# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 7 Conclusions and Future Work  
RAN sharing has a great potential to reduce both OPEX and CAPEX for the network operators, freeing up valuable resources that can be used to acquire new services and improve user experience. In this work, we analyzed how a RANaaS RAN-sharing approach affected the energy consumption of network operators under varying throughput degradation limits. Additionally, we explored how AI could be integrated into the optimization process in order to assist operators with resource optimization. Furthermore, we evaluated the impact of different operator requirements on the optimization process, such as the energy-saving probability and absolute energy savings from an operator and TowerCo's perspective [22]. We showed that an AI-driven network optimization could allow an MNO to scale its resources according to traffic demands, resulting in average energy savings of 15-20 percent; however, these benefits tended to saturate when densifying the network for higher traffic demands. This was where RAN sharing could provide significant additional value: up to 50 percent more energy savings over 24 h by sharing active/idle-mode power consumption (see Figure 24). From the analysis of our results, it is evident that network operators' requirements have an important influence not only on the optimization process, energy-saving potential, and absolute energy savings but also on selecting the right RAN-sharing solution. Deciding on the type of RAN sharing is a complex task that depends on:  
1. The operator's preferences and needs;
2. The level of autonomy and control desired by the operators over their resources;
3. The data shared with TowerCo service providers;
4. How often data should be exchanged in the case of RAN services for achieving reasonable gains from a centralized network optimization;
5. Whether or not there is any pricing difference for both RAN-sharing options. Deciding between these two approaches--RANaaS and RAN services--requires careful consideration of all the relevant aspects, most importantly, the security and privacy of the data shared with the TowerCo service provider. In both the RAN sharing scenarios, the responsibility of data security lies with the TowerCo service provider. Furthermore, the level of security depends on the type of data and services that an operator would like to activate, and the TowerCo should guarantee that there is no information leakage. As for the control, the motivation for a more centralized control comes from the fact that slower energy-saving features, such as carrier adaptation, need more coordination between MNOs compared to faster energy-saving features such as antenna or bandwidth adaptation (see Sections 6.3.4 and 6.3.3). Ensuring that the TowerCo has the ability to enforce a certain level of coordination could enable it to achieve its own targets, if any, thereby bringing balance to negotiations and discussions which have historically favored the MNOs, while providing commercial/business incentives for TowerCos to continue operating and provide services beyond standard functions. Not only does this enable the MNOs to optimize their share of energy consumption through resource optimization, but it also provides an opportunity for TowerCos to increase profits by providing some high-level coordination based on source and cost of energy. Moreover, it can further allow them access to advanced analytics capabilities in order to improve network performance metrics such as latency, throughput, reliability, and overall energy savings. Overall, it is a win-win scenario for both the operators and the TowerCo service provider. There are several ways this work can be expanded in future studies, including dynamic RAN-sharing scenarios where a TowerCo serves operators who have opted for either approach; carrying out a deeper analysis of RAN-sharing scenarios where both  
Figure 24: Average energy savings from different activations.