This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …

In the next generation wireless networks, more applications will emerge, covering virtual
reality movies, augmented reality, holographic three-dimensional telepresence, haptic …

The potential applications of deep learning to the media access control (MAC) layer of
wireless local area networks (WLANs) have already been progressively acknowledged due …

In this paper, we propose a new framework, exploiting the multi-agent deep deterministic
policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment …

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …

Motivated partially by a control-theoretic viewpoint, we propose a game-theoretic model,
called random access game, for contention control. We characterize Nash equilibria of …

In this paper, we address the rate control problem in a multi-hop random access wireless
network, with the objective of achieving proportional fairness amongst the end-to-end …

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 …

This papera 1 investigates deep reinforcement learning (DRL) based on a Recurrent Neural
Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to …

Exploiting the advantages of both non-orthogonal multiple access technique and millimeter-
wave communications requires joint efficient resource allocation techniques toward …