# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Result Categories
#### 6.3.3 Impact of Operator Requirements on Energy-Saving Feature Activation and Savings  
The operator requirements (e.g., different TDLs) could influence the activation probability and the energy savings obtained from the various features. For this analysis, we focused on a single feature-carrier adaptation. Table 10 summarizes the impact of different TDLs on the activation probability and the energy savings obtained by an operator by turning off a different number of bands. We observed that a higher TDL did not necessarily lead to increased activation carrier adaptation. Rather, the probability of selecting carrier  
Figure 19: Impact of operator requirements on the probability of turning off a different number of bands. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**S.No** & **Parameter** & **Unit** & **S1** & **S2** & **S3** \\ \hline
1 & Total simulation instances & & 1000 & 1000 & 1000 \\ \hline  & Total simulation outcomes (from an operator’s perspective) falling under & & & & \\  & & \% & 77.7 & 94.59 & 83.35 \\
2 & \(=>\) Category 2 & \% & 19.10 & 1.76 & 12.80 \\  & \(=>\) Category 3 & \% & 0.36 & 1.76 & 1.23 \\  & \(=>\) Category 4 & \% & 2.84 & 1.86 & 2.60 \\ \hline  & Total simulation outcomes (TowerCo’s perspective) falling under & & & & \\
3 & \(=>\) Category 1 & \% & 39.0 & 84.8 & 64.2 \\  & \(=>\) Category 2 & \% & 60.9 & 15.2 & 35.6 \\  & \(=>\) Category 3 & \% & 0.1 & 0 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Share of outcomes for an operator and a TowerCo across three simulation scenarios S1, S2, and S3. adaptation remained constant at 25 percent across all scenarios. The lower preference (25 percent) for selecting carrier adaptation over bandwidth or antenna adaptation could be due to two reasons:  
1. The limitation imposed in terms of how often bands could be turned ON/OFF during an observation period. We set this limitation factor to 10, i.e., it took ten times longer to perform carrier adaptation as compared to adapting the bandwidth or antenna configuration. In other words, in an episode of 20 steps, a band's state could be changed at most twice. 2. The partial reassociation of connected users--turning off a band required moving the users connected to a different site and (or) band. However, in a stationary environment where all bands are uniformly loaded, it could be difficult to find a band at the same/different site that could accommodate additional users without degrading the KPIs of interest. In such a scenario, the carrier shutdown action is not executed fully, leading to a more unbalanced network. Such a network is more likely to degrade on the KPIs of interest. These factors rendered carrier adaptation to be a multilevel decision-making problem of identifying the carriers that could be turned off and reassociating the connected users. Across all scenarios, we found the probability of turning off just one band was 70 percent higher compared to turning off more bands. This was due to the two reasons mentioned earlier. Additionally, the increased network energy consumption due to user reassociation was not compensated by the energy saving obtained by turning off more bands. However, as described in Section 6.3.2 and seen in Table 10, having some room in the form of a higher TDL offered the AI system an opportunity to take this action and achieve up to 37 percent higher \(MNO_{1}\) simulation outcomes. From a TowerCo's perspective, long-term actions such as carrier shutdown can only be implemented if it has been selected by all the operators. This extent of overlap in decisions taken by the operators is the focus of the next section.