# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 4 System Model  
At the highest level, the system consists of four main entities: _(i)_ the TowerCo, _(ii)_ MNOs, _(iii)_ AI system, _(iv)_ shared/logical RAN resources, as seen in Figure 8. In this work, we consider three MNOs operating in a network consisting of a homogeneous deployment of seven three-sector sites, as seen in Figure 9. The serving base station \(i\) is surrounded by \(\phi_{c}\) interferers. To simplify the analysis, we assume a maximum of six interfering base stations, i.e., \(\phi_{c,max}=6\). Each base station serves \(N\) active users in time \(T\), distributed uniformly in the cell of radius \(D_{o}\). Assuming a fixed average requirement of \(\Omega\) megabits per user, the hourly traffic demand \(\zeta\) can be expressed as Equation (1). The variation in the number of active users during the day is assumed to follow the profile shown in Figure 10.  
\[\xi=N*\Omega \tag{1}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Papers** & **Area/Key Points** \\ \hline
2022 & [4, 22] & \(\bullet\) & [4]—Traffic forecasting, overview of network optimization strategies, other emerging technologies, intelligent network, site simplification, green 5G network, technical development trend of green 5G networks, ultra-wide band devices, multi-antenna RF technology \\
2022 & [4, 22] & \(\bullet\) & [22]—Network optimization, energy efficiency, multi-objective optimization, hierarchical reinforcement learning, network energy management, distributed learning, advanced sleep modes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Figure 8: TowerCo RANaaS simulation setup. Figure 10: Traffic demand profile over 24 h. (Note: In this plot, \(\alpha(t)\) represents the share of active subscribers at a given hour). Figure 9: Seven three-sector sites deployment scenario. Considering the downlink scenario and assuming perfect channel state information (CSI) to be available at the transceivers, the achievable rate per user \(k\) is expressed as Equation (2). Here, \(r_{k}\) depends on the signal power \(\mathcal{S}_{k,i}\) (Equation (3)) from the base station \(i\) that the user is connected to, and the interference from the neighboring base stations \(\mathcal{I}_{k}\) as in Equation (4). Furthermore, \(\mathcal{I}_{k}\) depends on the activity of the interferers \(\eta_{j}\) while \(r_{k}\) varies with the bandwidth \(B_{i}\) and the number of spatial multiplexing layers \(N_{k}\). These parameters can be expressed as  
\[r_{k}=B_{i}N_{k}\log_{2}\biggl{(}1+\frac{\mathcal{S}_{k,i}}{\mathcal{I}_{k}+ \mathcal{N}_{k}}\biggr{)} \tag{2}\]  
\[\mathcal{S}_{k,i}=\frac{c}{\left(D_{ik}\right)^{\alpha}}\frac{\left|\mathbf{H} _{ik}\mathbf{W}_{ik}\right|^{2}}{\left\|\mathbf{W}_{ik}\right\|^{2}}\frac{p_{i }M_{i}}{K_{c}}(M_{i}-N_{k}) \tag{3}\]  
\[\mathcal{I}_{k}=\sum_{j=1}^{\phi_{i}}\frac{c\eta_{j}}{\left(D_{ik}\right)^{ \alpha}}\frac{\left|\mathbf{H}_{jk}\mathbf{W}_{jk}\right|^{2}}{\left\|\mathbf{ W}_{jk}\right\|^{2}}\frac{p_{j}M_{j}}{K_{c}}\Bigl{(}M_{j}-N_{k}\Bigr{)} \tag{4}\]  
\[\mathcal{N}_{k}=\sigma^{2}\left\|\mathbf{W}_{jk}\right\|^{2} \tag{5}\]  
In Equation (3), \(\mathcal{S}_{k,i}\) depends on the number of users served simultaneously \(K_{c}\), the power per power amplifier (PA) \(p_{i}\), and the active array size \(M_{i}\). The array gain \((M_{i}-N_{k})\) increases the received signal power and is obtained using a larger antenna array at the base station. It is the maximum gain obtained by considering a zero-forcing precoder at the transmitter [26]. \(D_{ik}\) and \(D_{jk}\) are the distances of the user from the serving and interfering base stations, respectively. \(\alpha\) is the path loss exponent with a value of 2. \(c\) captures the gains of the antennas at the base station and user.