The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …

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

Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …

Hybrid precoding plays a key role in realizing massive multiple-input multiple-output (MIMO)
transmitters with controllable cost. MIMO precoders are required to frequently adapt based …

Deep neural networks (DNNs) allow digital receivers to learn to operate in complex
environments. To do so, DNNs should preferably be trained using large labeled data sets …

Most research studies on deep learning (DL) applied to the physical layer of wireless
communication do not put forward the critical role of the accuracy-generalization trade-off in …

In the design of wireless receivers, deep neural networks (DNNs) can be combined with
traditional model-based receiver algorithms to realize modular hybrid model-based/data …

The Kalman filter (KF) is a widely used algorithm for tracking dynamic systems that are
captured by state space (SS) models. The need to fully describe an SS model limits its …

Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging
architectures integrating deep neural networks (DNNs) with traditional modular receiver …