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Found 21 papers, 4 papers with code
Score-based Source Separation with Applications to Digital Communication Signals
We propose a new method for separating superimposed sources using diffusion-based generative models.
Online Segmented Recursive Least-Squares for Multipath Doppler Tracking
Underwater communication signals typically suffer from distortion due to motion-induced Doppler.
Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch
Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments.
A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch
A bilateral (i. e., upper and lower) bound on the mean-square error under a general model mismatch is developed.
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communication systems.
Can Shadows Reveal Biometric Information?
We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way.
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture.
Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains.
Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach
Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e. g., using time-difference of arrivals).
A Semi-Blind Method for Localization of Underwater Acoustic Sources
We also derive the Cram\'er-Rao bound for this model, which can be used to guide the placement of collections of receivers so as to optimize localization accuracy.
Blind Modulo Analog-to-Digital Conversion
In a growing number of applications, there is a need to digitize signals whose spectral characteristics are challenging for traditional Analog-to-Digital Converters (ADCs).
One-Bit Direct Position Determination of Narrowband Gaussian Signals
One of the main drawbacks of the well-known Direct Position Determination (DPD) method is the requirement that raw signal data be transferred to a common processor.
Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting
One-bit quantization has recently become an attractive option for data acquisition in cutting edge applications, due to the increasing demand for low power and higher sampling rates.
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing.
Iterative Symbol Recovery For Power Efficient DC Biased Optical OFDM Systems
Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multi-carrier digital communication technique.
Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals
However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in order to obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS) estimates of the sensors gains and phases offsets with asymptotically optimal weighting, based only on the empirical covariance matrix of the measurements.
Blind Determination of the Number of Sources Using Distance Correlation
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed.
Performance Analysis of the Gaussian Quasi-Maximum Likelihood Approach for Independent Vector Analysis
As an immediate consequence of this result, we provide an asymptotically attainable lower bound on the resulting ISRs.
The Extended "Sequentially Drilled" Joint Congruence Transformation and its Application in Gaussian Independent Vector Analysis
In this paper we extend our results to the IVA problem, showing how the ML solution for the Gaussian model (with arbitrary covariance and cross-covariance matrices) takes the form of an extended SeDJoCo problem.
"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals
Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linear Wiener-like filter as if the unknown deterministic signal were known.
Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting
By exploiting results from fundamental statistics and the recently re-emerging tensor theory, we derive a consistent blind CPD-based DOAs estimate without prior assumptions on the array configuration.
