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 …
O Lavi, N Shlezinger - IEEE Transactions on Communications, 2023 - ieeexplore.ieee.org
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 …
Emerging communication technologies, such as 5G and beyond, have introduced diverse requirements that demand high performance and energy efficiency at all levels …
T Raviv, N Shlezinger - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
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 …
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 …
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 …
I Buchnik, G Revach, D Steger… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
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 …
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn how to operate in …