# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Result Categories  
The results were analyzed from two perspectives: (i) TowerCo and (ii) MNO. From the TowerCo's perspective, we had three different outcome categories as listed in Table 6 depending on the number of KPIs fulfilled for each operator. Furthermore, the requirements of the MNOs played a crucial role in the energy savings obtained and the energy cost  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Replay memory size & 100,000 \\ Minibatch size & 64 \\ Learning rate & 0.001 \\ Discount factor (\(\gamma\)) & 0.9 \\ Optimizer & Stochastic gradient descent \\ Activation function & tanh/softmax \\ Loss function & Mean squared error \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reinforcement learning parameters.  
distribution. For the MNO, we had five different outcome categories, as seen in Table 7. The KPIs fulfilled depended on how well the AI system could reduce the network energy consumption without degrading the throughput beyond the permissible limit for each operator. Amongst these two, we selected the energy consumption to play a dominant role by having a higher importance factor, i.e., achieving energy savings had a higher priority over throughput degradation within permissible limits. That led to the possibility of obtaining outcomes where only one of the requirements was satisfied, as listed in Table 7. We also analyzed how the AI system optimized an MNO's share of resources under different TDLs. In the rest of the document, we refer to the TowerCo and MNO's outcome categories as \(TC_{x}\) and \(MNO_{x}\), respectively, where \(x\) is the category of outcome and \(x\in\{1,2,3,4,5\}\).  
In addition to the two perspectives presented above, we divided the results into three scenarios as seen in Table 8. This division was based on the throughput degradation limits for each operator. We also assumed that the operators had the same 24 h traffic demand profile as seen in Figure 10. In our analysis, we not only evaluated the benefits of RAN sharing from an MNO or TowerCo's perspective but also analyzed the scenarios which of the two RAN-sharing approaches (RANaaS and RAN services) could be more applicable and (or) beneficial.  
(i.e., the TowerCo's level). Based on the outcome for each MNO, the TowerCo's results could be categorized into three categories as listed in Table 6. From the TowerCo's perspective, we would have liked to have most of the simulation outcomes falling in \(TC_{1}\) (see Table 6), which would have meant more \(MNO_{1}\) simulation outcomes (see Table 7). However, due to the limitations of the AI system in learning the actual behavior of the environment, we ended up with outcomes in categories 2, 3, and 4 for an operator. During training, we tried to maximize both the reward obtained by the agents in each episode and the share of outcomes in categories 1 or 5. This approach was more applicable in our case as the maximum reward that could be obtained in a given scenario depended on the energy savings and throughput change. As these values were dependent on the network conditions and user distribution, we ended up in a scenario where similar actions under similar traffic demands could yield totally different rewards.