## 1 Introduction
### Contributions to Knowledge  
The channel prediction problem in downlink NOMA systems was considered in numerous works. In addition, there have been several works that apply machine learning (ML) to handle the channel estimation task in wireless communication systems. However, most of the current research on channel prediction in the NOMA systems based on ML is introduced via deep neural networks. To the best of the author's knowledge, currently, there is no research that manages the channel approximation task in a multiuser multi-input single-output NOMA system through an RL based Q-learning algorithm. The RL based Q-learning algorithm is developed based on maximizing the sum rates for all users in the network such that it can be used efficiently to predict the channel parameters for each user in the MISO-NOMA cell. In addition, in this work, a structured mathematical analysis is introduced to formulate a non-complex analytical form for the power allocation for user devices in the examined MISO-NOMA system based on boosting the sum rate of the system while considering the constraints of the total power budget in the system, and the QoS for each user. Furthermore, the performance of the MISO-NOMA system is investigated when both the developed Q-learning algorithm for channel estimation and the derived power allocation scheme are jointly implemented. In this work, the contributions can be summed up as shown:  
* In this study, a framework is proposed to illuminate how RL based Q-learning algorithm is developed based on maximizing the sum rates for all users in a MISO-NOMA system in order that it can be used dynamically to predict the channel parameters for each user in the MISO-NOMA cell. * As a reference comparison, four further simulation environments are established. (1) the standard minimum mean square error (MMSE) based channel prediction scheme ([PERSON] et al. ); (2) the DNN algorithm based on LSTM network for channel prediction applied in [17], (3) the RL based actor-critic procedure for channel prediction applied in [15], (4) the fourth simulation environment is dependent on applying RL based State-Action-Reward-State-Action (SARSA) procedure ([PERSON] et al. and [PERSON] et al.). The simulation outcomes of these environments are compared with the results of our proposed RL based Q-learning scheme, and the results emphasized that dependability can be assured by our developed Q-model for predicting channel parameters even when the number of devices in the cell is increased. * To validate the efficacy of the developed Q-learning algorithm for channel prediction, the developed Q-model is investigated using Rayleigh and Rician fading channels. * Evaluate the beneficial impact of cooperatively integrating the RL based Q-learning algorithm for channel prediction and the derived power allocation scheme for the purpose of multiuser recognition in the power domain MISO-NOMA system. * The optimized power allocation scheme and the fixed power allocation scheme are both compared when the developed Q-learning scheme is implemented as a channel estimator. The remainder of this paper is structured as follows. Section 2 describes the system model. Analysis of the optimization problem is presented in Section 3. The optimization framework and procedure are discussed in Section 4. The RL structure is introduced in Section 5. Section 6 discusses the Q-learning algorithm-based channel prediction. The RL-based Q-model architecture and channel estimation algorithm are summarized in Section 7. The simulation environment is described in Section 8, and simulation results are presented in Section 9. Lastly, conclusions are shown in Section 10. _Notation_: bold lower-case letters denote vectors, bold upper-case letters denote matrices, and lower-case letters denote scalars. The subscript on a lower-case letter \(x_{i}\) represent \(i\)th element of vector \(\mathbf{x}\). \(E(\cdot)\) refers to the expectation and \((\cdot)^{\mathrm{T}}\) refers to the transpose of the vector.