# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 1 Introduction
### Related Work and its Limitations  
From this perspective, various channel estimation techniques have been proposed to enhance system performance. They are typically divided into two categories, i.e., with and without changing the IEEE 802.11p standard. With respect to the schemes that change the standard structure [16; 17; 18; 19; 20; 21], because they can supplement the lack of pilots of IEEE 802.11p, outstanding performance can be obtained compared to approaches that do not modify IEEE 802.11p. However, these methods cause compatibility and interoperability problems with standard equipment.  
Because of the problems mentioned above, the majority of channel estimation schemes do not change the standard structure [22; 23; 24; 25; 26; 27; 28; 29; 30]. In [22], Wiener filter and decision-directed estimation are employed to minimize the mean squared error (MSE). This scheme requires the characteristics of a channel that are difficult to obtain in vehicular environments. In [23; 24], an adaptive reduced-rank estimation using subspace selection and an iterative estimation using generalized discrete prolate spheroidal sequences are proposed, respectively. However, both schemes employ the Wiener filter and iterative method for channel estimation, resulting in high computational complexity. In [25], a spectral temporal averaging (STA) method was presented to improve error performance. the STA scheme performs a moving average of the initial channel estimates in the time and frequency domains so as to mitigate noise effects and demapping errors. Therefore, the STA scheme performs well at low signal-to-noise ratio (SNR) regions with strong noise, but an error floor occurs in high-SNR regions. In [26; 27], constructed data pilots (CDPs) and time-domain reliable test frequency domain interpolation (TRFI) are, respectively, addressed using the correlation characteristics of channels between adjacent symbols. These schemes obtain excellent performance in high-SNR regions of the signal. However, in low-SNR regions, the correlation characteristics are very poor due to noise, thus degrading performance. In [28], a minimum MSE (MMSE) scheme based on a virtual pilot subcarrier was proposed. In contrast to conventional MMSE which requires prior knowledge of a channel, this scheme employs virtual pilot subcarriers to obtain a covariance matrix; thus, it is less complex. Although the MMSE scheme shows good performance in certain cases, it still performs worse than STA in low-SNR signal regions. To achieve outstanding performance at all SNR levels, two-channel estimation schemes were developed in [29]. First, a modified CDP (MCDP) was proposed that compensates for poor performance using a Markov process in the low-SNR region of the CDP. However, MCDP has a higher error rate than CDP in high-SNR regions; thus, SNR-assisted MCDP (SAMCDP), which selectively uses MCDP and CDP according to a predefined SNR threshold, has been proposed. Because the SNRthreshold may change depending on the channel environment, an adaptation method based on the SNR threshold cannot obtain high accuracy. In [30], channel estimation based on self-organization of frequencies was proposed to mitigate the time-varying channel effects. However, this method requires decoding and encoding processes that are equal to the number of symbols in a packet for obtaining the channel estimate, leading to high computational complexity.