Recognition of radio signals with deep learning Neural Networks

Nowadays, the use of Artificial Neural Networks shows continuity in many different applications. In particular, the use of deep learning type neural networks, seen as impractical previously, is increasing by processing power growth. Software Defined Radio (SDR) is another hot topic called as Digital Radio concept. With a growing interest in the environment was formed especially by radio amateurs. Analyzing signals and making sense out of them by SDR is accepted in many areas. In this study, design and implementation of a sample work has been subjected for identification of received radio signals on SDR with deep learning neural networks. (DLNN).

[1]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[2]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[5]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[6]  Eugene Grayver Implementing Software Defined Radio , 2013 .

[7]  Chong Wang,et al.  Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.

[8]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[9]  W. A. Clark,et al.  Simulation of self-organizing systems by digital computer , 1954, Trans. IRE Prof. Group Inf. Theory.

[10]  Joseph F. Murray,et al.  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.

[11]  Florent Perronnin,et al.  High-dimensional signature compression for large-scale image classification , 2011, CVPR 2011.

[12]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[13]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.