# Blind Channel Estimation Method Using CNN-Based Resource Grouping  
[PERSON]  
1 Department of ICT Convergence, Soonchunhaying University, Asan 31538, Republic of Korea; [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (D.J.) [PERSON]  
2 Department of Information and Communication Engineering, Soonchunhaying University, Asan 31538, Republic of Korea; [EMAIL_ADDRESS] (Y.K. ); [EMAIL_ADDRESS] (B.-G.K.)  
[PERSON]  
1 Department of ICT Convergence, Soonchunhaying University, Asan 31538, Republic of Korea; [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (D.J.) [PERSON]  
2 Department of Information and Communication Engineering, Soonchunhaying University, Asan 31538, Republic of Korea; [EMAIL_ADDRESS] (Y.K. ); [EMAIL_ADDRESS] (B.-G.K.)  
[PERSON]  
3 School of Electrical Engineering, Ko  
sliding bidirectional gated recurrent unit (SBGRU) estimator, which integrates the recurrent neural network (RNN) structure with the sliding window concept [8]. To further enhance channel estimation, a convolutional neural network (CNN)-based method was proposed to tackle performance degradation caused by spreading factor interference in long-range (LoRa) systems [9]. Moreover, for doubly selective channels in high-speed mobile scenarios, channel estimation was enhanced through the compression-and-reconstruction channel estimation network (CRCENet) [10]. Therefore, accurate channel estimation is crucial to ensuring network stability and improving the quality of received signals across various environments. For channel estimation, the demodulation reference signal (DM-RS), commonly referred to as a pilot signal, has been adopted as a standard technology for channel estimation. DM-RS is allocated to radio resources and transmitted along with uplink and downlink data, enabling it to effectively respond to real-time channel variations. However, due to its inherent characteristic of occupying radio resources, inefficient resource management of DM-RS can lead to resource wastage. Several studies have explored neural network-based approaches to optimize DM-RS resource management efficiently. Beyond optimizing existing DM-RS resources, channel estimation algorithms leveraging the most significant lag (MSL) have been proposed, along with semi-blind channel estimation methods that combine conventional DM-RS with detected symbols [11; 12]. Moreover, blind channel estimation methods utilizing the K-means clustering algorithm, a widely used unsupervised learning scheme, have also been introduced [13]. K-means clustering-based channel estimation enhances wireless resource efficiency by relying solely on received signals, eliminating the need for DM-RS consumption. This approach provides an advantage over semi-blind channel estimation methods that require a significant number of DM-RS resources to ensure reliable operation. Therefore, this paper aims to improve the K-means algorithm-based channel estimation method, which is one of the blind channel estimation schemes that do not utilize DM-RS. [PERSON] and [PERSON] [13] proposed a K-means-based blind channel estimation method that performs clustering on the received signals without transmitting DM-RS. In this study, the received signals are clustered using the K-means algorithm, and the centroids of the clusters are used to identify the original positions of quadrature phase shift keying (QPSK) symbols of the transmitted signal. By identifying the phases of QPSK symbols significantly altered due to multipath fading back to their original positions, this method achieves channel estimation without pilot signals, effectively canceling noise. However, this method has the limitation of determining the same channel for all received signals processed by the K-means clustering algorithm, making it ineffective in addressing channel variations at the subcarrier and orthogonal frequency division multiplexing (OFDM) symbol levels.