Predicting congestion level in wireless networks using an integrated approach of supervised and unsupervised learning

A Thapaliya, J Schnebly… - 2018 9th IEEE Annual …, 2018 - ieeexplore.ieee.org
The usage data from user devices can be analyzed to answer a number of possible
questions in regards to congestion, access point (AP) load balancing, user mobility trends …

Environmental impacts on hardware-based link quality estimators in wireless sensor networks

W Liu, Y Xia, D Zheng, J Xie, R Luo, S Hu - Sensors, 2020 - mdpi.com
Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use
physical layer parameters to estimate packet reception ratio, which has advantages of high …

Optimal RTS threshold for IEEE 802.11 WLANs: basic or RTS/CTS?

Y Yin, Y Gao, S Manzoor, X Hei - 2019 IEEE SmartWorld …, 2019 - ieeexplore.ieee.org
IEEE 802.11 standard provides two access mechanisms for its distributed coordination
function (DCF) protocol, the default basic access mechanism and the optional request-to …

Artificial neural network-based uplink power prediction from multi-floor indoor measurement campaigns in 4G networks

T Mazloum, S Wang, M Hamdi… - Frontiers in Public …, 2021 - frontiersin.org
Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in
the number of user equipment has strengthened public concerns on human exposure to …

Low Complexity High Speed Deep Neural Network Augmented Wireless Channel Estimation

V Singh, BT Tanaji, S Darak - arXiv preprint arXiv:2311.08689, 2023 - arxiv.org
The channel estimation (CE) in wireless receivers is one of the most critical and
computationally complex signal processing operations. Recently, various works have shown …

Wireless network simulation to create machine learning benchmark data

M Katzef, AC Cullen, T Alpcan… - … 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
While several wireless network simulators exist, the absence of modern, standardised
network datasets may adversely affect the application of machine learning methods to …

CNN-based indoor path loss modeling with reconstruction of input images

H Cheng, H Lee, S Ma - 2018 International Conference on …, 2018 - ieeexplore.ieee.org
Convolutional Neural Networks (CNNs) have shown surprisingly good performance in both
classification and regression problems. Given a floor plan of a building and indoor …

Analysis of deep learning based path loss prediction from satellite images

MZ Alam, HF Ates, T Baykas… - 2021 29th signal …, 2021 - ieeexplore.ieee.org
Determining the channel model parameters of a wireless communication system, either by
measurements or by running electromagnetic propagation simulations, is a time-consuming …

Deep learning-based estimator for fast harq feedback in urllc

S AlMarshed, D Triantafyllopoulou… - 2021 IEEE 32nd …, 2021 - ieeexplore.ieee.org
Autonomous systems and mission-critical applications demand ultra-reliable low-latency
communication (URLLC). To build wireless communication networks capable of …

Intelligent Transmission Scheduling for Edge Sensing in Industrial IoT Systems

T Jin, Y Ma, Z Ji, C Chen - GLOBECOM 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Edge sensing supported by wireless transmission is one of the core enabling technologies
for flexibly implementing the Industrial Internet of Things (IIoT). Balancing network resource …