Citation :

Nilesh Kambale, Priyanka D. Halle, Ashwini Basavraj Utture, Nutan Patil, "Machine Learning-Based Adaptive Channel Estimation for Next-Generation Wireless Networks," International Journal of Electrical and Electronics Engineering, vol. 13, no. 6, pp. 28-42, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I6P103

Abstract

Extremely precise channel estimation is necessary for effective communication in developing next-generation wireless networks. High mobility and abundant connectivity make communication more difficult. Time-varying channels cannot be handled by the conventional methods for channel estimation because of their limited performance and significant pilot overhead. Instead of using complicated deep learning models, the paper suggests a unique Machine Learning (ML) framework for adaptive channel estimation that is based on straightforward ML techniques. By combining reinforcement learning with regression and classification models, the suggested framework improves channel estimate accuracy. The results of the simulation studies show improved adaptive pilot allocation optimization. The suggested machine learning model attains a Bit Error Rate (BER) of 1.7 × 10⁻4 and a normalized Mean Square Error (MSE) of -18.9 dB. The findings show that pilot overhead was reduced by almost 20%. The successful merger of traditional machine learning with adaptive optimization is the main innovation in real-time channel estimation. The study’s findings also demonstrate the ML framework’s scalability and usefulness in 5G systems and upcoming 6G wireless networks.

Keywords

Adaptive Channel Estimation, Dynamic Networks, Machine Learning, Next-Generation Wireless Networks, Regression, Reinforcement Learning, Signal Processing.

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