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

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Non-orthogonal communications are expected to play a key role in future wireless systems.
In downlink transmissions, the data symbols are broadcast from a base station to different …

Recent years have witnessed growing interest in the application of deep neural networks
(DNNs) for receiver design, which can potentially be applied in complex environments …

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 …

Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in
communication networks are adaptation and monitoring. Adaptation refers to the need to …

The design of methods for inference from time sequences has traditionally relied on
statistical models that describe the relation between a latent desired sequence and the …

To attain high quality of service (QoS) with efficient power consumption with minimum delay
through Wireless Local Area Network (WLAN) through mesh network is an important …

Meta-learning, or learning to learn, offers a principled framework for few-shot learning. It
leverages data from multiple related learning tasks to infer an inductive bias that enables …

Predicting fading channels is a classical problem with a vast array of applications, including
as an enabler of artificial intelligence (AI)-based proactive resource allocation for cellular …