We propose a novel deep learning-based channel estimation technique for high-
dimensional communication signals that does not require any training. Our method is …

Research on machine learning for channel estimation, especially neural network solutions
for wireless communications, is attracting significant current interest. This is because …

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless
communication systems, especially under some imperfect environments. However, even …

Channel estimation is a critical task in digital communications that greatly impacts end-to-
end system performance. In this work, we introduce a novel approach for multiple-input …

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising
technology for future wireless communications. The deployment of XL-MIMO, especially at …

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging
due to the beam squint effect. To solve the problem, we propose a learnable iterative …

This paper presents a novel compressed sensing (CS) approach to high dimensional
wireless channel estimation by optimizing the input to a deep generative network. Channel …

Communication at high carrier frequencies such as millimeter wave (mmWave) and
terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence …

Recently, machine learning has been introduced in communications to deal with channel
estimation. Under nonlinear system models, the superiority of machine learning based …

With the depletion of spectrum, wireless communication systems turn to exploit large
antenna arrays to achieve the degree of freedom in the space domain, such as millimeter …