Adaptive contention window design using deep Q-learning
We study the problem of adaptive contention window (CW) design for random-access
wireless networks. More precisely, our goal is to design an intelligent node that can …
wireless networks. More precisely, our goal is to design an intelligent node that can …
Contention window optimization in IEEE 802.11 ax networks with deep reinforcement learning
W Wydmański, S Szott - 2021 IEEE wireless communications …, 2021 - ieeexplore.ieee.org
The proper setting of contention window (CW) values has a significant impact on the
efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is …
efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is …
Learning to continuously optimize wireless resource in episodically dynamic environment
There has been a growing interest in developing data-driven, in particular deep neural
network (DNN) based methods for modern communication tasks. For a few popular tasks …
network (DNN) based methods for modern communication tasks. For a few popular tasks …
Deep reinforcement learning for simultaneous sensing and channel access in cognitive networks
We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks,
consisting of primary users (PUs) and secondary users (SUs), where only partial …
consisting of primary users (PUs) and secondary users (SUs), where only partial …
Trajectory optimization for autonomous flying base station via reinforcement learning
H Bayerlein, P De Kerret… - 2018 IEEE 19th …, 2018 - ieeexplore.ieee.org
In this work, we study the optimal trajectory of an unmanned aerial vehicle (UAV) acting as a
base station (BS) to serve multiple users. Considering multiple flying epochs, we leverage …
base station (BS) to serve multiple users. Considering multiple flying epochs, we leverage …
Real-time channel management in WLANs: Deep reinforcement learning versus heuristics
O Iacoboaiea, J Krolikowski, ZB Houidi… - 2021 IFIP Networking …, 2021 - ieeexplore.ieee.org
Today's WLANs rely on a centralized Access Controller (AC) entity for managing distributed
wireless Access Points (APs) to which user devices connect. The availability of real-time …
wireless Access Points (APs) to which user devices connect. The availability of real-time …
Sample efficient reinforcement learning in mixed systems through augmented samples and its applications to queueing networks
This paper considers a class of reinforcement learning problems, which involve systems with
two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states …
two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states …
[HTML][HTML] Applying multi-agent deep reinforcement learning for contention window optimization to enhance wireless network performance
This paper investigates the Contention Window (CW) optimization problem in multi-agent
scenarios, where the fully cooperative among mobile stations is considered. A partially …
scenarios, where the fully cooperative among mobile stations is considered. A partially …
Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective
There has been a growing interest in developing data-driven, and in particular deep neural
network (DNN) based methods for modern communication tasks. These methods achieve …
network (DNN) based methods for modern communication tasks. These methods achieve …
Stateless reinforcement learning for multi-agent systems: The case of spectrum allocation in dynamic channel bonding WLANs
S Barrachina-Muñoz, A Chiumento… - 2021 Wireless Days …, 2021 - ieeexplore.ieee.org
Spectrum allocation in the form of primary channel and bandwidth selection is a key factor
for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with …
for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with …