There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

Spectrum inference, also known as spectrum prediction in the literature, is a promising
technique of inferring the occupied/free state of radio spectrum from already …

This paper investigates the issue of spatial-temporal opportunity detection for spectrum-
heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at …

Cognitive radio (CR) is an enabling technology for numerous new capabilities such as
dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this …

Due to exponential growth in demand for radio spectrum for wireless communication
networking, the radio spectrum has become over-crowded. The fixed spectrum allocation …

The problem of channel quality prediction in cognitive radio networks is investigated in this
paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov …

A new statistical model, in the form of a hidden bivariate Markov chain observed through a
Gaussian channel, is developed and applied to spectrum sensing for cognitive radio. We …

Spectrum sensing detects the availability of the radio frequency spectrum, which is essential
and vital to cognitive radio. Traditional techniques for spectrum sensing fail to take the …

Security concerns are raised for collaborative spectrum sensing due to its vulnerabilities to
the potential attacks from malicious secondary users. Most existing malicious user detection …

In this paper, we propose a novel spectrum occupancy model designed to generate
accurate temporal and frequency behavior of various wireless transmissions. Our proposed …