Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement
learning (RL) architecture. It has been able to perform a wide range of complex decision …

Modern radios, such as 5G New Radio, feature a large set of physical-layer control knobs in
order to support an increasing number of communication scenarios spanning multiple use …

In order to overcome the limitation of traditional reinforcement learning techniques on the
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …

With recent advances, Artificial Intelligence (AI) and Machine Learning (ML) approaches
have emerged to show great promise in the field of wireless communications. Although …

We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …

The rapid growth of data traffic has pushed the mobile telecommunication industry towards
the adoption of fifth generation (5G) communications. Cloud radio access network (CRAN) …

Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs)
supporting complex and dynamic tasks by collaboratively exploiting the computation and …

Sixth generation mobile networks (6G) may experience a huge evolution on vertical industry
scenarios, where deep edge networks () become an important network structure for the …

Wireless networking using GHz or THz spectra has encouraged mobile service providers to
deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As …

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising
concept in future 5G technologies where all DSP processors are moved into a central base …