… In section III, we present the deep reinforcement learning technique used in this paper in
details. The MC-DLMA protocol is designed in Section IV, and Section V gives the performance …

… channels. In the heterogeneous networks, multiple wireless networks adopt different … This
paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for …

… and the coherence time of wireless channels is typically shorter than the … a deep learning
(DL) based algorithm to accelerate the power allocations in a general interference-channel sce…

… aims for heterogeneous networking in which LTE-U base stations coexist with WiFi APs, its …
use the WiFi channels in a non-disruptive way; we focus on sharing an uplink channel among …

… channels and to expedite more efficient spectrum utilization, we exploit the advanced deep
reinforcement … , referred to as multi-channel deep-reinforcement learning multiple access (MC…

… utilization rate when multiple channels are idle at the same time. To address these issues,
we consider a heterogeneous wireless network (HetNet) with multiple orthogonal channels. …

… a Deep Recurrent Q-Network (DRQN). Specifically, we consider a scenario with multiple
independent channels and multiple heterogeneous … to predict the future channel state based on …

… Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for
heterogeneous WLANs to reduce transmission delay, where the APs have different channel …

… Heterogeneous cellular networks (HetNets) have been … We propose a distributed channel
access and power control scheme… In particular, we exploit a deep reinforcement learning(DRL)-…

… state containing the spectrum state and the channel gain state. Considering that the
spectrum state and channel gain state are heterogeneous information, we designed the HIF-DQN …