With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

As we advance towards 6G communication systems, the number of network devices
continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS) …

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 …

Accurate flight delay prediction is fundamental to establish the more efficient airline
business. Recent studies have been focused on applying machine learning methods to …

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 …

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 …

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 …

Cognitive radio has been proposed to improve spectrum utilization in wireless
communication. Spectrum sensing is an essential component of cognitive radio. The …

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 …

Transfer learning is a pervasive technology in computer vision and natural language
processing fields, yielding exponential performance improvements by leveraging prior …