# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Comparison to Other Works  
In this section, we compare our work and other related works in the methodology, assumptions, and results obtained. In [12, 13], the authors looked into RAN sharing in different network deployments. Their idea of RAN sharing revolved around the virtualization of RAN functionalities, and its impact on different KPIs such as latency, throughput, and network energy consumption for a single operator. Contrarily, we assumed a fixed degree of virtualization and analyzed the impact of varying resource utilization by multiple operators on the distribution of the TowerCo's energy costs, the probability of fulfilling both the KPIs from a TowerCo's perspective, and the energy-saving features that overlapped had the most impact during different hours of the day. In simple words, the foundation of our work was on radio resource utilization, while the others focused on the virtualization of RAN functionalities.  
In [13], the authors focused on energy efficiency aspects and the benefits of RANaaS implementation in a heterogeneous network. They analyzed the variation in the packet  
Figure 23: Number of overlapped carrier adaptation actions and their impact on the TowerCo’s outcome category (20 percent TDL-S2). (Note: TC_x refers to TowerCo category x or \(TC_{x}\)).  
loss and the network power consumption with a progressive virtualization of RAN functionalities. They found that in the case of a single operator, an optimal virtualization solution could provide up to 17% higher energy efficiency compared to a random policy. Similarly, in [12], the authors analyzed the impact of the centralization of processing and management in RAN based on the service requirements and network characteristics in a homogeneous network.  
In a more recent work [14], the authors proposed a dynamic radio slice allocation to each operator based on their requirements and service level agreement (SLA) constraints. In particular, they analyzed the impact of dynamic spectrum allocation on the throughput and latency. It is in line with our RAN services approach described in Section 1.1.3, wherein the TowerCo manages and controls the resources allocated to the different operators. On evaluating the performance of their algorithm, the authors showed that it was possible to reduce the latency by 50 percent and improve their throughput by 3-5 times for operators with fewer spectral resources. As we studied a different RAN-sharing approach, we consider this work as a baseline for future studies on the RAN services scenario.  
A comparison of energy savings depends heavily on the assumption of the base station's power model, the network deployment scenarios, and the traffic demand. As a result, we are limited to [15, 20, 22] for energy savings, and [27] for the power model constants. While [6, 7, 8, 9] provided a good input on the management of ASMs, they were not very comparable in terms of the energy-saving results due to their significantly lower sleep mode's power consumption values. The authors assumed a reduction of 80, 94, and 96 percent in the power consumption of the base station due to activating \(SM_{1}\), \(SM_{2}\), and \(SM_{3}\), respectively, in contrast to our assumption of 16, 31, and 50 percent for the same. As a result, their energy savings of 60 percent were significantly higher as compared to our values of 20 percent. This coupled with their very low traffic demands of 1 user/s/km\({}^{2}\) makes our work unique in this regard.