Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks.

Autor: Essai Ali, Mohamed Hassan¹ (AUTHOR) mhessai@azhar.edu.eg, Abdellah, Ali R.¹ (AUTHOR) mhessai@azhar.edu.eg, Atallah, Hany A.² (AUTHOR) h.atallah@eng.svu.edu.eg, Ahmed, Gehad Safwat³ (AUTHOR) enggehad6145@gmail.com, Muthanna, Ammar⁴ (AUTHOR) muthanna.asa@spbgut.ru, Koucheryavy, Andrey⁴ (AUTHOR) akouch@mail.ru

Publikováno v: Mathematics (2227-7390). Aug2023, Vol. 11 Issue 15, p3386. 19p.

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems.

Autor: Essai Ali, Mohamed Hassan, Alraddady, Fahad, Al-Thunaibat, Mo’ath Y., Elnazer, Shaima

Publikováno v: CMES-Computer Modeling in Engineering & Sciences; 2023, Vol. 135 Issue 1, p755-778, 24p

Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach.

Autor: Essai Ali MH; Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena, Qena, Egypt., Taha IBM; Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.

Publikováno v: PeerJ. Computer science [PeerJ Comput Sci] 2021 Aug 26; Vol. 7, pp. e682. Date of Electronic Publication: 2021 Aug 26 (Print Publication: 2021).