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 …

In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Model-free techniques, such as machine learning (ML), have recently attracted much
interest toward the physical layer design (eg, symbol detection, channel estimation, and …

Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …

Deep learning (DL) has been applied to the physical layer of wireless communication
systems, which directly extracts environment knowledge from data and outperforms …

Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the
forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks …

A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous
network framework for the next generation mobile communications. Moreover, federated …

Millimeter wave massive multi input multi output (mmWave m-MIMO) holds immense
potential for beyond 5G networks, however its practical implementation faces hurdles in …

Artificial intelligence (AI) has become a promising solution for meeting the stringent
performance requirements on wireless physical layer in sixth-generation (6G) …