Spectrum sensing is a critical component of cognitive radio systems, enabling the detection
and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and …

Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz
bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly …

Spectrum sensing is one of the key problems in the cognitive radio network. Existing
spectrum sensing methods commonly use deep learning models such as the convolutional …

Spectrum sensing methods based on deep learning require massive amounts of labeled
samples. To address the scarcity of labeled samples in a real radio environment, this paper …

One of the key challenges in wideband spectrum sensing is that it has to be performed with
extremely low latency and high accuracy over a large band to detect tiny spectrum holes …

In this paper, we propose a data-driven framework for collaborative wideband spectrum
sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the …

In the field of radio surveillance and cognitive radio, the reception of a signal is usually made
in a non-cooperative manner, which means there exists little prior information to detect the …

The traditional spectrum sensing algorithm based on deep learning requires a large number
of labeled samples for model training, but it is difficult to obtain them in the actual sensing …

Artificial intelligence has found its way into space, and similar to the situation on ground
demands powerful hardware to unfold its full potential. With the heterogeneous compute …

In this work, a novel signal detection approach for dense co-channel environments is
developed that leverages the intelligent combination of traditional signal preprocessing and …