International Journal of Computer Theory and Engineering

Abstract—This paper explores and evaluates advanced signal processing techniques for blind channel identification and equalization in modern 5G networks. The focus is on traditional statistical methods and emerging artificial intelligence based on the autoencoder method. The study concentrates on two principal frameworks: high-order cumulants, specifically the fourth-order and sixth-order cumulants, which are capable of exploiting non-Gaussian and higher-order statistical dependencies in digitally modulated signals, and Artificial Intelligence (AI)-based models, particularly autoencoders, which use deep learning architectures to reconstruct or denoise received signals without prior knowledge of the transmission channel. To systematically assess the capabilities of each method, three distinct algorithms were developed and implemented: the fourth-order cumulant, the sixth-order cumulant, and the autoencoder algorithm. These algorithms were applied to 5G signals transmitted over Rayleigh multipath fading channels, modeled using standard propagation profiles such as BRAN-A, BRAN-B, and Proakis B. Simulations were conducted over a wide range of Signal-to-Noise Ratios (SNRs) and varying levels of noise and channel dynamics. Key findings from the simulation results are as follows: The sixth-order cumulant demonstrates superior noise resilience compared to the fourth-order cumulant, making it more suitable for environments with stronger interference or non-Gaussian noise. The autoencoder algorithm achieves the highest estimation and identification accuracy after sufficient training, due to its ability to model complex, nonlinear transformations. However, this comes with increased data requirements and computational cost. The fourth-order cumulant algorithm is computationally efficient, theoretically tractable, and performs well under stationary and low-noise conditions. However, it exhibits a notable performance decline in time-varying or high-noise scenarios. In contrast, the autoencoder algorithm shows enhanced robustness and adaptability to nonlinear, time-varying, and imperfectly modeled environments. Its strength lies in learning the underlying channel effects implicitly from raw data, without requiring a structured analytical model. Nevertheless, this benefit is offset by higher computational complexity and a lack of interpretability compared to the cumulant-based methods. In conclusion, while cumulant-based techniques remain valuable in structured or lightly impaired channels, AI based on autoencoder models, especially autoencoders, offers a more powerful and flexible solution for blind equalization under realistic and challenging 5G conditions.

Keywords—identification, equalization, 5G, High-Order Cumulant (HOS), Bran A, Bran B, Proakis B, Artificial Intelligence (AI), Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Orthogonal Frequency Division Multiple Access (OFDMA)

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Cite: Said Elkassimi, Bouzid Manaut, and Said Safi, "A High-Order Cumulants and AI-Based Methods for Blind Identification and Equalization in 5G Networks," International Journal of Computer Theory and Engineering, vol. 18, no. 2, pp. 145-153, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).