The proposed modulation scheme results in a non-complex high-performance system in terms of the BER and the SER. The performance of deep learning (DL) was comprehensively studied in [24], where the authors proposed an algorithm based on the LSTM network to determine the channel characteristics automatically by training the network with simulated channel data. The results [24] showed an improvement in the sum rates and bit error rates (BERs) when machine learning was utilized compared with conventional NOMA. DL algorithms were also proposed in [16; 17; 25], where the authors applied and verified the proposed algorithms in traffic control. In [26], the authors proposed a partially linear beamforming filter that could be used to receive and decode the received NOMA signal instead of the SIC algorithm. This adaptive filter exhibited high resolution and robustness against variations in the channel environment. The simulations in [26] showed that this method outperforms both SIC-based detection and nonlinear adaptive filtering in a dynamic wireless environment. Unicast-multicast NOMA systems were studied in [27], where the authors were able to attain diversity orders identical to the number of users in the network. The objective of this paper is to present a novel PD-NOMA system. Firstly, we propose an effective channel estimation algorithm based on LSTM neural networks, which can dynamically adapt to the behavior of the fluctuating channel conditions. LSTMs are used to investigate the complex channel features of PD-NOMA. LSTM is preferred over traditional RNNs to model long-term dependencies between samples, since traditional RNNs using back-propagation suffer from the vanishing gradient problem and slow learning [28]. LSTM in combination with PD-NOMA can be used to improve the outage probability and user data rates of the conventional PD-NOMA system. Then, a novel power allocation algorithm based on [PERSON]'s triangle is introduced. Our proposed power allocation algorithm is used to distribute the power coefficients amongst N users by assigning the power coefficients according to each user's channel conditions. In addition, we introduce an adaptive coding scheme based on Bose-Chaudhuri-Hocquenghem (BCH) codes due to its superior ability to identify and correct errors. Thanks to this coding scheme, the code rates can adapt to the predicted channel state depending on the SNR values of the users to enhance the error performance of the system in terms of the BERs of users. The aforementioned adaptive coding scheme was initially introduced in MIMO systems in [29]. However, it has never been tested in combination with NOMA. Finally, an adaptive modulation scheme based on constellation rotation and cyclic Q-delay is proposed to overcome channel fading and hence restore the transmitted sequences with fewer errors. The adaptive modulation scheme was previously implemented in digital video broadcasting (DVB) systems [30; 31; 32]. The proposed algorithms are discussed with respect to the conventional uplink PD-NOMA system. The system proposed is investigated in terms of BER, outage probability, and sum rates under different conditions. This paper is organized as follows: First, a detailed description of the conventional PD-NOMA system model is given in Section 2. Second, adaptive coding, cyclic Q-delay, constellation rotation, the proposed LSTM channel estimation algorithm, and the power allocation algorithm are explained in detail in Section 3. Third, the system evaluation is proposed in Section 4. Section 5 summarizes the simulation parameters and the results of the proposed system evaluation. Finally, Section 6 concludes the paper.