequency division duplexing (FDD) systems.
Variational Autoencoder Leveraged MMSE Channel Estimation
First, we show that given perfectly known channel state information at the input of the VAE during estimation, which is impractical, we obtain an estimator that can serve as a benchmark result for an estimation scenario.
Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels
In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output (MaMIMO) system operating in frequency-division-duplex (FDD) mode.
An Asymptotically MSE-Optimal Estimator based on Gaussian Mixture Models
Then, a conditional mean estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF.
CSI Clustering with Variational Autoencoding
The model order of a wireless channel plays an important role for a variety of applications in communications engineering, e. g., it represents the number of resolvable incident wavefronts with non-negligible power incident from a transmitter to a receiver.
Learning a Compressive Sensing Matrix with Structural Constraints via Maximum Mean Discrepancy Optimization
We interpret a matrix with restricted isometry property as a mapping of points from a high- to a low-dimensional hypersphere.
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
This constitutes a statistical inference task in terms of the optimal latent space activations of the underlying uncorrupted datum.
ChainNet: Neural Network-Based Successive Spectral Analysis
We discuss a new neural network-based direction of arrival estimation scheme that tackles the estimation task as a multidimensional classification problem.
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder
An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection.
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling
In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling.
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images
In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work.
Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques
In this paper, we present a machine learning approach for estimating the number of incident wavefronts in a direction of arrival scenario.
