Channel Estimation in Smart Grid Networks: A CNN and Diffusion Model Approach for mmWave Massive MIMO

With the rapid advancement of smart grid technology, the demand for high-speed wireless communication has become increasingly critical. In this context, millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems have emerged as a promising solution for enhancing network performance. However, the application of MIMO systems in smart grids poses significant challenges to existing business units. Thus, a method that integrates Convolutional Neural Networks (CNN) and diffusion models is proposed to effectively estimate channels in mmWave massive MIMO systems. In the method, the signal data are divided into high-frequency and low-frequency components to achieve the effect of processing the dominant informative parts and the dominant noise parts separately. After that, a rough estimate of the received signal is performed, and the data are then directed into the CNN model. The outcomes from the CNN serve as the basis for the low-frequency component. Subsequently, the signal data estimated by CNN are input into the diffusion model and its output is utilized to inform the high-frequency component. Experimental results demonstrate that the proposed method significantly reduces Mean Squared Error compared to existing techniques, confirming its superior performance and potential application in practical communication systems.