## 4 Results
### Scenario Description  
The considered scenario is a square area of 500 m \(\times\) 500 m, composed by four 5G NR cells and two LTE cells. As explained in Section 2.1, \(C_{m,n}\) denotes the \(n\)-th cell of the \(m\)-th RAT, being \(m=1\) for LTE and \(m=2\) for 5G NR. Therefore, the LTE cells are identified as C\({}_{1,1}\) and C\({}_{1,2}\) and the 5G NR cells are C\({}_{2,1}\), C\({}_{2,2}\), C\({}_{2,3}\), C\({}_{2,4}\). The relevant parameters of the cells are presented in Table 1. Figure 2 illustrates the position of the different cells. The positions of the LTE and 5G NR cells were selected to illustrate a scenario in which 4 5G NR microcells are deployed in a denser area associated with a traffic hotspot, complementing the deployment of 2 LTE macrocells for wider coverage footprint.  
The scenario assumes a non-homogeneous traffic distribution with UEs that support MC and other UEs that generate additional background traffic. The UEs that support MC follow specific trajectories moving at 1m/s along the scenario and have an active session during the whole simulation duration with a required bit rate \(R_{u}=50\) Mb/s. These UEs can connect to up to \(N_{max}=2\) cells from any of the two RATs.  
The background traffic generation assumes Poisson session arrivals with aggregate generation rate 0.6 sessions/s and exponentially distributed session duration with average 120 s. As a result, the number of background UEs vary randomly during a simulation and the average number is 0.6\(\cdot\)120 = 72 background UEs. A background UE remains static during a session. Fifty percent of the background UEs are randomly located inside a square hotspot of 250 m \(\times\) 250 m centered at the middle of the scenario (see Figure 2). The rest of background UEs are randomly distributed in the whole scenario. Background UEsconnect to the RAT/cell with the highest Signal to Interference and Noise Ratio (SINR). To capture the different bit rates achievable in the two technologies, when a background UE is connected to LTE, its serving cell allocates the needed resource blocks to achieve a bit rate of 2.5 Mb/s, and when it is connected to 5G NR, the allocation is to achieve a bit rate of 40 Mb/s.  
The DQN model was developed in Python using the _TF-agents_ library [32]. The DQN model parameters are detailed in Table 2. They were selected after conducting different tests of the algorithm with different configurations, then choosing a suitable configuration with satisfactory behavior in terms of, for example, reward performance, convergence, and stability. The presented results correspond to the performance obtained by the DQN algorithm with the MC configuration policy learnt by the DQN agent after a total of 1E6 policy updates according to the procedure of Section 3.2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Cell Configuration Parameters**} \\ \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \hline Type of RAT & LTE & 5G NR \\ \hline Cells position [x, y] m & [62; 250] & [187; 125][187; 375] \\  & [437; 250] & [312; 125][312; 375] \\ \hline Frequency & 2100 MHz & 26 GHz \\ \hline Subcarrier separation & 15 kHz & 60 kHz \\ \hline Nominal channel bandwidth & 20 MHz & 50 MHz \\ \hline Number of available PRBs & 100 & 66 \\ \hline Base station transmitted power & 49 dBm & 21 dBm \\ \hline Base station antenna gain & 5 dB & 26 dB \\ \hline Base station height & 25 m & 10 m \\ \hline UE antenna gain & 5 dB & 10 dB \\ \hline Overload threshold \(\rho_{max}\) & 0.95 & 0.95 \\ \hline UE noise figure & \multicolumn{2}{c}{9 dB} \\ \hline UE height & \multicolumn{2}{c}{1.5 m} \\ \hline Path loss model & Urban Macrocell (UMa) model of Section & Urban Microcell (UMi) model of Section \\  & 7.4 of [33] & 7.4 of [33] \\ \hline \(w_{max}\) & 95.04 MHz (corresponds to the case when MC is done with 2 cells of 5G NR) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cell configuration parameters.  
Figure 2: Graphic representation of the scenario used for training/evaluation.