MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||9|
|Published online||10 August 2018|
- Hwang T, Yang C, Wu G, Li S and Li G Y 2009 OFDM and its wireless applications: A survey IEEE Trans. Veh. Technol. 58 1673-94 [CrossRef] [Google Scholar]
- Banelli P, Buzzi S, Colavolpe G, Modenini A, Rusek F and Ugolini A 2014 Modulation Formats and Waveforms for 5G Networks: Who Will Be the Heir of OFDM? An overview of alternative modulation schemes for improved spectral efficiency IEEE Signal Processing Mag. 31 80-93 [Google Scholar]
- Dostert K 2001 Powerline communications (New Jersey: Prentice Hall) [Google Scholar]
- Stuber G L, Barry J R, McLaughlin S W, Li Y, Ingram M A and Pratt T G 2004 Broadband MIMO-OFDM wireless communications Proc. IEEE 92 271-94 [Google Scholar]
- Li Y G, Winters J H, and Sollenberger N R 2002 MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation IEEE Trans. Commun. 50 1471-77 [Google Scholar]
- Armstrong J 2009 OFDM for optical communications J. Lightw. Technol. 27 189-204 [CrossRef] [Google Scholar]
- Ma Y H, So P L and Gunawan E 2005 Performance analysis of OFDM systems for broadband power line communications under impulsive noise and multipath effects IEEE Trans. Power Del. 20 674-82 [CrossRef] [Google Scholar]
- Roy S and Li C 2002 A subspace blind channel estimation method for OFDM systems without cyclic prefix IEEE Trans. Wireless Commun. 1 572-79 [CrossRef] [Google Scholar]
- Li C and Roy S 2003 Subspace-based blind channel estimation for OFDM by exploiting virtual carriers IEEE Trans. Wireless Commun. 2 141-50 [Google Scholar]
- Towell G G and Shavlik J W 1994 Knowledge-based artificial neural networks. Artificial Intelligence 70 119–65 [CrossRef] [Google Scholar]
- Gershenfeld N A and Weigend A S 1993 The Future of Time Series Time Series Prediction: Forecating the Future and Understanding the Past (Boston: Addison-Weslay) pp 1-70 [Google Scholar]
- Sesbastian R 2016 An overview of gradient descent optimization algorithm [Google Scholar]
- Sutskever I, Martens J, Dahl G and Hilton G 2013 On the importance of initialization and momentum in deep learning 30th Int. Conf. on Mach. Learning, pp 1139-47, [Google Scholar]
- LeCun Y, Bottou L, Orr G B and Muller K R 1998 Efficient BackProp, Neural Networks: Tricks of the Trade Neural Networks: Tricks of the Trade (Berlin: Springer) pp 9-50 [CrossRef] [Google Scholar]
- Rumehart D E, Hinton G E and Williams R J 1986 Learning representations by backpropagating errors Nature 323 533-36 [Google Scholar]
- Darken C, Chang J and Moody J 1992 Learning rate schedules for faster stochastic gradient search Neural Networks for Signal Process. II IEEE Workshop 3-12 [Google Scholar]
- Robids H and Monro 1951 S A stochastic approximation method, Mathematical Statistics vol 42 pp 400-07 [Google Scholar]
- Kingma D and Ba J 2015 Adam: A method for stochastic optimization 3rd Int. Conf. for Learning Representations (San Diego) [Google Scholar]
- François C 2015 Keras GitHub repository https://gihub.com/fchollet/keras [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.