In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.

This paper analyzes the power optimization for a downlink multi-input single-output non orthogonal multiple access (MISO-NOMA) system. Power coefficients are optimized in order to maximize the sum throughput of the system users based on the total transmitted power and Quality of service (QoS) constraints. First, we formulate a simple expression for Signal to interference noise ratio (SINR) for each user in MISO-NOMA system, then an analysis for the optimization problem and the considered constraints to prove the concavity of the objective function is presented. Lagrange function and Karush-Kuhn-Tucker (KKT) optimality conditions are utilized to derive the optimal power coefficients. Simulations are conducted and optimization solver is used to investigate the improvement achieved when power coefficients are optimized compared to fixed power scheme. Simulation results are conducted in terms of sum-rate, outage probability, SIC error and users' individual rates. Results revealed that optimized power scheme is more satisfactory than fixed power scheme for far user than near user and both schemes are superior compared to conventional orthogonal multiple access (OMA) scheme.

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