Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions

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Year : 2026 | Volume : 13 | 02 | Page :
By

Manan Desai,

Ansh Patel,

Rizwan Alad,

Ashish Pandya,

Nirav Desai,

Purvang Dalal,

  1. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
  2. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
  3. Associate Professor, Department of Electronics and Communication Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
  4. Assistant Professor, Department of Electronics and Communication Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
  5. Assistant Professor, Department of Electronics and Communication Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
  6. Professor and Head, Department of Electronics and Communication Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India

Abstract

Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined statistical assumptions, which limit their performance under highly dynamic, non-linear, and high-mobility propagation environments. In recent years, Machine Learning (ML) and Deep Learning (DL) based channel estimation techniques have emerged as powerful alternatives that can learn complex channel characteristics directly from received data without explicit channel models. This paper presents a comprehensive review of ML-based channel estimation techniques for wireless communication systems, covering Orthogonal Frequency Division Multiplexing (OFDM) system models, channel propagation fundamentals including Doppler shift, coherence time, coherence bandwidth, and delay spread, conventional estimation methods, convolutional neural network (CNN) architectures including Super-Resolution CNN (SRCNN), Denoising CNN (DnCNN), and Regression CNN, as well as their application to 5G NR channel models including Tapped Delay Line (TDL) and Clustered Delay Line (CDL) profiles. Detailed analysis of Doppler parameters across TDL channel profiles at 1.8 GHz carrier frequency demonstrates Doppler shifts ranging from 5 Hz in pedestrian scenarios (TDL-A) up to 500 Hz in high-speed train scenarios (TDL- E). Performance analysis in terms of BER, Mean Square Error (MSE), and Signal-to-Noise Ratio (SNR) demonstrates that ML- based techniques provide significant gains over traditional methods. Applications in satellite communication, Non-Terrestrial Networks (NTN), and future 6G systems are also discussed.

Keywords: Channel Estimation, Machine Learning, Deep Learning, 5G NR, OFDM, Convolutional Neural Network, TDL Channel, CDL Channel, Doppler Shift, BER, MMSE, SRCNN, DnCNN, 6G, Non-Terrestrial Networks, Satellite Communication

How to cite this article: Manan Desai, Ansh Patel, Rizwan Alad, Ashish Pandya, Nirav Desai, Purvang Dalal. Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions. Journal of Telecommunication, Switching Systems and Networks. 2026; 13(02):-.
How to cite this URL: Manan Desai, Ansh Patel, Rizwan Alad, Ashish Pandya, Nirav Desai, Purvang Dalal. Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions. Journal of Telecommunication, Switching Systems and Networks. 2026; 13(02):-. Available from: https://journals.stmjournals.com/jotssn/article=2026/view=252233

References

  1. Oyerinde OO, Mneney SH. Review of channel estimation for wireless communication systems. IETE Technical review. 2012 Jul 1;29(4):282-98.
  2. Laidouni MZ, Benyahia TE, Pavlović BZ, Amokrane SB, Adli TB. Deep learning channel estimation for 5G wireless communications. Vojnotehnički glasnik. 2023;71(4):911-40.
  3. Letaief KB, Chen W, Shi Y, Zhang J, Zhang YJ. The roadmap to 6G: AI empowered wireless networks. IEEE communications magazine. 2019 Aug 21;57(8):84-90.
  4. Saad W, Bennis M, Chen M. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE network. 2019 Oct 15;34(3):134-42.
  5. Ye H, Li GY, Juang BH. Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters. 2017 Sep 28;7(1):114-7.
  6. Soltani M, Pourahmadi V, Mirzaei A, Sheikhzadeh H. Deep learning-based channel estimation. IEEE Communications Letters. 2019 Feb 12;23(4):652-5.
  7. Chun CJ, Kang JM, Kim IM. Deep learning-based channel estimation for massive MIMO systems. IEEE Wireless Communications Letters. 2019 Apr 19;8(4):1228-31.
  8. Dong P, Zhang H, Li GY, Gaspar IS, NaderiAlizadeh N. Deep CNN-based channel estimation for mmWave massive MIMO systems. IEEE Journal of Selected Topics in Signal Processing. 2019 Jul 1;13(5):989-1000.
  9. Jiang W, Schotten HD. Deep learning for fading channel prediction. IEEE Open Journal of the Communications Society. 2020 Mar 23;1:320-32.
  10. Zhang Y, Wu Y, Liu A, Xia X, Pan T, Liu X. Deep learning-based channel prediction for LEO satellite massive MIMO communication system. IEEE Wireless Communications Letters. 2021 May 24;10(8):1835-9.
  11. Jones VK, Raleigh GC. Channel estimation for wireless OFDM systems. InIEEE GLOBECOM 1998 (Cat. NO. 98CH36250) 1998 Nov 8 (Vol. 2, pp. 980-985). IEEE.
  12. Barb GR, Otesteanu M, Budura G, Balint C. Performance evaluation of TDL channels for downlink 5G MIMO systems. In2019 International Symposium on Signals, Circuits and Systems (ISSCS) 2019 Jul 11 (pp. 1-4). IEEE.
  13. Röhrig C, Cramer B. Experimental Evaluation of LPWAN Technologies: mioty, LoRaWAN, Sigfox, NB-IoT, and LTE-M in Deep Indoor Environments. arXiv preprint arXiv:2605.23483. 2026 May 22.
  14. Garcia-Roger D, González EE, Martín-Sacristán D, Monserrat JF. V2X support in 3GPP specifications: From 4G to 5G and beyond. IEEE access. 2020 Oct 5;8:190946-63.
  15. Kay SM. Fundamentals of statistical signal processing: estimation theory. Prentice-Hall, Inc.; 1993 Mar 1.
  16. Tuzlukov VP. Signal detection theory. Springer Science & Business Media; 2013 Mar 14.
  17. Afsar MM, Tayarani-N MH. A. goldsmith, wireless communications, cambridge uni-versity press, 2005. Journal of Network and Computer Applications. 2014;46:198-226.
  18. Tse D, Viswanath P. Fundamentals of wireless communication. New York, NY: Cambridge; 2005 Jun.
  19. Oubassghir M, Boulouird M. A survey on mmwave and massive mimo for 5g and beyond: Potential and challenges. In2025 IEEE 8th Congress on Information Science and Technology (CiSt) 2025 Oct 4 (pp. 298-303). IEEE.
  20. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
  21. Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence. 2015 Jun 1;38(2):295-307.
  22. Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing. 2017 Feb 1;26(7):3142-55.
  23. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014 Dec 22.
  24. Herrera JL, Montebugnoli S, Scotece D, Foschini L, Bellavista P. A tutorial on O-RAN deployment solutions for 5G: From simulation to emulated and real testbeds. IEEE Communications Surveys & Tutorials. 2025 Jun 9;28:1709-48.
Ahead of Print Subscription Review Article
Volume 13
02
Received 04/08/2026
Accepted 10/08/2026
Published 12/08/2026
Publication Time 8 Days

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