# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 4 Performance Evaluation
### Performance of Trained Policies  
To assess the performance of the trained policies, the offered load evolution during one day depicted in Figure 6 has been considered in the scenario in Section 4.1 with \(N=4\) initial cells, which is then extended to \(N^{\prime}=5\) cells. For both scenarios, the figure includes the evolution of the offered load per tenant in one cell, denoted as \(O_{1}\) and \(O_{2}\) for Tenant 1 and Tenant 2, respectively, as well as the aggregated offered load of both tenants in the cell, denoted as \(O\). The scenarios with \(N=4\) cells and \(N^{\prime}=5\) cells are represented by straight and dotted lines, respectively. The considered offered loads per tenant show a complementary behavior, i.e., while the offered load values of Tenant 1 are larger during the middle of the day, larger values of Tenant 2 are experienced during the night. It is worth highlighting that for \(N=4\) cells, the aggregated offered load of both tenants is close to or even greater than 1 reflecting that there is not enough cell capacity to serve the required bit rate of the two tenants. Instead, for the new scenario with \(N^{\prime}=5\) cells, the aggregate offered load is at most 0.9, meaning that there is no overload.  
For the case of the \(N^{\prime}=5\) cells, the policies obtained after convergence as a result of the \(TRL\) and _Non-TRL_ modes have been applied to the offered loads of both tenants. Figure 7 shows the assigned capacity to each tenant in the cell, denoted as \(A_{1}\) and \(A_{2}\) for tenants 1 and 2, respectively, by the policies obtained with each re-training mode against the offered loads of each of the tenants in the cell. The results show that the policies obtained with both re-training modes execute a similar capacity assignment in which the offered loads are satisfied during most of the day without overprovisioning. In fact, the SLA satisfaction ratio, computed as the average ratio between the throughput of a tenant and the minimum of \(SAGBR_{k}\) and the tenant's offered load, is 96% for the policy trained with the _Non-TRL_ mode and 95% with the \(TRL\) mode, reflecting very similar performance. These results reveal that the behavior of the policies trained with the proposed TRL approach is similar to the one obtained without TRL but with the benefit of a much smaller training duration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Cells After the New**} & **Training Duration (Training Steps)** & \multirow{2}{*}{**Training Duration**} \\ \cline{2-2}
**Cell Deployment (N\({}^{\prime}\))** & & _Non-TRL Mode_ & & **Reduction** \\ \hline
2 & \(178\times 10^{3}\) & \(88\times 10^{3}\) & 51\% \\ \hline
3 & \(169\times 10^{3}\) & \(92\times 10^{3}\) & 46\% \\ \hline
4 & \(251\times 10^{3}\) & \(124\times 10^{3}\) & 51\% \\ \hline
5 & \(265\times 10^{3}\) & \(123\times 10^{3}\) & 54\% \\ \hline
6 & \(950\times 10^{3}\) & \(195\times 10^{3}\) & 79\% \\ \hline
7 & \(2377\times 10^{3}\) & \(788\times 10^{3}\) & 67\% \\ \hline
8 & \(9700\times 10^{3}\) & \(2058\times 10^{3}\) & 79\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training duration for the different deployments.