DeepMux: Deep-learning-based channel sounding and resource allocation for IEEE 802.11 ax
PK Sangdeh, H Zeng - IEEE Journal on Selected Areas in …, 2021 - ieeexplore.ieee.org
MU-MIMO and OFDMA are two key techniques in IEEE 802.11 ax standard. Although these
two techniques have been intensively studied in cellular networks, their joint optimization in …
two techniques have been intensively studied in cellular networks, their joint optimization in …
Improving IEEE 802.11 ax UORA performance: Comparison of reinforcement learning and heuristic approaches
Machine learning (ML) has gained attention from the network research community because
it can help solve difficult problems and potentially lead to groundbreaking achievements. In …
it can help solve difficult problems and potentially lead to groundbreaking achievements. In …
LB-SciFi: Online learning-based channel feedback for MU-MIMO in wireless LANs
Multi-user MIMO (MU-MIMO) is a key technology for current and next-generation wireless
local area networks (WLANs). While it has widely been deployed in WLANs, its potential is …
local area networks (WLANs). While it has widely been deployed in WLANs, its potential is …
Deep Learning-aided Channel Allocation Scheme for WLAN
In the wireless local area networks (WLANs) based on the IEEE 802.11 technology, the
limited set of channels is shared by a large number of access points (APs), which inevitably …
limited set of channels is shared by a large number of access points (APs), which inevitably …
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) …
A machine learning approach for IEEE 802.11 channel allocation
Today's communication is mainly done over wireless networks, with IEEE 802.11 (Wi-Fi) at
the forefront. There are billions of devices and millions of access points (APs), but only very …
the forefront. There are billions of devices and millions of access points (APs), but only very …
Distributed convolutional deep reinforcement learning based OFDMA MAC for 802.11 ax
D Kotagiri, K Nihei, T Li - ICC 2021-IEEE International …, 2021 - ieeexplore.ieee.org
The IEEE 802.11 ax also known as Wi-Fi 6, incorporates multi-user (MU) Orthogonal
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …
The challenges of Scheduling and Resource Allocation in IEEE 802.11 ad/ay
The IEEE 802.11 ad Wi-Fi amendment enables short-range multi-gigabit communications in
the unlicensed 60 GHz spectrum, unlocking new interesting applications such as wireless …
the unlicensed 60 GHz spectrum, unlocking new interesting applications such as wireless …
A deep reinforcement learning based approach for channel aggregation in IEEE 802.11 ax
Channel aggregation (CA) is proposed in IEEE 802.11 ax to allow wireless users to
aggregate multiple available channels, either contiguous or non-contiguous, to improve the …
aggregate multiple available channels, either contiguous or non-contiguous, to improve the …
Uplink resource allocation in IEEE 802.11 ax
S Bhattarai, G Naik, JMJ Park - ICC 2019-2019 IEEE …, 2019 - ieeexplore.ieee.org
One of the notable features of the upcoming Wireless Fidelity (Wi-Fi) standard-namely, IEEE
802.11 ax-is the use of Multi-User Orthogonal Frequency Division Multiple Access (MU …
802.11 ax-is the use of Multi-User Orthogonal Frequency Division Multiple Access (MU …