An Efficient Adaptive Channel Estimation in a Massive MIMO-OFDM Communication Network Based on Minimization of Error Entropy

The integration of Massive MIMO, OFDM, and NOMA technologies represents a powerful solution for next-generation wireless communication networks.However, these systems face significant challenges, including accurate channel estimation under low Signal-to-Noise Ratio (SNR) conditions, slow convergence and limited adaptability of conventional algorithms such as LMS and NLMS, inter-user interference, and the complexity of modeling frequency-selective fading in large-scale antenna arrays.This study proposes an adaptive channel estimation framework based on the Minimum Error Entropy (MEE) criterion.Unlike traditional methods that rely on second-order statistics, the MEE approach utilizes higher-order statistics, making it more effective in modeling non-Gaussian and impulsive noise commonly encountered in real-world communication channels.The adaptive nature of the filter also allows it to respond dynamically to timevarying channel conditions.Simulation results demonstrate that the proposed MEE-based estimator achieves a remarkably low Mean Squared Error (MSE) of approximately 2 * 10 -4 and an average Bit Error Rate (BER) of around 9 * 10 -4 , outperforming conventional estimators in both accuracy and robustness.The simulation results show that Leveraging Kernel Density Estimation (KDE) for improved error modeling and coefficient adaptation, the proposed method offers a scalable and efficient solution for reliable channel estimation in Massive MIMO-OFDM-NOMA systems.These results highlight the potential of the proposed framework to significantly enhance spectral efficiency and communication reliability in future 5G/6G wireless networks.