## 8 Simulation Environment  
In this section, a description of the simulation settings and parameters is introduced. Our examined downlink NOMA cell contains one base station (BS) and three distinct users in which the BS and each user in the cell is supplied with one antenna. For the downlink NOMA scenario, the modulated signals are superimposed and transmitted by BS to the users through uncorrelated Rayleigh and Rician fading channels affected by additive white gaussian noise (AWGN), where the noise spectral density \(N_{0}=-174\) dBm and path loss exponent is 4.  
In this paper, simulations are conducted using MATLAB to simulate and to emphasize the following: first, to evaluate the effectiveness of embedding the proposed DL-based LSTM network to accurately estimate the channel parameters for each user in downlink NOMA cell. Second, to integrate the proposed DL channel estimation algorithm with the derived optimized power allocation scheme and compare it with the NOMA system when fixed power factors are considered along with the proposed DL scheme. Monte-Carlo simulations are conducted with \(N=10^{6}\) iterations, and at the start of every single set of iterations, pilot symbols are generated at random and recognized at the BS and at the receiver side of each user. The main simulation parameters are summarized in Table 1.  
In our simulation environment, we assume that the channel state information (CSI) is not available at the receiver side. Therefore, for the sake of comparison and in order to investigate the efficiency of the proposed DL algorithm, two methods for channel estimation are implemented at the receiver side for each user. The first method, which is the proposed scheme, uses DNN-based LSTM layer to estimate the desired channel coefficients. The gradient descent algorithm is applied in conjunction with LSTM layer, and the LSTM layer is attached to a fully connected layer, where each neuron in the former layer is fully connected to every neuron in the consequent layer. The second channel estimation scheme implemented at each receiver side is initiated based on the minimum mean square error (MMSE) [33]. The MMSE technique will be applied as a conventional channel estimation technique for each user in NOMA cell, and in the simulations results, we refer to the MMSE scheme as conventional NOMA, to clarify that users are using the MMSE procedure for estimating the channel coefficients before recovering the original signal.  
Channel taps that are employed to model the Rayleigh fading wireless channel are generated on the basis of ITU channel models. Throughout the simulations, NOMA system parameters are employed on the basis of the long-term evolution (LTE) standard [34]. Both training and implementation phases are conducted online throughout the simulations, and the fading coefficients in the testing stage are generated such that these coefficients are not the same as in the training stage. At the end of the training stage, which includes training and testing data, the trained network will be utilized as online channel estimator for the users rather than the conventional NOMA scheme that use the MMSE procedure for channel estimation.  
Initially, different power factors are allocated for each user according to their distance from the BS and the current channel gain. Power allocation coefficients \(a_{n}\), \(a_{m}\), and \(a_{f}\) are defined for near, middle, and far users, respectively. In the fixed power allocation (FPA) scenario, we can assign \(a_{f}=0.7\), \(a_{m}=0.2\), and \(a_{n}=0.1\). Alternatively, in the optimized power scheme, power factors are apportioned between users according to the analytical form derived earlier for each user in Section 4. The propagation distances for each user with respect to base station are initially assigned in the simulation files as follows \(d_{f}=1000\) m, \(d_{m}=500\) m, and \(d_{n}=200\) m. Quadrature phase shift keying QPSK is utilized as a modulation scheme for the data symbols and pilot sequences. The applied transmitted power mainly varies from 0 to 30 dBm.