MATEC Web Conf.
Volume 210, 201822nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
|Number of page(s)||6|
|Published online||05 October 2018|
Data Recovery through Modulation Identification in Dense Wireless Networks
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Department of Electronics and Communication Engineering, Gauhati University
4 Technical University of Sofia, Sofia, Kliment Ohridski 8, Bulgaria firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com
With rise in device complexity and transmission rates, reliability in data recovery has become another critical issue requiring costly and computationally demanding mechanism. The popularity of artificial intelligence (AI) and its ubiquitousness have established the usefulness of design of data recovery schemes where device level complexity is less. Lower device complexity is being ensured by the use of AI driven data recovery. In this work, we focus on the design of such a mechanism where traditional process are replaced by a neuro-computing structure. The advantage is lower levels of device complexity but incorporation of a training latency. Experimental results have established the reliability of the proposed system.
Key words: Modulation Recognition / Artificial Neural Networks / Multi Layer Perceptron
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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