# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 2 Related Works  
RAN sharing has been studied mainly from two perspectives: (i) resource allocation and (ii) resource optimization. The former case could be to meet the service level agreement (SLA) requirements of different operators, while the latter is to improve on the network KPIs of interest. Ref. [12] was one of the first few works to lay the foundation for the concept of RANaaS and how cloud technology could be leveraged to achieve a flexible RAN. In [13], the energy-efficiency aspects of RANaaS are highlighted. In [14], the authors analyzed the benefits of employing network slicing in the RAN to introduce more flexibility in the configuration of RAN-sharing architectures. There, specific radio slices were allocated to an operator based on their SLA constraints. Figure 6: Coordination functionality required in a TowerCo RANaaS offering to independent MNO logical RANs. For a separate RAN, the indicated AI functionalities are offered by vendor-specific platforms. Figure 7: TowerCo function for RAN coordination introduction. Previous works on energy-efficient networks have mainly considered radio resource adaptation and sleep modes independently. In [15], the authors showed that mMIMO adaptation could provide energy savings of up to 40 percent compared to a fixed antenna system under varying traffic loads. In the same work, the authors also presented a base station power model. In [16], the authors proposed a multistream carrier aggregation algorithm to maximize the network capacity while considering the aspects of fairness and network energy efficiency. Sleep modes reduce the power consumption of a base station by turning off various hardware components during the idle period. The papers [6, 7, 8, 9, 10, 17, 18] focused specifically on sleep-mode management under varying loads. In [6, 8, 9], the authors proposed a reinforcement-learning-based algorithm to manage and determine the appropriate sleep mode depending on the user requests arrival rate. They obtained significant energy savings at low loads at the expense of a slight degradation in user throughput and latency. However, that technique has drawbacks, as the energy cost associated with turning ON/OFF base station components cannot be overlooked. To minimize the switching cost associated with the transitions, the authors in [19] proposed an algorithm to help determine how often and to which sleep level a base station should switch down. In [17], the authors proposed a distributed energy-saving algorithm that determined the sequence in which base stations can be turned off while ensuring a minimal impact on the network energy efficiency due to the additional load on the neighboring base stations. In [18], the authors addressed the limitations of a Q-learning-based sleep mode management algorithm by proposing a deep-Q learning-based solution that adapted better to the non-stationary nature of real-world traffic scenarios. In [20], we extended the analytical methodology developed in [21] to propose a dynamic Q-learning-based resource adaptation algorithm to obtain higher energy savings under varying traffic loads. As an extension to [20], in [22], we mainly addressed the challenges associated with complex long-horizon problems by developing a hierarchical reinforcement learning solution wherein different optimization strategies were implemented as a hierarchy of reinforcement learning agents. It was not only to improve the network energy efficiency but also to learn the best way to optimize the network in any given scenario. To an extent, we were able to show that different agents with different objectives and following different policies can coexist and coordinate with each other in the same environment to achieve a common goal [23]. Table 1 provides a summary of related works from 2020 to 2023.