Paper Title
DEEP LEARNING-AIDED CHANNEL ESTIMATION FOR MMWAVE OFDM SYSTEMS IN MASSIVE MIMO NETWORKS

Abstract
Millimeter-waveorthogonal frequency division multiplexing (OFDM) is regarded as a key enabler for future wireless communication systems including massive multiple-input multiple-output (MIMO) networks. However, reliable channel estimation becomes a cumbersome task because of the rapidly changing propagation environment, non-ideal behavior of hardware components and antenna array size. In this paper, a deep learning-aided channel estimation framework which employs convolutional and recurrent neural networks (CNNs and RNNs) to learn the spatial and temporal characteristics of mmWave channels.The objective is to obtain an accurate and robust estimation in both low signal-to-noise ratio (SNR) and limited pilot overhead conditions for improved spectral efficiency of massive MIMO–OFDM systems.A hybrid neural network model, using convolutional layers for feature extraction and LSTM layers for sequential learning is proposed. The developed system is learnt on the simulated and measured mmWave data collected in different mobility patterns. The proposed methods are compared with standard least squares (LS), minimum mean square error (MMSE) and compressed sensing–based estimators.The experimental results show that the deep learning model yields a significant NMSE and BER reduction, about 25–35% over baseline models. It is demonstrated that the proposed framework exhibits robust generalization to unseen channel states and an immunity for pilot contamination.The developed model can be implemented at the base station in a low/medium computational complexity through GPU/FPGA accelerators, providing a real-time channel estimation in 5G and beyond 6G networks. It develops a cooperative frequency–spatial and temporal channel relationship modeling based dual-domain learning scheme. Incorporating real-world data set training and covering practical application, the introduced framework is a first step towards intelligent channel estimation for large-scale mmWave massive MIMO systems. Keywords - Deep Learning, Channel Estimation, mmWave OFDM, Massive MIMO ,6G Wireless Networks.