# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 5 Energy-Saving Features and AI-Assisted Radio Resource Management
### Implementation Flow  
In Figure 16, we illustrate the implementation flow of the RANaaS scenario consisting of four different entities, namely, _(i)_ the TowerCo's controller and energy manager, _(ii)_ MNOs, and _(iii)_ AI manager. The role of these entities is as follows:  
* **TowerCo's controller and energy manager:** The TowerCo's controller is responsible for allocating the share of resources to different MNOs, including the maximum bandwidth and antenna configuration that can be used on each band (step 1). We assumed equal and non-overlapping spectral resources to be available to all the MNOs as outlined in Table 4. Furthermore, the TowerCo's controller receives the energy cost per MNOs from the TowerCo's energy manager (step 7). The latter calculates the energy cost per MNO based on resources used by them at different sites in a network as described in Section 4.5 (step 5).
* **MNO:** The operator controls its share of resources and the KPIs it would like to optimize or improve. In this case, we assumed all operators would like to improve on both the energy efficiency and the network throughput. The only difference was the maximum throughput degradation that each operator could permit. To make this decision, they could consider the average traffic demands at different sites and the requirements of the connected users. Moreover, we assumed that the operators used the AI system described in Section 5 for managing their share of resources. Doing so required the operators to send their AI and KPI requirements to the AI manager (step 2).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Feature** & **Input to Agent** \\ \hline Overall activity factor & 1–4, 5 * \\ Channel conditions & (1–4)\({}^{\dagger}\), 5*\({}^{\ddagger}\) \\ Number of connected users & 1–4, 5*\({}^{\ddagger}\) \\ Number of users per band at each site & 1–4, 5*\({}^{\ddagger}\) \\ Average SINR & 1–4, 5*\({}^{\ddagger}\) \\ Carrier state at each site & 1–4, 5*\({}^{\ddagger}\) \\ Antenna configuration & 1–4, 5*\({}^{\ddagger}\) \\ Bandwidth utilized & 1–4, 5*\({}^{\ddagger}\) \\ Statistics of the user with lowest SINR & 1, 2 \\ Permissible throughput degradation & 1–5 \\ Current throughput degradation & 1–5 \\ Network energy consumption & 1–5 \\ \hline \hline \end{tabular}  
* *: values pertaining to a single site, \({}^{\ddagger}\): values pertaining to the three bands at a site. Note: unless explicitly stated, the values are separate for the three bands.  
\end{table}
Table 3: List of features.  
* **AI manager:** The role of the AI manager is to select an AI configuration based on the needs and requirements of the operator. In this case, it could depend on the desired energy-saving features, the KPIs to optimize, and the degradation restrictions or limitations on the throughput set forth by the operator. In this work, we assumed each operator to have its own AI system, as seen in Figure 15. The AI system/functionality shown in the Figure 13 assists the operator in optimizing their share of resources. Figure 16 shows it as separate AI agents connected to and controlled by each operator.