# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method  
### Resource Grouping Pattern  
The channel estimation based on the K-means algorithm identifies the centroids of each cluster, effectively counteracting signal distortion. However, this channel estimation method must adequately account for significant channel variability along the time and frequency domains to perform clustering effectively. For instance, if the channel varies rapidly in the time domain due to high Doppler effects, the groups must be densely configured along the time domain. Similarly, if the channel exhibits rapid variation in the frequency domain due to significant delay spread, the groups must be densely configured with single or multiple subcarriers along the frequency domain. Such clustering-based K-means algorithm channel estimation cannot be optimally performed for all UEs. In this paper, to address this limitation, we propose a method that generates resource group patterns within the resource grid for received signals to enable the K-means-based blind channel estimation to operate effectively by identifying the characteristics of the dynamically changing channel. The resource group pattern consists of \(t\) consecutive OFDM symbols in the time domain and \(f\) consecutive subcarriers in the frequency domain. The number of consecutive OFDM symbols, \(t\), can range from {1, 2,..., 14}. If \(t\) is not a divisor of 14, which is the total number of OFDM symbols in a single slot, the remaining OFDM symbols that do not satisfy this condition are grouped into the maximum possible number of consecutive OFDM symbols. In this paper, such groups are defined as remainder groups. Additionally, the number of consecutive subcarriers, \(f\), can take any value among the divisors of 120 within 10 RBs. The divisors of 120, \(D(120)\) are expressed in Equation (6) and are listed as {1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 24, 30, 40, 60, 120}, for a total of 16 values:  
\[D(120)=\Big{\{}\ 2^{a}.3^{b}\cdot 5^{c}\ \Big{|}0\leq a\leq 3,\ 0\leq b\leq 1,\ 0\leq c \leq 1\Big{\}}. \tag{6}\]  
In other words, the resource grouping patterns for the received signal can consider 14 cases in the time domain and 16 cases in the frequency domain, resulting in a total of 224 possible patterns. For instance, in a pattern where \(t=7\) and \(f=60\) within 1 slot in the time domain and 10 RBs in the frequency domain, one resource group consists of 7 consecutive OFDM symbols in the time domain and 60 consecutive subcarriers in the frequency domain. As shown in Figure 2, the clustering pattern consists of a total of four groups, and K-means clustering-based channel estimation is performed for each group within the pattern. In Figure 2, within the resource grid corresponding to one slot in the time domain and 10 RB in the frequency domain, each group is distinguished by different colors, and K-means clustering-based channel estimation is executed for each group. Channel estimation is performed for a single resource group, as shown by the red dashed lines. The estimated channel values are represented as \(\tilde{h}_{1},\ \tilde{h}_{2},\ \tilde{h}_{3},\ \tilde{h}_{4}\), and each resource group is estimated to have the same channel. The generated resource groups must identify QPSK symbols through K-means-based channel estimation, requiring at least four REs within each group. In this paper, this requirement is defined as the minimum RE count condition. Patterns that do not satisfy this condition are excluded. For example, in the pattern where \(t=2\) and \(f=1\), a single group consists of two consecutive OFDM symbols in the time domain and one subcarrier in the frequency domain, resulting in a total of 840 groups. Since each group in this pattern contains only two REs, channel estimation using the K-means algorithm cannot be performed. Therefore, this pattern is excluded for failing to meet the minimum RE count condition.