# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 4 Simulation Results
Figure 8 illustrates the MSE performance of three patterns according to SNR. The left graph represents the case with a delay spread of 50 ns, while the right graph corresponds to 300 ns. The bold dashed lines represent the optimal patterns obtained from the exhaustive search, as shown in Figure 3 through Figure 6. The solid lines represent the Proposed Pattern obtained using the two-step CNN training introduced in this paper. The thin dashed line  
Figure 8: MSE performance for each pattern with delay spared according to SNR: (**a**) 50 ns delay spread, (**b**) 300 ns delay spread. Figure 9: MSE performance for each pattern with velocity according to SNR: (**a**) 30 km/h velocity, (**b**) 150 km/h velocity. representing the Conventional Pattern indicates the channel estimation performance from the most recent study on K-means algorithm-based channel estimation, presented in [13]. In the Conventional Pattern, the K-means algorithm is applied to resources corresponding to one slot in the time domain land ten RB in the frequency domain, demonstrating superior performance compared to the traditional least-squares (LS) channel estimation method. Since the method proposed in this paper is designed to address the limitations of [13], its performance is analyzed through a comparison with the Conventional Pattern. The red, green, and blue curves indicate velocities of 30 km/h, 90 km/h, and 150 km/h, respectively. The optimal pattern exhibited the best performance across all SNR ranges, followed by the proposed pattern and the conventional pattern. The proposed pattern showed slightly inferior performance compared to the optimal pattern, with the performance gap widening in the SNR range of \(-2\) dB to \(2\) dB. However, beyond \(2\) dB, the gap gradually decreased, and the proposed pattern achieved performance close to the optimal pattern. In contrast, the conventional pattern exhibited the worst performance across all SNR ranges and tended to converge to a fixed MSE threshold beyond \(0\) dB. This indicates that the conventional pattern determines the same channel for all received signals processed through clustering, failing to effectively reflect the complexity of the channel environment, which limits performance improvement even in high-SNR conditions. For a delay spread of \(50\) ns, all patterns demonstrated relatively good MSE performance, and the proposed pattern consistently maintained lower MSE compared to the conventional pattern across all SNR ranges. On the other hand, when the delay spread increased to \(300\) ns, channel variations became more irregular, causing performance degradation for all patterns. Nonetheless, the proposed pattern continued to outperform the conventional pattern, demonstrating its reliability. In the velocity analysis, at \(30\) km/h, the performance gap between the proposed pattern and the optimal pattern was minimal, while the proposed pattern outperformed the conventional pattern by approximately \(3\) dB. At higher velocities, such as \(150\) km/h, the performance of the proposed pattern was slightly degraded as a result of increased channel variability but still maintained significantly lower MSE compared to the conventional pattern, demonstrating high reliability even in high-mobility scenarios. Figure 9 illustrates the MSE performance of three patterns according to SNR. The left graph represents the case where the UE velocity is \(30\) km/h, and the right graph corresponds to \(150\) km/h. The red, green, and blue curves represent the delay spread conditions of \(100\) ns, \(200\) ns, and \(300\) ns, respectively. The optimal pattern demonstrated the best performance across all SNR ranges under all conditions, while the proposed pattern showed slightly lower performance but maintained a similar trend. In the low SNR range from \(-6\) dB to \(-2\) dB, the proposed pattern exhibited performance close to the optimal pattern. However, as the SNR increased from \(-2\) dB to \(4\) dB, the performance gap became more pronounced.