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Article

Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation

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Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt
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Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt
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School of Creative Arts and Engineering, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3022; https://doi.org/10.3390/app9153022
Received: 22 May 2019 / Revised: 17 July 2019 / Accepted: 24 July 2019 / Published: 26 July 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Non-orthogonal multiple access (NOMA) is the technique proposed for multiple access in the fifth generation (5G) cellular network. In NOMA, different users are allocated different power levels and are served using the same time/frequency resource blocks (RBs). The main challenges in existing NOMA systems are the limited channel feedback and the difficulty of merging it with advanced adaptive coding and modulation schemes. Unlike formerly proposed solutions, in this paper, we propose an effective channel estimation (CE) algorithm based on the long-short term memory (LSTM) neural network. The LSTM has the advantage of adapting dynamically to the behavior of the fluctuating channel state. On average, the use of LSTM results in a 10% lower outage probability and a 37% increase in the user sum rate as well as a maximal reduction in the bit error rate (BER) of 50% in comparison to the conventional NOMA system. Furthermore, we propose a novel power coefficient allocation algorithm based on binomial distribution and Pascal’s triangle. This algorithm is used to divide power among N users according to each user’s channel condition. In addition, we introduce adaptive code rates and rotated constellations with cyclic Q-delay in the quadri-phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulators. This modified modulation scheme overcomes channel fading effects and helps to restore the transmitted sequences with fewer errors. In addition to the initial LSTM stage, the added adaptive coding and modulation stages result in a 73% improvement in the BER in comparison to the conventional NOMA system. View Full-Text
Keywords: adaptive coding; adaptive modulation; channel estimation; constellation rotation; cyclic-q delay; long-short term memory (LSTM); machine learning; non-orthogonal multiple access (NOMA); recurrent neural networks (RNNs) adaptive coding; adaptive modulation; channel estimation; constellation rotation; cyclic-q delay; long-short term memory (LSTM); machine learning; non-orthogonal multiple access (NOMA); recurrent neural networks (RNNs)
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MDPI and ACS Style

AbdelMoniem, M.; Gasser, S.M.; El-Mahallawy, M.S.; Fakhr, M.W.; Soliman, A. Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation. Appl. Sci. 2019, 9, 3022. https://doi.org/10.3390/app9153022

AMA Style

AbdelMoniem M, Gasser SM, El-Mahallawy MS, Fakhr MW, Soliman A. Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation. Applied Sciences. 2019; 9(15):3022. https://doi.org/10.3390/app9153022

Chicago/Turabian Style

AbdelMoniem, Mai, Safa M. Gasser, Mohamed S. El-Mahallawy, Mohamed Waleed Fakhr, and Abdelhamid Soliman. 2019. "Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation" Applied Sciences 9, no. 15: 3022. https://doi.org/10.3390/app9153022

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