This paper proposes a medium access control (MAC) protocol based on deep reinforcement
learning (DRL), ie, multi-channel transmit deep-reinforcement learning multi-channel access …

This paper investigates a new medium access control (MAC) protocol for multi-channel
heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to …

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

In heterogeneous wireless networks, when multiple nodes need to share the same wireless
channel, they face the issue of multiple access, which necessitates a Medium Access …

This paper investigates a deep reinforcement learning algorithm based on dueling deep
recurrent Q‐network (Dueling DRQN) for dynamic multichannel access in heterogeneous …

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 …

We consider the problem of dynamic multichannel access for transmission maximization in
multiuser wireless communication networks. The objective is to find a multiuser strategy that …

This paper deals with the problem of the dynamic multichannel access (MCA) based on the
LTE-WLAN aggregation in dynamic heterogeneous networks. To ensure the users' …

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

This paper proposes a resource allocation scheme for hybrid multiple access involving both
orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The …