MSE Performance Comparison of OFDM Channel Estimation

This paper conducts a comparative analysis of the Mean Square Error (MSE) performance in Orthogonal Frequency Division Multiplexing (OFDM) channel estimation. It provides detailed simulations comparing the performance of several classical algorithms, including Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators, with discussions on their respective advantages, limitations, and applicable scenarios. The implementation typically involves MATLAB or Python simulations where channel frequency responses are estimated using pilot symbols inserted in OFDM frames. Key functions include calculating estimation error matrices and plotting MSE curves against Signal-to-Noise Ratio (SNR). Furthermore, the paper introduces novel algorithms and techniques aimed at enhancing the performance and accuracy of OFDM channel estimation, such as compressed sensing-based approaches and deep learning-assisted methods that utilize neural networks for nonlinear channel modeling. Through this research, we can gain deeper insights into the principles and methodologies of OFDM channel estimation, providing valuable references and guidance for performance optimization in practical applications, particularly in 5G and wireless communication systems where robust channel estimation is crucial for maintaining data integrity.