# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 1 Introduction  
The energy consumption of information and communication technology (ICT) has triggered a lot of discussions in recent times, and there has been a growing concern to keep it within sustainable limits. As of 2020, ICT contributed to seven percent of the total global electricity usage and is expected to increase for several years as data traffic volumes continue to grow [1]. It is challenging to make accurate predictions due to the various entities and factors involved, their contribution being unpredictable. For instance, new trends such as cryptocurrency mining, the relentless speed of data center construction, blockchain, artificial intelligence, virtual reality, and augmented reality have not been factored into recent energy consumption studies but could potentially end up as the highest contributors [1]. To analyze these different trends and contributors, authors in [2] divided the electricity usage by ICT into four categories: _(1)_ consumer devices; (2) network infrastructure; (3) data center computation and storage; _(4)_ the production of the above categories (see Figure 1). Of these categories, our focus is on the network infrastructure--in particular, the base station's energy consumption, which accounts for 15 percent of the total ICT energy consumption [3].  
The fifth-generation (5G) mobile networks have been designed to cater to higher traffic demands, more intelligent terminals, and an ever-growing capacity and service-aware demand, while consuming the same or less energy. While densifying the network could address the capacity requirements, it could lead to increased network infrastructure costsand energy consumption by up to 2-3 times for operators, as 5G is rolled out alongside existing technologies. This has a direct impact on operational expenditure (OPEX), which continues to grow as networks evolve and expand. In line with the European Commission's 2030 climate and energy framework, there is a pressing need for a higher energy efficiency of 5G networks to reduce global greenhouse gas (GHG) emissions by 50 percent by 2030. To this end, mobile operators are beginning to use renewable energy sources that meet or exceed decarbonization goals, and vendors play their role by developing flexible and energy-efficient solutions [4].  
A typical network experiences large variations in traffic demand in a day. As shown in [5], the demand during peak hours could be around 8-10 times higher as compared to the off-peak hours. Moreover, since networks are designed to cater to the peak demands, large variations could lead to the under-utilization of base station resources and a higher energy consumption during the off-peak hours. To mitigate this problem, one can turn off the base station's hardware components depending on their activation-deactivation and idle time--a technique known as advanced sleep modes (ASMs). This has been looked into in [6; 7; 8; 9; 10]. The selection of the most suitable sleep mode that fulfills both the quality of service and energy-saving requirements is a complex task, as it depends on their activation-deactivation time, the network conditions, and the variation in the energy consumption of a base station with the radio resource used. To this end, reinforcement learning (RL) has proven to be a useful assistance tool for radio resource management.  
While these solutions improve the network energy efficiency and reduce the OPEX for the operators, there is still room for savings in their capital expenditure (CAPEX). This is where tower companies (or TowerCos) can play a crucial role.