Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will
allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed …

One of the key problems in spectrum sensing is to design the test statistic. Existing methods
generally exploit the model-based features as the test statistic, such as energies and …

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way
to obtain the available spectrum for objects. Wide-band spectrum sensing plays an important …

The state-of-the-art goodness-of-fit (GoF) test algorithms for spectrum sensing directly make
decisions based on temporal samples or energies of the observations, which perform well …

The eigenvalues of the sample covariance matrix can capture signal correlations and noise
characteristics well, which are widely used for spectrum sensing in cognitive radio networks …

In this paper, we investigate resource allocation design for unmanned aerial vehicle (UAV)-
enabled communication systems, where a UAV is dispatched to provide communications to …

Predictive beamforming design is an essential task in realizing high-mobility integrated
sensing and communication (ISAC), which highly depends on the accuracy of the channel …

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme
eigenvalues. A general framework for detector design involving model-driven and data …

State-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge
of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum …

In cognitive networks, efficient spectrum sensing is of great importance for communication of
unlicensed secondary users (SU) without interfering with the communication of licensed …