# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 5 Simulation and Results
### Results and Discussion  
We studied the performance of the utility per BS in a HetNet where we had 15 femto BSs and 5 macro BSs. To add an aspect of a dense environment, we opted to create base stations with limited resources relative to demand. Thus, the max number of UEs associated with uplink and downlink were \(q_{ul}=10\ q_{dl}=10\) for macro BSs and \(q_{ul}=5\ q_{dl}=5\) for femto BSs. As a result, Figure 2 proves that there was a considerable improvement in terms of utility values per BS for our proposed algorithm compared to the following two cases:  
1. Traditional downlink (DL) handover where the UE is associated based on downlink received power. 2. Traditional DUDe handover where the UE choose two different BSs based on received signal strength for downlink and the distance between BS and the UE for the uplink. Despite the great improvement brought by the association with BS that offered the best downlink received power, it can be noticed that at a very high number of UEs the evolution of the curve begins to slow down, and at some point it can even be judged that it will begin to decrease. We can conclude that choosing a BS that offers the best downlink received power in neither case guarantees a better service, since at some point the problems of delay, interference, and congestion will appear with increasing demand. Nevertheless, our context-aware algorithm offers a considerable improvement, \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Channel bandwidth & 20 MHz \\ Macro BS radius & 500 m \\ Femto BS radius & 40 m \\ Network Element & 5 macro BSs \& 15 femto BSs \\ Max. macro BS transmit power & 46 dBm \\ Max. femto BS transmit power & 22 dBm \\ Max. UE transmit power & 20 dBm \\ Path-loss model & \(15+36\log_{10}(d)\) \\ Log-normal shadowing fading & 4 dB \\ Device mobility & 3 km/h \\ Device mobility direction & Random \\ Noise level & \(-121\) dBm \\ Overlapping region between macro BSs & 100 m \\ \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 2: The utilities rate for the BS regarding the number of UEs N, under the considered approaches: 5 macro BSs with \(q_{ul}=10\ q_{dl}=10\) and 15 femto BSs with \(q_{ul}=5\ q_{dl}=5\). DL: downlink. especially for the network which is experiencing high demand, which means that the improvement of the congestion will offer us the possibility to reduce the delay and to offer a better QoS for a large population. In terms of numerical results, we found that the DUDe context-aware handover algorithm could reach up to 35% over the traditional DUDe handover and 52% over the traditional downlink handover in terms of utility. In changing the number of \(N^{Req}\) UEs set, Table 4 exhibits the average delay in the handover procedure when the number of \(N^{Req}\) UEs set requests a handover at the same time as the \(UE_{k}\). Accordingly, we presume that the proposed scheme grants quick and stable convergence. We assessed the load distribution between the macro BS and femto BS in terms of the average rate of UE served by femto BS in the framework. Figure 3 demonstrates the behavior of the average rate of UE served by femto BS against the fluctuating UE number. Therefore, based on Figure 3, we can perceive that the average rate of femto BS serving UEs generally did not change with the expanded number of clients for all plans. Additionally, as should be obvious in this figure, around 66% of the clients were served by the femto BS in the traditional DUDe handover. Then again, only 10% were served by traditional downlink handover. Thus, with the proposed plan, the level of femto BSs serving UEs was approximately halved. Accordingly, considering that in our case there were three femto BSs per macro BS and the macro BS has large transmission power with improved system efficiency compared to the femto BS, this distribution can be considered as reasonable.