Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks
J Lei, D Tan, X Ma, Y Wang - Ad Hoc Networks, 2024 - Elsevier
Abstract Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel
utilization efficiency although it always is regarded as a promising distributed channel …
utilization efficiency although it always is regarded as a promising distributed channel …
Non-uniform time-step deep Q-network for carrier-sense multiple access in heterogeneous wireless networks
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 …
employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep …
Carrier-sense multiple access for heterogeneous wireless networks using deep reinforcement learning
This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that
employ deep reinforcement learning (DRL) techniques for heterogeneous wireless …
employ deep reinforcement learning (DRL) techniques for heterogeneous wireless …
Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms
In flying ad hoc networks (FANET), medium access layer (MAC) protocols play an essential
role in ensuring better network performance. The effective utilization of network resources …
role in ensuring better network performance. The effective utilization of network resources …
Enhancing WiFi multiple access performance with federated deep reinforcement learning
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) …
protocol for IEEE 802.11 networks. However, tuning the binary exponential back-off (BEB) …
Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
A Dueling Deep Recurrent Q‐Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks
H Chen, H Zhao, L Zhou, J Zhang, Y Liu… - Wireless …, 2022 - Wiley Online Library
This paper investigates a deep reinforcement learning algorithm based on dueling deep
recurrent Q‐network (Dueling DRQN) for dynamic multichannel access in heterogeneous …
recurrent Q‐network (Dueling DRQN) for dynamic multichannel access in heterogeneous …
Deep reinforcement learning-based multichannel access for industrial wireless networks with dynamic multiuser priority
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 …
different quality of service requirements, and communicate via industrial wireless networks …
Deep Reinforcement Learning Heterogeneous Channels for Poisson Multiple Access
X Zhang, P Chen, G Yu, S Wang - Mathematics, 2023 - mdpi.com
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 …
learning (DRL), ie, multi-channel transmit deep-reinforcement learning multi-channel access …
Multi-channel opportunistic access for heterogeneous networks based on deep reinforcement learning
This paper investigates a new medium access control (MAC) protocol for multi-channel
heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to …
heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to …