Radio transformer networks: Attention models for learning to synchronize in wireless systems

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2017, Pages 662-666

  O'Shea, Timothy J ^a   Pemula, Latha ^a   Batra, Dhruv ^a   Clancy, T Charles ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Attention Models; Cognitive Radio; Con volutional Autoencoders; Deep Learning; Machine Learning; Neural Networks; Radio communications; Radio Transformer Networks; RadioML; Signal Processing; Software Radio; Spatial Transformer Networks; Synchronization

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP LEARNING; ELECTRIC TRANSFORMERS; LEARNING SYSTEMS; MODULATION; NEURAL NETWORKS; RADIO COMMUNICATION; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; SOFTWARE RADIO; STRUCTURAL OPTIMIZATION; SYNCHRONIZATION;

ATTENTION MODEL; AUTOENCODERS; CLASSIFICATION ACCURACY; IDENTICAL SYSTEMS; MODULATION RECOGNITION; RADIOML; SPARSE REPRESENTATION; WIRELESS SYSTEMS;

COGNITIVE RADIO;

EID: 85016274803     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2016.7869126     Document Type: Conference Paper

References (9)

1. C. Clancy, J. Hecker, E. Stuntebeck, and T. O'Shea, "Applications of machine learning to cognitive radio networks", Wireless Communications, IEEE, vol. 14, no. 4, pp. 47-52, 2007.
2. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, "Theano: A CPU and GPU math expression compiler", in Proceedings of the Python for Scientific Computing Conference (SciPy), Oral Presentation, Austin, TX, Jun. 2010.
3. D. Kingma and J. Ba, "Adam: A method for stochastic optimization", ArXiv preprint arXiv: 1412. 6980, 2014.
4. V. Mnih, N. Heess, A. Graves, et al., "Recurrent models of visual attention", in Advances in Neural Information Processing Systems, 2014, pp. 2204-2212.
5. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting", The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
6. M. Abadi, A. Agarwal, et al., Tensorflow: Large-scale machine learning on heterogeneous systems, Software available from tensorflow. org, 2015. [Online]. Available: http: //tensorflow. org/.
7. F. Chollet, Keras, https: //github. com/fchollet/keras, 2015.
8. M. Jaderberg, K. Simonyan, A. Zisserman, et al., "Spatial transformer networks", in Advances in Neural Information Processing Systems, 2015, pp. 2008-2016.
9. T. J. O'Shea and J. Corgan, "Convolutional radio modulation recognition networks", CoRR, vol. Abs/1602. 04105, 2016. [Online]. Available: http : / / arxiv. org / abs / 1602. 04105.