## 5 Discussion
The saturation of EE at higher SNRs highlights the fact that merely raising transmit power above a particular threshold does not always result in a corresponding rise in EE since power consumption rises faster than possible signal quality gains. As shown in [42], in comparison to current state-of-the-art (SOTA) algorithms, this approach focuses on mathematical convergence to a local solution that is approximately less complex and in reaching a compromise between computational complexity and SE. From the benchmark  
Figure 7: Related of SE to SNR. when the beam training overhead is reduced, the RBT-DRL continuously achieves a higher EE at different SNR levels, peaking at about 90 Mb/s/Hz. By utilizing DRL and adaptive beam training for dynamic network conditions, RBT-DRL provides notable advantages over SOTA hybrid precoding techniques such as compressive sensing-based approaches as shown in [42], conventional precoding, and heuristic optimization methods. RBT-DRL is a more adaptable and energy-efficient solution for ultra-dense, high-mobility networks because of its capacity to dynamically optimize RF chain utilization and precoding based on environmental feedback. The needed long-term anticipated advantage for either RBT-DRL or DRL during an interaction with the environment is substantially larger than that for both RBT-DRL and beam training, and it does not change with varying SNR. To acquire greater rewards with fewer training beams, one can examine the distribution of action choices for all beam training policies. The SE of several techniques is displayed versus the number of N in Figure 10. Keep in mind that the experiment uses the highest bound of SE achieved by the  
Figure 8: Related of EE to SNR. Figure 9: Reward versus SNR. best full-digital beamforming. Figure 11 makes it evident that the SE attained by all RBT-DRL grows as the number of Ns rises. On the other hand, DRL and beam training greatly improve performance, whilst the random power and maximal power schemes only slightly improve SE. Furthermore, the SE obtained by the suggested RBT-DRL approach has a SE greater than previous ways as the number of Ns increases in the mm-wave. Furthermore, when employing RBT-DRL and beam training, the maximal power scheme performs worse consistently. This shows how optimizing PA and beamforming together can raise the SE of mm-wave systems. Figure 5.0 SE versus a sample of an mm-wave channel. Figure 5.1 SE versus a number of antennas. From Figure 11, the three primary methods for preserving the stability of the channel estimation are interactions, examining the role of RBT-DRL provides important insights into channel stability, and if it can successfully adjust to changing channel conditions, it may improve SE performance. As a result, stability is provided during simulation by the mm-wave-sampling realization in both native and mutant forms, as shown in Figure 5.2 If you have the channel realizations available, you can replace the range function with actual channel data or change it to acquire a more realistic depiction, as the beam training bonding has shown a slight decrease in the number of possible channel realizations for the SE for the highest possible RBT-DRL award. The goal of this technique is to attain maximum efficiency by refining the beamforming strategy. Beam training offers less adaptive beamforming options, and DRL approaches, while trying to maximize beamforming, may not be as specialized as RBT-DRL. The EE performance for each of the three methods, RBT-DRL, DRL, and beam training, is plotted against time steps in Figure 5.3 RBT-DRL consistently produces larger EE values than DRL and beam training, as seen in Figure 5.4 However, RBT-DRL exhibits notable EE fluctuations, most likely as a result of altered ambient factors or system characteristics. Although the DRL method does not achieve the highest EE levels of beam training, it does show greater stability than beam training. The RBT-DRL approach shows resilience in sustaining steady performance even if it has the lowest average EE.