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

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 …

In Industry 4.0, massive heterogeneous industrial devices generate a great deal of data with
different quality of service requirements, and communicate via industrial wireless networks …

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 …

Carrier sensing multiple access/collision avoidance (CSMA/CA) is the backbone MAC
protocol for IEEE 802.11 networks. However, tuning the binary exponential back-off (BEB) …

This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that
employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep …

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

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 …

In this paper, we conduct a survey of the literature about reinforcement learning (RL)-based
medium access control (MAC) protocols. As the scale of the wireless ad hoc network …

This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless
networks with imperfect channels. In the heterogeneous networks, multiple wireless …