# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method
### Data Set Generation
Figure 3 shows the distribution of the optimal number of OFDM symbols as \(t\), which is the parameter determining the pattern in the time domain under different velocity conditions. The x-axis in each graph, labeled \"Number of OFDM symbols per group\", represents the optimal \(t\). In Figure 3, as the velocity of the UE increases, channel state variations in the time domain become more frequent and rapid, resulting in smaller values of \(t\) appearing more frequently at higher velocities. This indicates that as velocity increases, the number of OFDM symbols per group decreases, necessitating channel estimation at smaller group sizes. Consequently, for relatively low velocities, such as 30 km/h, patterns with larger values of \(t\) per group are determined to be optimal. In contrast, at higher velocities, such as 150 km/h, smaller group sizes are more suitable, leading to patterns with fewer OFDM symbols per group being identified as optimal. Figure 4 illustrates the distribution of the optimal number of subcarriers, \(f\), which is the parameter that determines the resource grouping pattern in the frequency domain under different delay spread conditions. This is represented on the \(x\)-axis of each graph. Figure 4: Frequency domain pattern distribution according to delay spread conditions. Figure 5: Number of data distribution according to SNR (\(-6,-4,-2,0\) dB). In Figure 4, as the delay spread increases, channel variations in the frequency domain occur more rapidly and irregularly, requiring smaller values of \(f\) per group for channel estimation in the frequency domain. For a low delay spread, such as 50 ns, forming groups is frequently determined as optimal patterns. In contrast, for a high delay spread, such as 300 ns, the group size becomes smaller to respond more finely to channel variations, and smaller values of \(f\) are more widely distributed. This indicates that creating groups with smaller units is more effective under conditions of high delay spread, emphasizing the necessity of finer resource grouping in the frequency domain as the delay spread increases. Figures 3 and 4 demonstrate that resource grouping into smaller units is effective under relatively high levels of velocity and delay spread. However, a substantial proportion of cases were identified where larger units of \(t\) and \(f\) were optimal, which shows that the \(f\) with a value of 120 was the most frequently identified optimal value. Because the channel environments considered in this study include both low and high SNR levels, channel estimation based on the K-means algorithm requires an appropriate amount of data to achieve sufficient channel estimation performance based on the SNR level. This requirement increases as the SNR decreases, requiring more data. To meet this requirement, \(f\), which offers more data points, is more effective than \(t\) in terms of data quantity. Consequently, when performing K-means-based channel estimation under high SNR conditions, smaller units of \(t\) and \(f\) are determined while satisfying the relatively small data requirements. The optimal data requirements for channel estimation under varying SNR levels are shown in Figures 5 and 6. Figure 5 illustrates the optimal number of data counts per group at SNR levels of \(-6\), \(-4\), \(-2\), and 0 dB, while Figure 6 shows the corresponding values at SNR levels of 2, 4, 6, 8, and 10 dB. In low-SNR conditions, relatively large data quantities, such as 960, 1080, 1200, and 1680, were detected to ensure channel estimation performance. In contrast, under high-SNR conditions, smaller data quantities were more  
Figure 6: Number of data distribution according to SNR (2, 4, 6, 8, 10 dB). broadly distributed. As shown in Figures 3-6, the analysis of optimal patterns demonstrates that appropriate resource grouping in the time and frequency domains is essential for K-means-based channel estimation methods, considering channel factors such as UE velocity, delay spread, and SNR.