# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 6 Performance Evaluation  
### Evaluation Scenario  
Before going to the performance analysis of the proposed coexistence schemes we start by defining and describing the evaluation scenario. In [38], 3 GPP proposed evaluation scenarios that can be used to evaluate the coexistence of WiFi and LTE-U networks. In our case, we use the indoor (Hotspot) scenario which is one of the proposed evaluation scenarios. Figure 2 presents the 3 rd generation partnership project (3 GPP) indoor scenario considered in this work which is composed of two operators, called operator A (LTE-U) and operator B (WiFi). Each operator deploys four small cells operating in a rectangular room with dimensions of \(50\times 120\) m. The room has no walls and the four base stations of each operator are equally spaced in the X-axis. Each cell is connected to \(n\) UE/stations, where \(n\) is randomly selected between 1 and 5. The UE/stations are randomly located within the room. The distance between base stations from different operators is 5 m while the distance between base stations of the same operator is 25 m.  
We implement and analyze the proposed coexistence schemes using the ns-3 simulator as it is one of the prominent simulators to model wireless networks. The simulator is also convenient to model coexistence scheme as it can be used to generate dynamic traffic loads of LTE-U and WiFi networks. Hence, we use ns-3 version specifically released with LTE/WiFi coexistence implementations [39]. This ns-3 simulator release is used to model the LTE-U and WiFi networks and the proposed coexistence scheme. The simulation parameters used to model the co-located technologies are shown in Table 3. During the evaluation and testing of the proposed solutions, each LTE UE or WiFi station generates a random offered User Datagram Protocol (UDP) data rate. The UDP data rates are randomly picked from [50 kbps, 500 kbps, 1 Mbps, 2 Mbps, 4 Mbps). Once a certain UDP data rate is selected by a UE, the specific load will be held constant for some specific duration \(T_{D}\) before it is updated by another new offered data rate. The duration time where the offered data rate remains constant is randomly picked from an interval between 5-15 s. In this way, there will be from 20 to 60 changes in data rate (total simulation duration divided by minimum and maximum possible values of \(T_{D}\)) in a total of 300 s simulation time. These values used for simulation of the dynamic traffic are adopted from [13]. The considered network uses a constant speed propagation delay model named as _ConstantSpeedPropagationDelayModel_ and a propagation loss model named _ITU InH_[40; 41]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth (MHz) & 20 \\ \hline Carrier frequency (MHz) & 5180 \\ \hline Packet size (bytes) & 1500 \\ \hline Traffic model & UDP \\ \hline UDP rate & [50 kbps, 500 kbps,1 Mbps, 2 Mbps, 4 Mbps] \\ \hline MIMO & NA \\ \hline Pathloss, shadowing and fading & ITU InH \\ \hline Tx Power & 18 dBm \\ \hline BS/AP Antenna gain & 5 dBi \\ \hline UE antenna gain & 0 dBi \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Common parameters used to model co-located IEEE802.11n and LTE networks [38]. Figure 2: Indoor network scenario based on 3 GPP specification [38]. In the coexistence schemes, the IEEE802.11n mode is used to model the WiFi network. Packet size of 1500 bytes, minimum contention window of 15, and maximum contention window of 1023 are used for the WiFi network. Furthermore, slot time, Short Inter-Frame Spacing (SIFS), and DCF Inter-Frame Spacing (DIFS) are 9 \(\upmu\)s, 16 \(\upmu\)s, and 34 \(\upmu\)s, respectively. A simulation scenario modeled in ns-3 can be integrated with the RL models using the framework described in [42]. In our simulations, this approach is used to integrate the proposed RL models with the ns-3 simulator. The ns-3 simulator is used as an environment that generates dynamic simulation scenarios reflecting the dynamic traffic load variations in the LTE-U and WiFi network models.