# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 4 Simulation Results  
This section provides a detailed analysis of the accuracy of the proposed two-step learning process and the CNN-based channel estimation performance. The \(N\) data samples used in the two-step training process were divided into training, validation, and test  
Figure 7: Proposed CNN structure. datasets in proportions of 0.8, 0.1, and 0.1, respectively. The training results are presented in Table 2. The horizontal axis represents the SNR, and the vertical axis corresponds to the learning accuracy at each step. The accuracy of the first step of learning shows 100% at an SNR of \(-6\) dB, as the optimal pattern consistently selects \(f=120\). However, as the SNR increases from \(-4\) dB to 2 dB, adjacent \(f\) values, such as \(f=12\), 15 or \(f=24\), 30 exhibits similar channel estimation performance, resulting in approximately 50% accuracy. When the SNR exceeds 4 dB, the CNN begins to effectively reflect channel states influenced by delay spread and velocity, achieving over 70% accuracy. The accuracy of the two-step learning represents the classification performance of the optimal \(t\) for each \(f\) selected at the corresponding SNR. At \(-6\) dB, due to the severely poor channel conditions, as shown in Figures 8 and 9, channel estimation performance in this low SNR range is significantly degraded. Consequently, the classification accuracy of the CNN was also found to be considerably low. At \(-4\) dB, the first-stage learning identified \(f=60\) and \(f=120\) as optimal. However, due to the same reasons observed at \(-6\) dB, the two-step learning recorded a low accuracy of 36.3%. Similarly, at \(-2\) dB, the accuracy remained low at 43.1%. From 0 dB onward, as the SNR increased, the accuracy of the two-step learning progressively improved. The total accuracy also increased, reaching 82.5% at 10 dB. This improvement indicates that the CNN was able to more effectively learn the optimal \(t\) as the stability of the channel conditions improved at higher SNR. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{**Learn Method**} & \multicolumn{1}{c|}{**SNR [dB]**} & \multicolumn{1}{c|}{\(-6\)} & \multicolumn{1}{c|}{\(-4\)} & \multicolumn{1}{c|}{\(-2\)} & \multicolumn{1}{c|}{\(0\)} & \multicolumn{1}{c|}{\(2\)} & \multicolumn{1}{c|}{\(4\)} & \multicolumn{1}{c|}{\(6\)} & \multicolumn{1}{c|}{\(8\)} & \multicolumn{1}{c|}{\(10\)} \\ \hline \([\)step 1\(]\) & Accuracy & \multirow{2}{*}{100} & \multirow{2}{*}{55.9} & \multirow{2}{*}{51.7} & \multirow{2}{*}{57.1} & \multirow{2}{*}{55.02} & \multirow{2}{*}{72.0} & \multirow{2}{*}{70.6} & \multirow{2}{*}{78.2} & \multirow{2}{*}{87.3} \\ to the size of \(f\) & & & & & & & & & & \\ \hline \multirow{10}{*}{ \begin{tabular}{c} \(f\) \\ \end{tabular}  } & \multirow{2}{*}{120} & \multirow{2}{*}{60} & \multirow{2}{*}{40} & \multirow{2}{*}{24} & \multirow{2}{*}{20} & \multirow{2}{*}{24} & \multirow{2}{*}{15} & \multirow{2}{*}{12} & \multirow{2}{*}{12} \\ \cline{1-1} \cline{6-11} \cline{6-11} \cline{6-11} \cline{6  
The proposed two-step learning method demonstrates a complementary structure. The first step of the learning accurately identifies the optimal number of subcarriers per group \(f\), providing a solid foundation for the two-step learning to fine-tune \(t\). This allows the CNN to adapt effectively to dynamic channel conditions. However, at low SNR values such as \(-6\) dB, the significant degradation of channel conditions leads to imbalanced data distribution and excessive noise, which limit the accuracy of the CNN. These limitations highlight the potential for improvement through noise-resilient learning methods or data augmentation strategies. In conclusion, the proposed two-step learning process adapts effectively to dynamic channel environments, maintaining consistently high accuracy as the SNR increases. Future research could focus on evaluating CNN performance under a wider range of channel conditions or incorporating reinforcement learning methods to enable real-time adaptation and further improve performance.