## 5 Discussion
This suggests that when compared to the traditional beam training method, both RBT-DRL and DRL, which make use of reinforcement learning, are more successful at adjusting to shifting channel circumstances and optimizing beamforming for SE. The DRL and RBT-DRL can dynamically modify their SE settings according to system factors such as battery level and UE queue status. Because of their flexibility, they can adjust beamforming tactics in real time, maximizing SE when necessary and maybe sacrificing some SE when battery levels are low. In contrast, the adaptive RL-based methods perform better than the standard beam training method, especially when SNR conditions alter, because the former probably uses a more static approach. The reward functions for RBT-DRL-SE are meant to balance the overhead and performance, which ultimately affects the observed variations in SE among the three approaches. Equations (4)-(10) explain how the SE is determined. With varying amounts of \(\delta\), the DRL method can reach 93.1%, 95.6%, and 98.0% of the highest possible EE. Furthermore, RBT-DRL can provide equivalent or even fewer beam measurements by alternating between several beam training techniques, which can perform better than continuously using a fixed beam training approach. However, DRL can draw lessons from beam training's past and choose the beam training strategy that will maximize long-term rewards. At \(\delta\) = 0.1, RBT-DRL achieves zero beam training at a considerable performance deterioration since optimizing the long-term benefit is comparable to limiting the beam training overhead. When \(\delta\) = 0.8, increasing the EE maximizes the long-term  
Figure 6: EE versus factor. benefit; therefore, it makes sense to do an exhaustive beam search more frequently to achieve a greater EE. From Figure 8, the performance of RBT-DRL, DRL, and three beam training method models is demonstrated to be quite similar in Figure 8, which shows the average EE attained by various beam training policies at varying SNRs. The EE increases to saturation value after that starts to decrease. As we can see, the SE only increases by 39% whereas the overall power consumption in Equation (4) rises by 45% from SNR = 14 dB to 20 dB. Thus, the high power consumption limits the EE performance at SNR = 20 dB. From Figure 9, by increasing task information, the appropriate incentive schemes in Figure 9 facilitate faster learning, with RBT-DRL and DRL requiring around 20% less reward over the whole SNR range. By combining dynamic PA with context awareness and utilizing real-time network conditions to optimize power consumption, the RBT-DRL enhances the solution to handle power consumption problems. To ensure energy-efficient operation without sacrificing SE, the reward mechanism in RBT-DRL can be changed to penalize excessive power usage. Furthermore, hybrid precoding can further lower power consumption by selectively activating antennas based on user density and demand. To reduce redundant power consumption, DRL can facilitate coordinated, dispersed operations among BSs to balance off higher power requirements in mobile and IoT environments. Lastly, the RBT-DRL framework can be verified and improved by benchmarking against low-power solutions, such as lightweight DRL algorithms, to guarantee useful implementation in situations with limited power. The EE increases with SNR in Figure 8 until a certain point, at which point it tends to plateau or even slightly rise. This pattern emphasizes the trade-off between boosting signal quality for SNR and the corresponding power usage. To compare RBT-DRL-based techniques, in particular in [41], the RBT-DRL variations in particular attain higher EE in terms of SNR values when compared to [41], indicating that DRL-based beam training in Figure 8 may be more successful in maximizing EE than adaptive approaches in [41].