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Open AccessArticle

Non Data-Aided SNR Estimation for UAV OFDM Systems

1
School of Electronic Engineering, Xi’an Aeronautical University, Xi’an 710077, China
2
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
3
Department of Electrical and Computer Engineering, [email protected] Tech, Arlington, VA 24061, USA
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(1), 22; https://doi.org/10.3390/a13010022
Received: 11 December 2019 / Revised: 3 January 2020 / Accepted: 7 January 2020 / Published: 10 January 2020
Signal-to-noise ratio (SNR) estimation is essential in the unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) system for getting accurate channel estimation. In this paper, we propose a novel non-data-aided (NDA) SNR estimation method for UAV OFDM system to overcome the carrier interference caused by the frequency offset. First, an absolute value series is achieved which is based on the sampled received sequence, where each sampling point is validated by the data length apart. Second, by dividing absolute value series into the different series according to the total length of symbol, we obtain an output series by stacking each part. Third, the root mean squares of noise power and total power are estimated by utilizing the maximum and minimum platform in the characteristic curve of the output series after the wavelet denoising. Simulation results show that the proposed method performs better than other methods, especially in the low synchronization precision, and it has low computation complexity.
Keywords: unmanned aerial vehicle; orthogonal frequency division multiplexing; signal-to-noise ratio; parameter estimation; non data-aided unmanned aerial vehicle; orthogonal frequency division multiplexing; signal-to-noise ratio; parameter estimation; non data-aided
MDPI and ACS Style

Li, J.; Liu, M.; Tang, N.; Shang, B. Non Data-Aided SNR Estimation for UAV OFDM Systems. Algorithms 2020, 13, 22.

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