During the last decade, deep learning (DL) techniques have demonstrated the capabilities
in various applications with a large number of labeled samples. Unfortunately, it is normally …

Traditional blind source separation (BSS) methods often want no additional noise effects.
But in practice, noise is ubiquitous, and there are even cases with low signal‐to‐noise ratios …

We study the potential of data-driven deep learning methods for separation of two
communication signals from an observation of their mixture. In particular, we assume …

This paper explores the important role of blind source separation (BSS) techniques in
separating M mixtures including N sources using a dual-sensor array, ie, M= 2, and …

This paper addresses the critical problem of interference rejection in radio-frequency (RF)
signals using a novel, data-driven approach that leverages state-of-the-art AI models …

Signal processing algorithms are crucial for the integrity of information transfer in wireless
transceivers, with mean square error (MSE) serving as a pivotal metric for performance …

Classical Blind Source Separation (BSS) methods rarely attain exact separation, even under
noiseless conditions. In addition, they often rely on particular structural or statistical …

ABSTRACT Brain-Computer Interfaces (BCI) techniques have greatly impacted people's life
in different ways. They are normally implemented by analyzing electroencephalography …

We address the problem of source separation from noisy mixtures in a semi-blind scenario,
with stationary, temporally-diverse Gaussian sources and known spectra. In such noisy …