Application of a Machine Learning Model to the realization of a wireless communication system

• Autorzy: Citko Wieslaw , Trzebiatowski Adam , Sienko Wieslaw

Identyfikatory

DOI

10.24425/ijet.2025.153540

• Języki publikacji: EN

Abstrakty

EN

This paper presents the author's proposal for a neural detector realization of a Massive-MIMO-OFDM system using extended Hopfield neural circuits. An important feature of such an implementation is that the system can be learned without the need to solve multi-parameter optimization tasks requiring high computational power.

Słowa kluczowe

EN

wireless communication system; machine learning; Massive-MIMO-OFDM systems

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 1
• Strony: 25--30
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Citko Wieslaw

• Gdynia Maritime University

• https://orcid.org/0000-0003-3517-4046

autor

Trzebiatowski Adam

• Gdynia Maritime University

autor

Sienko Wieslaw

• Gdynia Maritime University

• https://orcid.org/0000-0003-2941-2415

Bibliografia

• [1] Y. C. Eldar, A. Goldsmith, D. Gunduz, H. V. Poor H (Eds.), Machine Learning and Wireless Communication, Cambridge University Press, 2022.
• [2] N. Shlezinger, N. Farsad, Y. C. Eldar, A. Goldsmith, “Model-Based Machine Learning for Communications in Machine Learning and Wireless Communication,” Cambridge University Press, 2022.
• [3] H. He, H. Ye, S. Jin, G. Y. Li, “Channel Estimation, Feedback, and Signal Detection,” in Machine Learning and Wireless Communication, Cambridge University Press, 2022.
• [4] S. Coleri, M. Ergen, A. Puri, A. Bahai, “Channel Estimation Techniques Based on Pilot Arrangement in OFDM Systems,” IEEE Transactions on Broadcasting, vol. 48, no. 3, pp. 223-229, Sept. 2002, https://doi.org/10.1109/TBC.2002.804034
• [5] M. Soltani, V. Pourahmadi, A. Mirzaei, H. Sheikhzadeh, “Deep Learning-Based Channel Estimation,” IEEE Communications Letters, vol. 23, no. 4, pp. 652-655, April 2019, https://doi.org/10.1109/LCOMM.2019.2898944
• [6] C. Dong et al., “Image Super-Resolution Using Deep Convolutional Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 38 (2014): 295-307.
• [7] O. Sholev, H. H. Permuter, E. Ben-Dror, W. Liang, “Neural Network MIMO Detection for Coded Wireless Communication with Impairments,” 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), 2020, pp. 1-8, https://doi.org/10.1109/WCNC45663.2020.9120517
• [8] W. Citko, W. Sienko, “Inpainted Image Reconstruction Using an Extended Hopfield Neural Network Based Machine Learning System,” Sensors, 22 (2022), no. 3, https://doi.org/10.3390/s22030813
• [9] W. Citko, W. Sienko, "Image Recognition and Reconstruction With Machine Learning: An Inverse Problem Approach," in IEEE Access, vol. 11, pp. 107463-107471, 2023, https://doi.org/10.1109/ACCESS.2023.3315831
• [10] T. Marzetta, E. Larsson, H. Yang, H. Hgo, Fundamentals of Massive MIMO, Cambridge University Press, 2016.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).