# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 1 Introduction  
Unmanned aerial vehicle (UAV), as a groundbreaking technology, has been widely used in many applications such as delivery, surveillance, video streaming and so on [1]. In UAV communication systems, high-speed data rate is required based on many actual scenarios. For example, considering that UAV collects high-resolution images to describe the geometry of objects, we are expected to establish reliable data transmission links and provide enough bandwidth to ensure stable and accurate image transmissions. Orthogonal frequency division multiplexing (OFDM), as a high-speed data transmission method, splits the data stream across several separate narrowband channels at different frequencies to reduce interference and crosstalk [2, 3]. Leveraging OFDM into UAV communication requires the knowledge of exact UAV communication channels which have the following characteristics. First, since a UAV is on a certain flight height and the ground station uses directional control antennas, there are a strong direct signal component and a Gaussian white noise [4]. Second, as UAV usually has a flight speed, the Doppler effect is generated accordingly [5]. Third, there are multipath components caused by different propagation paths and the frequency selective fading in such air-to-ground channels [6]. According to these characteristics, the amplitude and the phase of each subcarrier will change more randomly with carrier frequency offset, timing offset and frequency selective fading, which leads to the fading in both time and frequency domains and the inter-symbol interference (ISI). Therefore, how to accurately detect the changed factors and demodulate the original signals at ground receiver under such UAV OFDM system is of great significance in UAV communication systems [7; 8; 9]. To ensure a desired communication performance, signal-to-noise ratio (SNR) needs to be well estimated, since accurately estimating SNR value guarantees the adaptive adjusted communication rate, modulation and coding schemes [10; 11]. In addition, the forward error correction coding and decoding code rate also requires real-time SNR estimation value. Affected by the fluctuated amplitude and phase of each subcarrier in UAV OFDM systems, SNR can be time-varying and thus is difficult to be estimated. There are two SNR estimation methods for OFDM system, that is, data-assisted (DA) estimation and non-data-assisted (NDA) estimation. Specifically, DA estimation method utilizes transmitted pilot symbols to obtain estimated value. However, the redundant pilot symbols decrease system throughput. NDA estimation method allows an in-service and inter-systems SNR estimation without the prior transmitted signals [12]. In existing works, some NDA SNR estimation methods have been proposed. References [2; 12; 13] estimated the SNR by utilizing the characteristic of OFDM symbols. However, high complexity of multi-path channel order estimation algorithm leads to high computation complexity and low estimation accuracy. Reference [14] proposed an algorithm for signal reconstruction by utilizing the auto-correlation functions of the received signal and the reconstructing signal. However, it had high computation complexity and was hard to achieve. In contradiction to the traditional maximum likelihood (ML) SNR estimation, the problem of finding the roots of a polynomial of third degree was avoided by a convenient approximation proposed in Reference [15]. Also, in Reference [16], a method for estimating the order and SNR of NDA channel based on time-varying autocorrelation function was designed for OFDM systems. A new closed-form expression of the time-varying autocorrelation function (TVAF) for the received signals was obtained. The proposed estimation method achieved significant performance gains in low SNR region while maintaining satisfactory performance gain in high SNR region. Reference [17] showed the feasibility of utilizing zero subcarrier to measure noise power and verified its performance through simulations.