INTELLIGENT DEEP LEARNING-BASED CHANNEL ESTIMATION FRAMEWORK FOR NEXT-GENERATION WIRELESS COMMUNICATION SYSTEMS
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Dr B Ramprasad
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Department Of ECE, SVS Group of Institutions, Hanmakonda, Telangana
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Abstract
The increasing demand for high-speed wireless communication services, coupled with the deployment of advanced technologies such as Massive Multiple-Input Multiple-Output (MIMO), millimeter-wave communications, Internet of Things (IoT), and Sixth Generation (6G) networks, has significantly increased the complexity of wireless channel environments. Accurate channel estimation plays a critical role in ensuring reliable communication, efficient resource utilization, and high-quality service delivery. Conventional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) often struggle to provide optimal performance in highly dynamic and complex communication environments due to nonlinear channel characteristics, mobility, and interference. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving channel estimation accuracy through intelligent learning and adaptive optimization. This paper presents a comprehensive study of AI-based channel estimation techniques and proposes an Intelligent Deep Learning-Based Channel Estimation Framework (IDL-CEF) designed to enhance wireless communication performance. The proposed framework integrates deep neural networks, machine learning algorithms, adaptive signal processing, and real-time channel prediction mechanisms. Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods. The findings indicate that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.
Keywords
Channel Estimation Artificial Intelligence Deep Learning Wireless Communication Massive MIMO 6G Networks Signal Processing Machine Learning.
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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 7
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Jul 06 2026
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Akshitha, A. & Kumar, N. (2026). Intelligent Deep Learning-Based Channel Estimation Framework for Next-Generation Wireless Communication Systems. International Journal of Science, Strategic Management and Technology, 02(7). https://doi.org/10.55041/ijsmt.v2i7.010
Akshitha, A, and N.Prashanth Kumar. "Intelligent Deep Learning-Based Channel Estimation Framework for Next-Generation Wireless Communication Systems." International Journal of Science, Strategic Management and Technology, vol. 02, no. 7, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i7.010.
Akshitha, A, and N.Prashanth Kumar. "Intelligent Deep Learning-Based Channel Estimation Framework for Next-Generation Wireless Communication Systems." International Journal of Science, Strategic Management and Technology 02, no. 7 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i7.010.
References
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[3] E. G. Larsson et al., “Massive MIMO for Next Generation Wireless Systems,” IEEE Communications Magazine, vol. 52, no. 2, pp. 186–195, 2014.
[4] H. Ye, G. Y. Li, and B. H. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114–117, 2018.
[5] X. Ma and Z. Gao, “Data-Driven Deep Learning for Massive MIMO Channel Estimation,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5500–5511, 2020.
[6] C. Wen, W. Shih, and S. Jin, “Deep Learning for Massive MIMO CSI Feedback,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748–751, 2018.
[7] H. Huang et al., “Deep Learning for Physical Layer 5G Wireless Techniques,” IEEE Wireless Communications, vol. 26, no. 2, pp. 93–99, 2019.
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[9] H. Tataria et al., “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021.
[10] M. Chen et al., “Machine Learning for Wireless Networks with Artificial Intelligence,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1044–1071, 2020.
[11] W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems,” IEEE Network, vol. 34, no. 3, pp. 134–142, 2020.
[12] Z. Zhang et al., “AI-Driven Wireless Communication for Future Networks,” IEEE Network, vol. 35, no. 1, pp. 154–161, 2021.
[2] D. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, 2005.
[3] E. G. Larsson et al., “Massive MIMO for Next Generation Wireless Systems,” IEEE Communications Magazine, vol. 52, no. 2, pp. 186–195, 2014.
[4] H. Ye, G. Y. Li, and B. H. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114–117, 2018.
[5] X. Ma and Z. Gao, “Data-Driven Deep Learning for Massive MIMO Channel Estimation,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5500–5511, 2020.
[6] C. Wen, W. Shih, and S. Jin, “Deep Learning for Massive MIMO CSI Feedback,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748–751, 2018.
[7] H. Huang et al., “Deep Learning for Physical Layer 5G Wireless Techniques,” IEEE Wireless Communications, vol. 26, no. 2, pp. 93–99, 2019.
[8] Y. Sun et al., “Artificial Intelligence and Machine Learning for 6G Networks,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 1221–1261, 2022.
[9] H. Tataria et al., “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021.
[10] M. Chen et al., “Machine Learning for Wireless Networks with Artificial Intelligence,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1044–1071, 2020.
[11] W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems,” IEEE Network, vol. 34, no. 3, pp. 134–142, 2020.
[12] Z. Zhang et al., “AI-Driven Wireless Communication for Future Networks,” IEEE Network, vol. 35, no. 1, pp. 154–161, 2021.
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