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

Signal-noise Ratio Recognition Algorithm Based on Singular Value Decomposition

Authors
Yi Qiao, Qian Cui, Wei Zhang, Yan Liu
Corresponding Author
Yi Qiao
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.218How to use a DOI?
Keywords
wireless communication, signal-noise ratio estimation, autocorrelation matrix, singular value decomposition.
Abstract
Signal-noise ratio is an important parameter in modern wireless communication systems and an important indicator to reflect signal quality. An accuracy signal-noise ratio can provide signal quality information required for power control and channel decoder, etc. This paper proposes a blind signal-noise ratio estimation algorithm, which makes use of singular value decomposition of signal autocorrelation matrix to estimate the signal-noise ratio. The algorithm does not require prior information related to the signal and has no special equipments on signal sampling rate and signal type. It is relatively easier, with a certain acceptable complexity to some estimated accuracy extent.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Machinery, Materials and Computing Technology
Part of series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.218How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yi Qiao
AU  - Qian Cui
AU  - Wei Zhang
AU  - Yan Liu
PY  - 2016/03
DA  - 2016/03
TI  - Signal-noise Ratio Recognition Algorithm Based on Singular Value Decomposition
BT  - 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1102
EP  - 1106
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.218
DO  - https://doi.org/10.2991/icmmct-16.2016.218
ID  - Qiao2016/03
ER  -