Real-time throughput prediction for cognitive Wi-Fi networks
Wi-Fi as a wireless networking technology has become a widely acceptable commonplace.
Over the course of time, the applications landscape of Wi-Fi networks is growing …
Over the course of time, the applications landscape of Wi-Fi networks is growing …
Towards more reliable deep learning-based link adaptation for WiFi 6
The problem of selecting the modulation and coding scheme (MCS) that maximizes the
system throughput, known as link adaptation, has been investigated extensively, especially …
system throughput, known as link adaptation, has been investigated extensively, especially …
Contention resolution in Wi-Fi 6-enabled Internet of Things based on deep learning
C Chen, J Li, V Balasubramaniam, Y Wu… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a
result, a multitude of IoT devices transmit various information through wireless …
result, a multitude of IoT devices transmit various information through wireless …
[引用][C] Machine learning and software defined networks for high-density wlans
Á López-Raventós, F Wilhelmi… - arXiv preprint arXiv …, 2018 - Apr
Online primary channel selection for dynamic channel bonding in high-density WLANs
S Barrachina-Muñoz, F Wilhelmi… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
In order to dynamically adapt the transmission bandwidth in wireless local area networks
(WLANs), dynamic channel bonding (DCB) was introduced in IEEE 802.11 n. It has been …
(WLANs), dynamic channel bonding (DCB) was introduced in IEEE 802.11 n. It has been …
Conflict graph-based model for IEEE 802.11 networks: A Divide-and-Conquer approach
M Stojanova, T Begin, A Busson - Performance Evaluation, 2019 - Elsevier
Abstract WLANs (Wireless Local Area Networks) based on the IEEE 802.11 standard have
become ubiquitous in our daily lives. We typically augment the number of APs (Access …
become ubiquitous in our daily lives. We typically augment the number of APs (Access …
DeepWiPHY: Deep learning-based receiver design and dataset for IEEE 802.11 ax systems
In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the
channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) …
channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) …
Channel Selection for Wi-Fi 7 Multi-Link Operation via Optimistic-Weighted VDN and Parallel Transfer Reinforcement Learning
PE Iturria-Rivera, M Chenier… - 2023 IEEE 34th …, 2023 - ieeexplore.ieee.org
Dense and unplanned IEEE 802.11 Wireless Fidelity (Wi-Fi) deployments and the
continuous increase of throughput and latency stringent services for users have led to …
continuous increase of throughput and latency stringent services for users have led to …
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 …
Deep reinforcement learning paradigm for dense wireless networks in smart cities
Wireless local area networks (WLANs) are widely deployed for Internet-centric data
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …