Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

Data Fitting-based SNR estimation algorithm for the Adaptive Transmission

Authors
Zezhou Sun, Chenghua Wang, Xiaofei Zhang
Corresponding Author
Zezhou Sun
Available Online January 2017.
DOI
10.2991/icmmita-16.2016.209How to use a DOI?
Keywords
SNR estimation; adaptive transmission; parameter estimation; data fitting.
Abstract

Adaptive transmission system selects the suitable transmission rate on the basis of the current channel state. In this paper, we exploit the signal to noise ration (SNR) as a benchmark of standard to measure the channel state. We employ data fitting (DF) to estimate SNR, which is a key technology of the adaptive transmission, and we change the transmission rate according to the estimation of SNR. The proposed algorithm can obtain a low computational complexity and can have performance improvement of SNR estimation. The superiority of the proposed algorithm is revealed by simulations.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
Series
Advances in Computer Science Research
Publication Date
January 2017
ISBN
978-94-6252-285-5
ISSN
2352-538X
DOI
10.2991/icmmita-16.2016.209How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zezhou Sun
AU  - Chenghua Wang
AU  - Xiaofei Zhang
PY  - 2017/01
DA  - 2017/01
TI  - Data Fitting-based SNR estimation algorithm for the Adaptive Transmission
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.209
DO  - 10.2991/icmmita-16.2016.209
ID  - Sun2017/01
ER  -