Capturing high-dimensional (HD) data is a long-term challenge in signal processing and
related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥ …

The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of
intelligent networks with the provision of some isolated artificial intelligence (AI) operations …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

This paper deals with the use of emerging deep learning techniques in future wireless
communication networks. It will be shown that the data-driven approaches should not …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …

We present and discuss several novel applications of deep learning for the physical layer.
By interpreting a communications system as an autoencoder, we develop a fundamental …