Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation  
[PERSON]\({}^{1,\star}\)  
1 Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt; [EMAIL_ADDRESS] (S.M.G. ); [EMAIL_ADDRESS] (M.S.E.-M.)  
2 Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt; [EMAIL_ADDRESS] (M.S.E.-M.)  
3 School of Creative Arts and Engineering, Staffordshire University, Stoke-on-Trent ST4 2 DE, UK; [PERSON]DRESS]  
4 Correspondence: [EMAIL_ADDRESS]  
[PERSON]\({}^{1}\)  
1 Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt; [EMAIL_ADDRESS] (S.M.G. ); [EMAIL_ADDRESS] (M.S.E.-M.)  
2 Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt; [PERSON]ADDRESS] (M.S.E.-M.)  
3 School of Creative Arts and Engineering, Staffordshire University, Stoke-on-Trent ST4 2 DE, UK; [PERSON]DRESS]  
4  
[PERSON]\({}^{2}\)  
1 Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt; [EMAIL_ADDRESS] (S.M.G. ); [EMAIL_ADDRESS] (M.S.E.-M.)  
2 Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt; [EMAIL_ADDRESS] (M.S.E.-M.)  
3 School of Creative Arts and Engineering, Staffordshire University, Stoke-on-Trent ST4 2 DE, UK; [PERSON]DRESS]  
4  
[PERSON]\({}^{3}\)  
1 Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport (AAASTMT), Cairo P.O. Box 2033, Egypt; [EMAIL_ADDRESS] (S.M.G. ); [EMAIL_ADDRESS] (M.S.E.-M.)  
3 School of Creative Arts and Engineering, Staffordshire University, Stoke-on-Trent ST4 2 DE, UK; [PERSON]DRESS]  
4  
Footnote 1: email: {[PERSON], M.  
NOMA can exploit the existing resources more efficiently by opportunistically taking advantage of the users' precise channel conditions and serving several users with diverse quality of service (QoS) requirements. Due to the new power domain dimension, NOMA systems can be combined with present multiple access (MA) models [2]. The power-domain NOMA (PD-NOMA) is considered a strong candidate for use in future 5G networks. PD-NOMA guarantees that several users are served with the same resources using superposition coding (SC) methods at the transmitter and successive interference cancellation (SIC) at the receiver [3]. PD-NOMA challenges lie in the possibility of appropriately allocating power coefficients according to each user's channel condition as well as the opportunity to merge NOMA with adaptive coding and modulation schemes in order to enhance the system's capacity and user sum rates. In order to resolve these issues, complete knowledge of the channel conditions (channel state information (CSI)) of individual users is essential [4]. Machine learning algorithms can adapt to changes and estimate the channel conditions for each user, and hence, they are considered strong candidates for future radio networks [2]. Previous studies mainly focused on how the outage probability of the NOMA system changes under different user conditions, excluding the variation in the channel [5; 6]. The authors of [5; 7; 8] investigated error performance, power and resource allocation algorithms, and capacity performance in NOMA to enhance the reliability and efficiency of NOMA systems compared to OMA. Recently, numerous innovative enhancements to NOMA were proposed, aiming to discover the superlative candidate for implementation in 5G systems [9; 10]. The authors of [11; 12; 13; 14] examined the performance of downlink transmission in NOMA.