Wireless sensor network (WSN) is an evergreen research area, which always looks for
energy efficiency. The main challenge for attaining energy efficiency in WSN is data …

Recent reinforcement learning approaches for continuous control in wireless mobile
networks have shown impressive results. But due to the lack of open and compatible …

Reinforcement Learning is gaining attention by the wireless networking community due to its
potential to learn good-performing configurations only from the observed results. In this work …

In this paper, we study how to optimize the transmission decisions of nodes aimed at
supporting mission-critical applications, such as surveillance, security monitoring, and …

A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is
proposed. The proposed scheme utilizes a reinforcement learning framework to …

Reinforcement learning means learning a policy-a mapping of observations into actions-
based on feedback from the environment. The learning can be viewed as browsing a set of …

Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a
dramatic improvement in decision-making and automated control problems. As a result, we …

The framework of cognitive wireless networks is expected to endow the wireless devices
with the cognition-intelligence ability with which they can efficiently learn and respond to the …

Mobile ad hoc networks (MANETs) consist of a set of nodes which can move freely and
communicate with each other wirelessly. Due to the movement of nodes and unlike wired …

Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …