Reducing normalized mean square error during channel estimation using minimum pilot symbols in massive MIMO network

Deep Learning technology has emerged as an exciting innovation in the field of applications in wireless communications systems. In this proposed work, fully connected neural network based on deep learning (DL-FCNN) as well as convolutional neural network (CNN) models were employed to determine the channel state information by estimating channel coefficients using a minimal number of pilot symbols. The findings consider the length of the pilot symbol and the cross-cell interference as the main aspects in the estimation of channel coefficients. Results demonstrate that the proposed deep learning method outperforms traditional approaches such as least square (LS) and minimal mean square estimation (MMSE) techniques for measuring the normalized mean square errors (NMSE), which is thought to be a loss-function or channel estimation error. Additionally, the deep learning (DL) method provides better results in the presence of pilot contamination in multi-cell cellular networks and at various levels of cross-cell interference. In conclusion, deep learning models thereof can efficiently be utilized for channel estimation operations as well as physical layer signal processing applications in Massive MIMO Networks.