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 …
Multi-armed bandits for spectrum allocation in multi-agent channel bonding WLANs
While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area
networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied …
networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied …
Distributed deep reinforcement learning with wideband sensing for dynamic spectrum access
Dynamic Spectrum Access (DSA) improves spectrum utilization by allowing secondary users
(SUs) to opportunistically access temporary idle periods in the primary user (PU) channels …
(SUs) to opportunistically access temporary idle periods in the primary user (PU) channels …
A study of wireless communications with reinforcement learning
W Lei - 2022 - diva-portal.org
The explosive proliferation of mobile users and wireless data traffic in recent years pose
imminent challenges upon wireless system design. The trend for wireless communications …
imminent challenges upon wireless system design. The trend for wireless communications …
Cooperate or not Cooperate: Transfer Learning with Multi-Armed Bandit for Spatial Reuse in Wi-Fi
PE Iturria-Rivera, M Chenier… - … Machine Learning in …, 2024 - ieeexplore.ieee.org
The exponential increase in the demand for high-performance services such as streaming
video and gaming by wireless devices has posed several challenges for Wireless Local …
video and gaming by wireless devices has posed several challenges for Wireless Local …
Optimized transfer learning for wireless channel selection
M Askarizadeh, M Hussien, M Zare… - 2021 IEEE Global …, 2021 - ieeexplore.ieee.org
A key challenge facing any channel selection technique is the dynamic nature of wireless
channels. To address this issue, reinforcement learning techniques have widely been used …
channels. To address this issue, reinforcement learning techniques have widely been used …
Dynamic channel access via meta-reinforcement learning
In this paper, we address the channel access problem in a dynamic wireless environment
via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications …
via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications …
Implications of Centralized and Distributed Multi-Agent Deep Reinforcement Learning in Dynamic Spectrum Access
AM Ibrahim, KLA Yau, LM Hong - 2022 IEEE 6th International …, 2022 - ieeexplore.ieee.org
Multi-agent Deep Reinforcement Learning (MADRL) has been applied to a plethora of state-
of-the-art applications such as resource allocations and network routing in both centralized …
of-the-art applications such as resource allocations and network routing in both centralized …
A federated reinforcement learning framework for incumbent technologies in beyond 5G networks
Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G)
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …
Accelerating model-free reinforcement learning with imperfect model knowledge in dynamic spectrum access
Current studies that apply reinforcement learning (RL) to dynamic spectrum access (DSA)
problems in wireless communications systems mainly focus on model-free RL (MFRL) …
problems in wireless communications systems mainly focus on model-free RL (MFRL) …