# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 6 Simulation Results and Discussion
Assuming a packet consists of 50 symbols, Figure 7 shows the duration of two packets. In Figure 3, the mean correlation coefficients of \(H_{CSI}(m,k)\) and \(H_{TT}(m,k)\) intersect at 24 dB; thus, the selective use of TRFI and DD-TT occurs most frequently at around 24 dB. As shown in Figure 7, the correlation coefficients of DD-TT in the red boxes are consistently higher than that of CSI, and the blue box is the reverse. Therefore, one channel estimation scheme is chosen from the beginning to the end of the box, and in this interval, the results are the same whether the SLS scheme is performed once or on a symbol-by-symbol basis. Based on this analysis, simulations are performed by changing the selection period of the SLS scheme within the packet. The selection period is defined as \(\eta\times T_{OFDM}\), where \(\eta\triangleq\{1,5,15,25,50\}\). As shown in Figure 6, in both the V2V and V2I channel environments, SLS with \(\eta=1\) (symbol-level selection) provides the best performance, and the performance decreases with increasing \(\eta\). When \(\eta=5\) and \(\eta=15\), SLS shows a performance similar to that of symbol-level selection. Interestingly, SLS with \(\eta=25\) and \(\eta=50\) (packet-level selection) provides a higher performance than the PLS scheme. Even with the same selection period with PLS, the SLS scheme offers better performance than the PLS scheme. This means that the criterion for selecting a better technique of SLS is superior to that of PLS. Consequently, as \(\eta\) grows, the complexity of the SLS scheme is reduced, and the performance decreases. However, by choosing the appropriate \(\eta\) depending on the channel environment, the SLS scheme can reduce the complexity while minimizing the performance degradation compared with symbol-level selection. Finally, link budget analysis is performed to confirm the effective communication range of the proposed SLS scheme in a practical vehicular network. By the simulation results of Figures 4 and 5, the SNR region where the selective use of DD-TT and TRFI occurs frequently in the proposed scheme is 18-45 dB in the V2V channel and 24-45 dB in the V2I channel. Therefore, the most performance gain occurs in this SNR region, and the link budget analysis is performed for this portion. The link budget expression for SNR is as follows:  
\[SNR=P_{r}(d)-N, \tag{16}\]  
where \(P_{r}(d)\) represents the received power according to distance \(d\), including transmission power, antenna gain, and pass loss. \(N\) is the thermal noise including the noise figure. The inverse calculation of Equation (16) yields an approximate communication range. We assume the transmit power, antenna gain, and the noise figure are set to 23 dBm, 3 dB, and 9 dB, respectively, and the WINNER II path-loss model [31] is applied to consider the practical network situation. Applying the set parameters to Equation (16) to obtain the pass loss value and using this value to obtain the distance from the WINNER II pass-loss model, the effective communication range of the V2V channel is 35-135 m and the V2I channel is 45-135 m. Therefore, the proposed SLS scheme within this range (35-135 m) can get the highest performance gain, and better performance can be obtained by using the SLS scheme than by using the existing channel estimation method alone. Figure 6: PER performance analysis according to the selection period of the SLS scheme (V2V/V2I channel, QPSK 1/2, 50 symbols). (**a**) V2V channel environment; (**b**) V2I channel environment. There are three discussions of the simulation results. The first discussion is related to the performance of the proposed SLS scheme. The proposed SLS scheme in Figures 4 and 5 provide superior performance over the entire SNR region compared with STA, TRFI, DD-TT, and PLS on both V2V and V2I channels in terms of BER and PER.