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

Abdelmoniem Abdelmoniem,Gasser Gasser,Soliman Soliman,Fakhr Fakhr,El-Mahallawy El-Mahallawy

doi:10.3390/app9153022

Abstract

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.

Highlights

Non-orthogonal multiple access (NOMA) addresses the increasing demand on resources inFifth Generation (5G) networks by accommodating several users within the same resource block (RB), which improves the bandwidth efficiency compared with orthogonal multiple access (OMA)techniques [1], i.e., time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA).Appl
This work, we presented a channel new channel estimation technique on long-short term memory (LSTM), aiming to
In this we presented a new estimation technique basedbased on LSTM, aiming to improve improve the outage probability, bit error rate (BER), and user sum rate of the conventional the outage probability, BER, and user sum rate of the conventional NOMA system

Summary

Introduction

Non-orthogonal multiple access (NOMA) addresses the increasing demand on resources inFifth Generation (5G) networks by accommodating several users within the same resource block (RB), which improves the bandwidth efficiency compared with orthogonal multiple access (OMA)techniques [1], i.e., time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA).Appl. Non-orthogonal multiple access (NOMA) addresses the increasing demand on resources in. Fifth Generation (5G) networks by accommodating several users within the same resource block (RB), which improves the bandwidth efficiency compared with orthogonal multiple access (OMA). The power-domain NOMA (PD-NOMA) is considered a strong candidate for use in future 5G networks. 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. Machine learning algorithms can adapt to changes and estimate the channel conditions for each user, and they are considered strong candidates for future radio networks [2]

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