NDLI: Estimation of MIMO channels using complex time delay fully Recurrent Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Sarma, K.K. Mitra, A.
Copyright Year	2011
Description	Author affiliation: Deptt of Electronics and Communication Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, Assam, India (Sarma, K.K.; Mitra, A.)
Abstract	Estimation of Multi Input Multi Output (MIMO) channels can be performed by Artificial Neural Network (ANN)s such as Multi Layer Perceptron (MLP)s. However, the cost of training overload in case of time varying MIMO channels is the main bottleneck of such ANN architectures for which a viable alterative, namely, the Recursive Recurrent Network (RNN) is explored. Although for tightly coupled real and imaginary components of a transmitted signal RNN cannot provide a satisfactory solution, nevertheless, a split — complex activation RNN approach can be adopted to deal with such cases averaging the output obtained for a given time length. The results demonstrate better performance as well as computational simplicity compared to MLP architectures with temporal characteristics.
Starting Page	1
Ending Page	5
File Size	165500
Page Count	5
File Format	PDF
ISBN	9781424495788
e-ISBN	9781424495818
DOI	10.1109/NCETACS.2011.5751375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Recurrent neural networks OFDM Neurons Channel estimation Artificial neural networks MIMO
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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