This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

We consider a dynamic multichannel access problem, where multiple correlated channels
follow an unknown joint Markov model and users select the channel to transmit data. The …

In this article, we propose a blockchain verification protocol as a method for enabling and
securing spectrum sharing in moving cognitive radio (CR) networks. The spectrum-sharing …

Widely regarded as one of the most promising emerging technologies for driving the future
development of wireless communications, cognitive radio has the potential to mitigate the …

In this paper, we consider a class of restless multiarmed bandit processes (RMABs) that
arises in dynamic multichannel access, user/server scheduling, and optimal activation in …

This paper considers opportunistic communication over multiple channels where the state
(ldquogoodrdquo or ldquobadrdquo) of each channel evolves as independent and …

We consider the following fundamental problem in the context of channelized dynamic
spectrum access. There are M secondary users and N¿ M orthogonal channels. Each …

To make efficient use of limited spectral resources, we in this work propose a deep actor-
critic reinforcement learning based framework for dynamic multichannel access. We …