## 7 Results and Analysis
### Discussion
#### 7.1.2 Energy-Latency Optimization  
Figure 11 illustrates that our framework achieves 32% energy savings while incurring only a 4.2% increase in latency--well below the 5% URLLC threshold. In contrast, standard Federated Averaging (FedAvg) [8] yields 25% savings at 9.1% latency, and centralized approaches only realize 18% savings at 6.7% latency. Moreover, as shown in Figure 12, our LSTM-based scheduler reduces DU wake-up latency by 58% (to 2.4 ms) compared to PID controllers [59]. These results confirm that predictive, workload-aware control can shift the energy-latency frontier beyond what reactive policies permit. The comprehensive evaluation of our LSTM-based scheduling approach highlights three key advantages that contribute to its superior performance under diverse network conditions. Firstly, the \begin{table}
\begin{tabular}{l l l l} \hline
**Scenario** & **Energy Savings (\%)** & **Latency Increase (\%)** & **Wake-Up Success (\%)** \\ \hline Normal Load & 32.0 \(\pm\) 1.2 & 4.2 \(\pm\) 0.8 & 98.5 \\ \hline High Load & 28.5 \(\pm\) 2.1 & 6.7 \(\pm\) 1.5 & 93.2 \\ \hline Bursty Traffic & 29.8 \(\pm\) 1.8 & 7.1 \(\pm\) 1.3 & 91.4 \\ \hline URLLC Slice & 26.4 \(\pm\) 1.5 & 3.9 \(\pm\) 0.7 & 99.1 \\ \hline \end{tabular}
\end{table} 
Table 19: Performance across network scenarios (95% confidence intervals). predictive adaptation capability enabled by the LSTM's temporal modeling allows for proactive Distributed Unit (DU) state transitions. This strength is particularly evident in Figure 19, where 95% of URLLC packets successfully maintained under 5 ms latency even during extreme traffic surges reaching 200%. Secondly, the scheduler demonstrates slice-aware optimization, as detailed in Table 19, by effectively enforcing slice-specific policies that preserve strict URLLC latency compliance--showing only a 3.9% increase--while simultaneously achieving 26.4% energy savings. Lastly, the approach exhibits robust load resilience, maintaining 83% of its normal-load efficiency under high-traffic scenarios with 80-100% PRB utilization, as shown in Figure 20, which outperforms traditional PID controllers by a significant margin of 21%. From an architectural perspective, the observed 1.8 s recovery time for URLLC traffic, as reported in Section 7.11, suggests that the proposed approach remains compatible with near-RT RIC control loop requirements, which typically range between 10 ms and 1 s. Despite these advantages, the evaluation also reveals three critical challenges that warrant further investigation. Mobility management emerges as a key concern, with vehicular mobility at speeds of 60 km/h introducing a 12% prediction error in DU wake-up timing due to rapid variations in channel state information. Furthermore, the analysis exposes an inherent energy-latency trade-off, evidenced by the 2.8% efficiency gap between eMBB and URLLC slices reported in Table 19, highlighting the need for dynamic energy budget allocation tailored to specific service classes. Finally, the LSTM scheduler exhibits a cold start problem, where an initial warm-up period of approximately five minutes is required for model stabilization, resulting in a reduced DU wake-up success rate of 84% during network initialization phases. **Future Directions:** The latency distributions presented in Figure 19 point to two promising avenues for future enhancement of the proposed scheduling framework. One potential improvement involves the integration of hybrid scheduling approaches, where the predictive capabilities of the LSTM model are combined with reinforcement learning techniques, particularly to enhance the handling of mMTC traffic bursts characterized by irregular and unpredictable patterns. Another enhancement focuses on implementing adaptive granularity in scheduling decisions by introducing multi-timescale control mechanisms.