Self-organization as applied to cellular networks is usually referred to Selforganizing
Networks (SONs), and it is a key driver for improving Operations, Administration, and …

This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …

Q-learning, a type of reinforcement learning, has gained increasing popularity in
autonomous mobile robot path planning recently, due to its self-learning ability without …

The fifth generation of wireless communications (5G) promises massive increases in traffic
volume and data rates, as well as improved reliability in voice calls. Jointly optimizing …

The model-based power allocation has been investigated for decades, but this approach
requires mathematical models to be analytically tractable and it has high computational …

In fact, optimizing path within short computation time still remains a major challenge for
mobile robotics applications. In path planning and obstacles avoidance, Q-Learning (QL) …

Optimizing radio transmission power and user data rates in wireless systems requires full
system observability. While the problem has been extensively studied in the literature …

In order to coexist with the WiFi systems in the unlicensed spectrum, Long Term Evolution
(LTE) networks can utilize periodically configured transmission gaps. In this paper …

Animals both explore and avoid novel objects in the environment, but the neural
mechanisms that underlie these behaviors and their dynamics remain uncharacterized …

In this paper, we focus on inter-cell interference coordination (ICIC) techniques in
heterogeneous network (HetNet) deployments, whereby macro-and picocells autonomously …