## 5 Discussion  
The performance of the proposed experience-driven power distribution approach in the mm-wave AB-HP is assessed in this section through a series of simulated experiments. Table 5 outlines the key simulation parameters used to assess the power distribution approach in the mm-wave AB-HP. These parameters, including bandwidth, transmit power, and beam adjustment are essential for evaluating the effectiveness of DL-based techniques. We evaluate the effectiveness of DL-based techniques and conventional beamspace-channel estimation algorithms in mixed-ADC architecture. In particular, for the RBT-DRL it is necessary to ascertain beforehand how many beams to track during time beam training using the value of \(V_{n}\). The most straightforward beam training technique is to reduce the overall beam training overhead while preserving sufficient performance. There is an impact of the different actor learning rates, while the critic's learning rate is predetermined. The learning rate has a significant impact on the stability and speed of the SE during the RBT-DRL algorithm training. This is so that the convergence of SE can be implemented, and the learning rate represents the learning step. Missing the global optimum during the training process is more likely if the learning rate is high. Conversely, a poor learning rate would likely cause the convergence rate to slow down. We can also observe from Figure 4 that the SE realized by RBT-DRL can converge to a higher and more \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth & 80 MHz \\ Transmit power \(\mathcal{P}\) & 20 dBm \\ Adjusting beam & 3 \\ CPU clock speed mobile edge server & \(1.2\times 10^{12}\) Hz \\ Batch size & 64 \\ Time slot duration \(\tau\) & 0.05 \\ Noise figure & 0.1 \\ Velocity of UEs & 0.5 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation parameters. stable value sooner when the RBT-DRL for beam training \(V_{n}\) delivers after the agents are trained to be stable. The average SE attained by various beam training procedures for varying factor \(\delta\) is displayed in Figure 5. In the DRL, RBT-DRL, and the reward technique, whose efficacy increases with increasing \(\delta\), similar patterns are observed, wherein the more expensive beam training approaches will be activated more frequently by the RBT-DRL, DRL, and reward to raise the SE as it rises. The range of achievable SE values is 15.9-18.7 bit/s/Hz. The DRL model can be adjusted to achieve, in that order, 91.7%, 93.4%, and 95.9% of the maximum SE within this range. The typical quantity of RF chains is needed for RET-DRL. More RF chains are employed to produce larger SE when there is no power constraint, particularly for \(\delta\geq 0.5\). RBT-DRL activates less RF chains than DRL when the SE is less weighted in the reward function in (9). Figure 4: Related SE to a number of iterations. Figure 5: SE versus factor. From Figure 6, the lowest EE is around 49 Mbit/Joule, which is attained without any beam training, while the maximum attainable EE is approximately 73.5 Mbit/Joule, which is reached by rigorous beam training. Achieving a greater EE becomes more crucial as \(\alpha\) increases, while the impact of the beam training overhead decreases. Therefore, in training, a higher EE with more beams is obtained as \(\delta\) grows. The three distinct beam training techniques--RBT-DRL, DRL, and conventional beam training--are depicted in Figure 7. All three approaches' SE rises in tandem with SNR, indicating the anticipated increase in data throughput that comes with improved signal quality. The final SE attained and the pace of the rise, however, differs between the approaches. Throughout the whole SNR range, RBT-DRL continuously shows the highest SE, indicating its superior capacity to use increased SNR for enhanced SE. Although the difference is not as noticeable as it is with RBT-DRL, DRL likewise exhibits a larger SE when compared to regular beam training.