Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been
in the limelight because of many recent breakthroughs in artificial intelligence, including …

The areas of machine learning and communication technology are converging. Today's
communications systems generate a huge amount of traffic data, which can help to …

How can machine learning help the design of future communication networks-and how can
future networks meet the demands of emerging machine learning applications? Discover the …

Many real-world systems problems require reasoning about the long term consequences of
actions taken to configure and manage the system. These problems with delayed and often …

Numerical validation is at the core of machine learning research as it allows us to assess the
actual impact of new methods, and to confirm the agreement between theory and practice …

Deep learning has proven itself to be a powerful tool to develop datadriven signal
processing algorithms for challenging engineering problems. By learning the key features …

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 …

Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore,
this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled …

Integrated space-air-ground networks promise to offer a valuable solution space for
empowering the sixth generation of communication networks (6G), particularly in the context …

Non terrestrial networks (NTN) involving 'in the sky'objects such as low-earth orbit satellites,
high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected …