# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Simulation Setup  
The network, system, and channel models have been detailed in prior descriptions. The UAVs are in motion at a velocity of 'v' and are subject to the impact of log-normal shadow fading and small-scale fading on their movement. The radius of the cell is denoted as 'r', and the inter-site distance is established as R = 1.5r. The channel conditions of the UAVs are influenced by a probability of LoS denoted as \(p_{\text{LOS}}\). The remaining parameters are presented in Table 4. The target effective SINRs for the objective are specified as  
\[\delta_{\text{target}}:= 3\,\text{dB},\] \[\delta_{\text{target}}^{\text{thresh}}:= \delta_{0}^{\text{thresh}}+10\log M\,\text{dB} \tag{17}\]  
where \(\delta_{0}^{thresh}\) is the established SINR. If the \(\delta_{0}^{thresh}\) drops below a minimum of \(-3\) dB, the episode is considered to have concluded and the session cannot proceed any further. The hyperparameters used to tune the RL-based model are shown in Table 4. Table 6 summarises the radio parameters. The simulated states \(\mathcal{S}\) are set up as  
\[(s_{i}^{0,\,1}):= \text{UAV}_{\ell}(x[t],y[t]),\,(s_{t}^{2},s_{t}^{3}):=\text{UAV}_{b} (x[t],y[t]),\] \[s_{t}^{4}:= \text{P}_{\text{TX},\ell}[t],\quad s_{t}^{5}:=\text{P}_{\text{TX},b }[t],\] \[s_{t}^{6}:= \text{h}_{n}^{\ell}[t],\quad s_{t}^{7}:=\text{h}_{n}^{b}[t], \tag{18}\]  
where (x,y) are the Cartesian coordinates (i.e., longitude and latitude) of the given UAV. By leveraging the cardinality of \(\mathcal{P}\), which scales in the order of 2, the set of actions \(\mathcal{A}\) can be derived. Specifically, a register \(a\) is utilised to facilitate a binary encoding of the available actions. With the aid of masks, bitwise-AND operations, and shifting, the power control commands can be obtained. The code segment as shown in Table 7 is adopted:  
When \(q=0\) :  
It can be inferred from above that \(\mathcal{P}=(\pm 1,\,\pm 3)\) dB offset from the transmitter power. The following factors affected the choice of these offset values:  
1. It conforms to the standards [46] that select integers for power value increments. 2. It maintains the non-convexity in (8) because it keeps the constraints discrete. Actions that increase or reduce gNB transmit powers are implemented as in [30]. A function \(p(\cdot)\) is defined that returns an element from \(\mathcal{P}\) depending on the selected code. In this work, it is considered that p(00) = -3, p(01) = -1, p(10) = 1, p(11) = 3. The received SINR resulting from the above actions can, therefore, be written as  
\[\delta_{\text{a}_{[0]},\text{a}_{[3]}}^{\text{b}}\text{for gNB b} \tag{19}\]  
\[\delta_{\text{a}_{[1]},\text{a}_{[2]}}^{\text{l}}\text{for gNB 1} \tag{20}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline gNB power \(P_{gNB}^{\text{max}}\) & 46 dBm \\ Cellular Geometry & Hexagonal \\ Antenna Gain (TX, thresh) & (11, 3) dBi \\ Probability of LOS (\(P_{\text{LOS}}\)-\(P_{\text{LOS}}^{\text{thresh}}\)) & (0.9, 0.8) [45] \\ Downlink Frequency & 4.7 GHz \\ Cell Radius r & 150 m \\ UAV Antenna Gain & 0 dBi \\ Inter-site distance R & 225 m \\ Number of Multipaths \(N_{p}\) & 15 \\ Average UAV Speed \(v\) & (10, 20, 20) m/s \\ Frame Duration & 20 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Radio environment parameters. \begin{table}
\begin{tabular}{l l} \hline \hline
**Code** & **Description** \\ \hline a[0, 1] = 00 & power is reduced by 3 dB for gNB \(b\) \\ a[0, 1] = 01 & power is reduced by 1 dB for gNB \(b\) \\ a[0, 1] = 10 & power is increased by 1 dB for gNB \(b\) \\ a[0, 1] = 11 & power is increased by 3 dB for gNB \(b\) \\ a[2, 3] = 00 & power is reduced by 3 dB for gNB \(l\) \\ a[2, 3] = 01 & power is reduced by 1 dB for gNB \(l\) \\ a[2, 3] = 10 & power is increased by 1 dB for gNB \(l\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Code for power control.