Transfer learning for wireless networks: A comprehensive survey

CT Nguyen, N Van Huynh, NH Chu… - Proceedings of the …, 2022 - ieeexplore.ieee.org
With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

L Alzubaidi, J Bai, A Al-Sabaawi, J Santamaría… - Journal of Big Data, 2023 - Springer
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Transfer learning for future wireless networks: A comprehensive survey

CT Nguyen, N Van Huynh, NH Chu, YM Saputra… - arxiv preprint arxiv …, 2021 - arxiv.org
With outstanding features, Machine Learning (ML) has been the backbone of numerous
applications in wireless networks. However, the conventional ML approaches have been …

Flight delay prediction based on aviation big data and machine learning

G Gui, F Liu, J Sun, J Yang, Z Zhou… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Accurate flight delay prediction is fundamental to establish the more efficient airline
business. Recent studies have been focused on applying machine learning methods to …

Breast cancer–detection system using PCA, multilayer perceptron, transfer learning, and support vector machine

HJ Chiu, THS Li, PH Kuo - IEEE Access, 2020 - ieeexplore.ieee.org
This study proposed a new processing method to predict breast cancer on the basis of nine
individual attributes, including age, body mass index, glucose, insulin, and a homeostasis …

Spectrum sensing in cognitive radio: A deep learning based model

H **ng, H Qin, S Luo, P Dai, L Xu… - Transactions on …, 2022 - Wiley Online Library
Spectrum sensing is an efficient technology for addressing the shortage of spectrum
resources. Widely used methods usually employ model‐based features as the test statistics …

Transfer learning for radio frequency machine learning: a taxonomy and survey

LJ Wong, AJ Michaels - Sensors, 2022 - mdpi.com
Transfer learning is a pervasive technology in computer vision and natural language
processing fields, yielding exponential performance improvements by leveraging prior …

When machine learning meets spectrum sharing security: Methodologies and challenges

Q Wang, H Sun, RQ Hu… - IEEE Open Journal of the …, 2022 - ieeexplore.ieee.org
The exponential growth of Internet connected systems has generated numerous challenges,
such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions …

Deep learning for spectrum sensing in cognitive radio

S Solanki, V Dehalwar, J Choudhary - Symmetry, 2021 - mdpi.com
The detection of primary user signals is essential for optimum utilization of a spectrum by
secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have …

Distributed unsupervised learning for interference management in integrated sensing and communication systems

X Liu, H Zhang, K Long, A Nallanathan… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Nowadays, the multi-access interference problem in the ISAC systems can not be ignored.
The study on interference management in ISAC has been envisioned as one of key …