In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently
use the spectrum. Spectrum occupancy prediction is a convenient way of revealing …

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum
utilization in cognitive radio systems. Spectrum prediction offers a convenient means for …

In cognitive radio (CR) systems, efficient spectrum utilization depends on the ability to
predict spectrum opportunities. Traditional statistical methods for spectrum occupancy …

A new paradigm for large-scale spectrum occupancy learning based on long short-term
memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum …

In general, judging the use/idle state of the wireless spectrum is the foundation for cognitive
radio users (secondary users, SUs) to access limited spectrum resources efficiently. Rich …

The application of machine learning models to spectrum sensing in cognitive radio is not
uncommon in literature, but most of these models fail to consider temporal dependencies in …

Spectrum prediction is a promising technique for improving spectrum exploitation in
cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the …

Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically
inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference …

Spectrum occupancy information is necessary in a cognitive radio network (CRN) as it helps
in modeling and predicting the spectrum availability for efficient dynamic spectrum access …

For most existing spectrum sensing detectors, the design of their test statistics relies on
certain signal-noise model assumptions and hence, their detection performance heavily …