# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 5 Simulation Results  
In this section, we present a performance analysis of our proposed scheme. By using the IEEE 802.11p link level simulator, BER and PER simulations have been performed on LS, STA, TRFI, MMSE, SAMCDP, and two versions of the proposed scheme, i.e., switching between STA and MMSE or between STA and TRFI. The IEEE 802.11p link-level simulator was designed to comply with the transmitters and receivers in OFDM systems of IEEE 802.11p (as shown in Figure 1) by using MATLAB. The simulations are performed by following the system parameters in Table 1 and are conducted on V2V and V2I channel models in Table 2. The modulation schemes employ QPSK (Figures 6 and 7) and 16 QAM (Figures 8 and 9) with a code rate of \(1/2\) and the packet is composed of 50 OFDM symbols. The performance of STA depends on the moving average parameters \(\alpha\) and \(\beta\), and that of MMSE depends on the number of virtual pilots. Therefore, to obtain the maximum performance in the STA and MMSE, \(\alpha\) and \(\beta\) are set to 2 and 48 virtual pilots are used, respectively. Figure 5: The last step of the proposed scheme. Figure 6: Performance analysis of STA, TRFI, MMSE, SAMCDP, and the proposed scheme (V2V Expressway Oncoming, QPSK 1/2, 50 symbols). Figure 7: Performance analysis of STA, TRFI, MMSE, SAMMCDP, and the proposed scheme (V2I Urban Canyon, QPSK 1/2, 50 symbols). Figure 8: Performance analysis of STA, TRFI, MMSE, SAMMCDP, and the proposed scheme (V2V Expressway Oncoming, 16 QAM 1/2, 50 symbols). Figure 9: Performance analysis of STA, TRFI, MMSE, SAMMCDP, and the proposed scheme (V2I Urban Canyon, 16 QAM 1/2, 50 symbols). Figures 6 and 8 present the BER and PER for STA, TRFI, MMSE, SAMCDP, and the proposed scheme in the V2V channel environment, whereas Figures 7 and 9 show the BER and PER of these methods in the V2I channel environment. The left side represents the BER and the right side shows the PER. The results for the V2V channel model (Figures 6 and 8) reveal that the STA performs well in low-SNR regions, but has an error floor in high-SNR regions. In contrast to STA, MMSE and TRFI performs better only in high-SNR regions. In addition, SAMCDP has lower performance than the channel estimation schemes which show good performance in the high- or low-SNR regions. The proposed scheme provides the performance of STA in low-SNR signal regions and the performance of the corresponding scheme in high-SNR signal regions. Our scheme compared to MMSE, TRFI, and SAMCDP can achieve a performance advantage of about 3-6 dB at a BER for QPSK of \(7\times 10^{-2}\) and provide a gain about 2-5 dB at a PER for that of \(5\times 10^{-1}\). When STA and MMSE are used for switching, the proposed scheme over TRFI and SAMCDP offers the gain of about 3 dB at a BER of \(7\times 10^{-3}\) and provide the advantage about 7 dB at a PER of \(8\times 10^{-2}\). In particular, we can see that the error flow of STA, which occurred in high-SNR regions, has been solved in the proposed scheme. In both BER and PER for 16 QAM, as with QPSK, the proposed scheme provides the performance of two different schemes depending on the SNRs, and show the performance improvement compared to the other schemes. These results show that in both QPSK and 16 QAM, this scheme provides the performance gain over the entire SNR range, as well as the ability to use various channel estimation schemes through proper adaptation algorithm. For the V2I channel model, Figures 7 and 9 show that the performance of the channel estimation scheme behaves similarly to the performance in the V2V channel model. The STA shows good performance in low-SNR regions, whereas TRFI and MMSE perform very well in the high-SNR regions. The SAMCDP performs worse than STA in the low-SNR regions and worse than TRFI and MMSE in the high-SNR regions. The proposed scheme has the performance of the STA in the low-SNR regions and the performance of the TRFI or MMSE scheme in high-SNR regions.