## 5 Performance Analysis  
This section presents simulation results comparing the proposed CNN-based power allocation method with three baselines: EPA, max-min power allocation, and a stage-wise CNN approach in which the CPU-to-AP power is fixed equally and only the AP-to-UE power allocation is learned. Performance is evaluated in terms of per-user SE and EE and visualized using CDF plots. These CDFs illustrate both average performance and distribution across users, allowing for a detailed evaluation of fairness, adaptivity, and energy usage.  
Figure 5 shows the CDF plot of the per-user SE. The CNN integrated method achieves the best overall performance, with a right-shifted curve compared to the other methods. A larger portion of users attain higher SE values, and the per-user average SE is also higher. However, the difference in SE between the two CNN-based methods is relatively small, as both effectively learn user-specific allocation patterns. The EPA method performs the worst, as it does not account for spatial or channel variability. The max-min method improves fairness but sacrifices overall throughput. The stage-wise CNN approach provides notable improvement over traditional baselines but is limited by its fixed CPU-to-AP allocation, which restricts full spatial optimization.  
Figure 5: CDF plot of per-user spectral efficiency for four power allocation methods.  
Figure 6 illustrates the CDF plot of the per-user EE. While SE and EE often trade off against each other, the CNN integrated model shows strong performance on both fronts. It achieves the most right-shifted EE curve, demonstrating that users can maintain high data rates with reduced power consumption. This is because the model learns to avoid unnecessary transmission by APs with poor link conditions, and allocates more power only where it is most effective. The stage-wise CNN model achieves similar SE but lower EE, as it cannot control how much total power is delivered to each AP. Even when some APs are underutilized, they still consume fixed power, reducing the overall EE. In contrast, the integrated model dynamically adjusts both AP-to-UE and CPU-to-AP allocations, enabling global energy savings without sacrificing throughput. EPA has the worst EE performance due to its indiscriminate power usage. Max-min guarantees minimum service levels, but is not energy-aware and often allocates excessive power to weak links.  
Table 4 summarizes the per-user average and standard deviation for SE and EE across all methods. The proposed CNN model outperforms all baselines in terms of average SE and EE while also achieving the lowest standard deviation, indicating improved fairness and consistency across users.  
These results clearly demonstrate that although both CNN-based methods perform similarly in terms of SE, the CNN integrated model offers a clear advantage in terms of EE. By jointly learning both levels of power allocation, it avoids wasteful power assignments and adapts better to user and channel conditions across the network. The ability to optimize  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Avg SE (bps/Hz)** & **Std SE** & **Avg EE (bits/J)** & **Std EE** \\ \hline Equal Power & 2.93 & 0.67 & 0.042 & 0.009 \\ Max-Min & 3.58 & 0.54 & 0.050 & 0.007 \\ Stage-wise CNN & 4.06 & 0.49 & 0.061 & 0.005 \\ Proposed CNN & 4.28 & 0.46 & 0.075 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Per-user average and standard deviation of SE and EE.  
Figure 6: CDF plot of per-user energy efficiency for four power allocation methods.  
energy usage without degrading throughput confirms the effectiveness of the integrated approach, particularly in scenarios where EE is critical.