Deep Learning-Based Channel Estimation for Massive MIMO Systems
Keywords:
Massive MIMO, Channel Estimation, Deep Learning, Neural Networks, Deep Neural Networks (DNN), Spectral Efficiency, Wireless Communication, Least-Squares (LS), Minimum Mean Square Error (MMSE), Computational Complexity, Estimation Accuracy, High-Dimensional MIMO Systems, Signal Processing, Robustness to Noise, 5G Networks, Channel State Information (CSI), Machine Learning in Wireless Networks.Abstract
MIMO technology has proved important for widening the capacity of wireless systems and making sure communication is reliable in today’s 5G networks and in the future. It is important to accurately estimate channels in these systems because this review influences system performance. When there is a lot of severe fading and noise, LS and MMSE can struggle with both complex calculations and inaccurate results. We propose an approach in this paper that uses neural networks to explore the characteristics of the channel from the signals we receive. The system relies on a deep neural network (DNN) setup to help reduce computing costs and improve the accuracy of channel estimation in MIMO systems having a high number of antennas. Thanks to the neural network, the received signal is converted directly to the channel matrix for better and faster estimation. In many simulations, it was found that deep learning-based channel estimation surpasses LS and MMSE in both accuracy and ability to resist noise. Thanks to its potential, the method is likely to be very useful for massive MIMO in the next generation of wireless networks.
