… of scientific publications reporting the application of deep learning for RF human sensing. …
the application of deep learning to this field of research. Since use of deep learning in wireless …

… deep learning minimizes the margin of error in the detection of the free spectrum. This research
provides an insight into using a deep … AI-based deep learning technique is used in many …

… deep learning-based CNN-LSTM spectrum sensing detector. Different from the traditional
detectors… moreover, it is able to simultaneously learn the signal energy-correlation features and …

… Nowadays, deep learning has become a key research component of the Sixth-Generation …
of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal …

… Our results strongly suggest that deep learning on simulations, boosted by transfer learning
on real data, will likely play a key role in the future of RFI flagging of radio astronomy data. …

… Deep learning based methods have the potential to focus on various aspects, including …
This article proposes a data-driven deep learning based model to classify the received raw …

… In particular, we design a flexible deep learning framework SigNet based on sliding … 1) We
develop SigNet as a novel deep learning framework for signal classification by introducing a …

… of using deep learning algorithms, particularly fully connected deep neural … radio frequency
bands. We proposed a supervised deep learning algorithm with fully-connected deep neural …

… , hence the state-of-the-art deep learning technique is leveraged [7]–[17]. This … deep
learning-based RFFI, namely system stability, selection of signal representations and deep learning …

… radio performance gains achieved by applying deep learning. Even though we have studied
different approaches in improving the efficiency of the network, a further study on adapting …