## 9 Simulation Results and Discussion  
Simulation outcomes that clarify the comparison between the developed RL based Q-learning algorithm and the conventional NOMA scheme that applies MMSE method to predict the channel coefficients for each device are shown in Figure 4 in terms of BER versus power transmitted. The predicted channel parameters using both schemes are employed for the signal detection for each user device and the simulated results are shown where fixed power allocation (FPA) is considered. When the developed Q-algorithm is applied for channel estimation, each user device in the examined MISO-NOMA cell provides a noticeable improvement in lowering the BER compared to the MMSE procedure. At particular BER values such as \(10^{-2}\), the attained power saving by the Q-learning algorithm is within 2 dBm for far and middle user devices, while a power reduction within 1 dBm is recorded for the near user. In terms of the outage probability against applied power, Figure 5 illustrates the results for the inspected user devices in the MISO-NOMA cell when the developed Q-learning and standard MMSE are considered as a channel estimator schemes. Far, and middle devices simulation outcomes indicate about 2 dBm enhancement in saving power to realize \(10^{-2}\) outage probability when the developed Q-learning algorithm scenario is applied compared to the MMSE procedure. Similarly, a near user with the developed Q-learning algorithm displays a 1 dBm improvement in power saving with respect to the MMSE scheme. This enhancement in power saving verifies the advantage of the developed Q-model as a channel estimator compared to the MMSE technique. Figure 4: BER vs. power (Q-learning & Conventional NOMA (MMSE)). In Figure 6, we implement three baselines for comparisons: (1) standard minimum mean square error (MMSE) based channel prediction scheme [45]; (2) DL algorithm based on LSTM network for channel prediction applied in [17]; and RL based actor-critic procedure for channel prediction applied in [15]. This figure shows simulation results for the sum rate for all the user devices in the MISO-NOMA network versus applied power. Based on the simulation outcomes, it is evidently shown that the developed RL based Q-learning algorithm reveals superiority over standard MMSE procedure by 12 b/s/Hz approximately. Furthermore, the developed Q-learning scheme performs an enhancement over the DL based LSTM procedure presented in [17] by 2 b/s/Hz. For the third benchmark in [15], we generate the simulation environment according to the following: the actor and critic networks are both composed of two hidden layers with 400 and 300 nodes, respectively. The learning rate for actor and critic networks are \(10^{-4}\) and \(10^{-3}\) respectively. The discount factor \(\gamma\) is set to be 0.9 and has a buffer size of \(10^{5}\)[15]. Our developed RL based Q-learning procedure, shows superiority over the RL based actor-critic procedure at low power levels while starting from 23 dBm the actor-critic procedure starts showing some enhancement in terms of sum rates compared to the Q-learning process. These findings can validate that the developed Q-learning algorithm can be a competitive scheme compared to other algorithms that mainly depend on hidden layers to predict channel parameters. Simulation outcomes for the sum rate against different number of users in the applied MISO-NOMA cell are illustrated in Figure 7, where the reference power is chosen to be 1 dBm. In addition to our proposed Q-learning algorithm, three distinct channel prediction methods are investigated as a benchmark comparison: (1) standard minimum mean square error (MMSE) based channel prediction scheme [45]; (2) DL algorithm based on LSTM network for channel prediction applied in [17]; and RL based actor-critic technique for channel estimation applied in [15]. As revealed from the results, our developed RL based Q-learning algorithm can achieve a substantial greater sum rate with respect to standard MMSE procedure, by at least 2 b/s/Hz.