Search Results for author:
Found 6 papers, 0 papers with code
New Optimization Method Based on Neural Networks for Designing Radar Waveforms With Good Correlation
Owing to advances in the overall performance and anti-interception capability of radars, the designs of radar waveforms with good correlation properties have been a concern for researchers.
DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network
The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods.
SigNet: A Novel Deep Learning Framework for Radio Signal Classification
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer
In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process.
Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios
We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.
Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification
In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900, 000 and 13, 000, 000, respectively, and with categories of 3, 143 and 5, 157 respectively.
