# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 1 Introduction
Reference [18] proposed a novel SNR estimation algorithm for OFDM system in the doubly selective channels by inserting special preambles. The noise variance was estimated by one preamble symbol in time domain. A modified ML method was proposed in Reference [19], which uses the redundant information of the cyclic prefix to work directly in the time domain and can be applied to any unary or non-unitary constellation. Reference [20] proposed a NDA estimation method for estimating primary signal transmission parameters in OFDM overlay system, using only the available fast Fourier transform (FFT) component of the secondary user. The method did not need any prior knowledge of the primary signal or the environmental noise. In Reference [21], the joint ML estimation of SNR was established by using the representation structure of the training preamble consisting of multiple identical parts. A new expression of Cramer-Rao bound for the joint estimation of all unknown parameters was given to evaluate the final accuracy of ML method. In Reference [22], the SNR estimation utilized two identical semi-characteristics of time synchronization preambles in OFDM system and depended on their autocorrelation. The existing estimator was compared with the normalized mean square error (NMSE) in single-input-single-output (SISO) SNR estimation method. Heretofore, lots of SNR estimation methods have been put forward but most of them still have not considered the effect of frequency offset. In Reference [23], the influence of frequency offset on SNR was analyzed. Under the premise of prior information, the SNR was estimated when the signal has frequency offset. However, it had high computation complexity. Therefore, we study the SNR estimation in UAV OFDM system with the frequency offset. In this paper a novel low-complexity NDA SNR estimation method is proposed for UAV OFDM system, consisting of three main components, an absolute value series, the maximum and minimum platform in the characteristic curve of the output series and the root mean squares. The main innovative contributions of the proposed method are summarized as follows--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 is effective and feasible conditioned on the poor synchronization precision (containing residual frequency offset) with lower computation complexity and better performance compared with other methods. The rest of this paper is organized as follows. The signals model of UAV OFDM system is presented in Section 2. The proposed NDA SNR estimation method is introduced in Section 3. Section 4 shows the numeric simulation and discussion to verify the estimation performance. Finally, in Section 5, we conclude the main work of this paper.