# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method
### Data Set Generation  
This paper evaluates channel estimation performance by supporting optimal resource groups for all UEs. To this end, among all configured resource grouping patterns, the pattern that achieves the highest channel estimation performance in arbitrary channel environments is derived based on Equation (5) and utilized as the correct label for the CNN. The channel environment considers factors such as the UE's velocity and delay spread. For instance, when the UE's velocity is 30 km/h, the channel environment is relatively stable, making a pattern composed of resource groups with many OFDM symbols effective. Conversely, when the velocity is 150 km/h, a pattern composed of resource groups with a smaller number of OFDM symbols will be more effective. The delay spread is considered from 50 ns to 300 ns in increments of 50 ns, and velocity is considered from 30 km/h to 150 km/h in increments of 30 km/h, resulting in 30 channel conditions. The SNR is assumed to be perfectly calculated. The parameters used for dataset generation, including these, are summarized in Table 1[16]. The K-means algorithm is employed for channel estimation without relying on pilot signals. In this study, an exhaustive search is performed to identify the optimal pattern among the 212 proposed resource grouping patterns for each of the 30 considered channel conditions using the K-means algorithm. The MSE for each pattern is computed using Equation (5), and the pattern with the best performance is selected as the optimal pattern for the corresponding channel condition. The identified optimal patterns are used to generate correct labels for each step of the two-step training process, which is detailed in Section 3.3. Subsequently, a CNN is employed to effectively group the data according to various channel conditions, including delay spread, velocity, and SNR. The CNN is trained to learn the optimal grouping method for each channel condition and outputs the corresponding grouping pattern for each \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Subcarrier spacing & 15 [kHz] \\ \hline Number of RBs & 10 \\ \hline Number of OFDM symbols & 14 \\ \hline FFT size & 2048 \\ \hline Sample rate & 61,440,000 [Hz] \\ \hline Carrier frequency & 4 [GHz] \\ \hline Channel model & tapped-delay line-D \\ \hline Velocity & 30:30:150 [km/h] \\ \hline Delay spread & 50:50:300 [ns] \\ \hline SNR & \(-\)6:2:10 [dB] \\ \hline Noise & AWGN \\ \hline \(N\) & 100,000 \\ \hline Iteration & 100,000 \\ \hline Batch size & 256 \\ \hline Epoch & 20 \\ \hline Learning rate & 0.002 \\ \hline Dropout period & 9 \\ \hline Dropout factor & 0.1 \\ \hline Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. received signal, thereby enabling more accurate channel estimation. K-means algorithm-based channel estimation requires a sufficient number of data samples to achieve accurate channel estimation, and the minimum number of required data samples for channel estimation may vary with the SNR level. When performing K-means algorithm-based channel estimation under low SNR conditions, a relatively larger number of resource grouping patterns from the 212 proposed in this study have proven to be effective. In contrast, at higher SNR levels, patterns with a relatively smaller number of resources are utilized for channel estimation. The proposed 212 patterns can be effective depending not only on the SNR but also on the delay spread, which affects channel variations in the frequency domain, and the velocity, which impacts the time domain. Therefore, in this study, the optimal pattern obtained through an exhaustive search of the proposed 212 patterns is analyzed to examine the influence of delay spread, velocity, and SNR on channel estimation performance, as illustrated in Figures 3-6. The 212 patterns proposed in this paper are grouped by each channel parameter and presented in Figures 3-6 to illustrate the distribution of the optimal patterns.