Deep Learning-Based Channel Estimation for MIMO-OFDM Systems

Abstract

The process of channel estimation is essential for precise signal detection and smooth data recovery on Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems which are widely used because they are both spectrally efficient and resistant to multipath fading. Even so, standard channel estimation approaches like Least Squares (LS) and Minimum Mean Square Error (MMSE) perform poorly when shadowing is nonlinear, Doppler is high and the SNR is low. They assume the environment does not vary and is like previous scenarios which does not always match the real-world characteristics of wireless channels. Based on the challenges stated, this study makes use of Convolutional Neural Networks (CNNs) to suggest a new framework for estimating channel conditions from wireless signals. The proposed algorithm learns a complex relationship between the received signal and the channel responses which means it can improve the accuracy of cellular channel estimation by simply using pilot signals without understanding the channel model. Many simulations are carried out for different channel conditions, covering Rayleigh fading, high mobility and various signal-to-noise ratios. Every time, evaluations discovered that the suggested method based on CNN achieves a much lower BER and MSE compared to standard LS and MMSE estimators, showing the most benefit in tough channel conditions where others falter. Besides, the strong results and broad applicability of the proposed method allow it to be deployed in future wireless networks like 5G and above, where channel estimation is very important. The paper shows that adding deep learning to physical layer communication can improve wireless technologies and makes way for future networks that self-adjust to different surroundings.