# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 6 Existing Methods and Proposed Solution
### Proposed Solution  
The primary objective of the optimization problem is to maximise the sum of \(\delta^{i}[t]\) for all \(i\) in the set \(\{1,2,\ldots,L\}\), which represents the gNBs. In mathematical terms, the objective function is  
\[\underset{P_{\text{TX},i}[t],\;\forall i}{\text{maximise}}\sum_{i \in\{1,2,\ldots,L\}}\delta^{i}[t]\] \[\text{if}\;P_{\text{TX},i}[t]\in\mathcal{P},\quad\forall i,\]  
\[\delta^{i}[t]\geq\delta_{\text{target}}. \tag{8}\]  
Here, \(P_{\text{TX},i}[t]\) represents the transmission power for the \(i\)th gNB at time \(t\). The objective is to find the values of\(P_{\text{TX},i}[t]\) that maximise the sum of \(\delta^{i}[t]\). The optimization variables are \(P_{\text{TX},i}[t]\) for all \(i\) gNBs. These variables represent the transmission power levels for different gNBs at a given time \(t\). The goal is to find the optimal values for these transmission power levels. To ensure that the optimization remains feasible and practical, there is a constraint on the transmission power levels. The transmission power \(P_{\text{TX},i}[t]\) for each gNB \(i\) must be chosen from a predefined set \(\mathcal{P}\). The second constraint is imposed on the values \(\delta^{i}[t]\). Each \(\delta^{i}[t]\) must be greater than or equal to a predefined minimum threshold value \(\delta_{\text{target}}\). The \(\delta\) values at each gNB \(\mathcal{B}\) come from the UAV measurement reports. The \(i\)-th gNB tries to solve this problem and, as a result, finds the optimal value for \(P_{\text{TX},j}\). The proposed DQL algorithm solves (8) at a central cloud location. Algorithm 1 is based on the DQL approach. It enables power control without the need for the UAV to transmit explicit power control commands, leading to lower computational overhead compared to tabular Q-learning for a given number of states and network depth of the DQN network [31]. The flow diagram for the proposed Algorithm 1 is shown in Figure 10. The main steps can be summarised as follows:  
1. Choose an optimising action. 2. Choose a power control action. 3. Evaluate the impact on effective SINR \(\delta^{l}_{eff}[t]\). 4. The action is rewarded based on the distance from \(\delta_{\text{target}}\) or \(\delta_{min}\); for instance, a higher reward could be given to an agent that is in closer proximity to the \(\delta_{\text{target}}\), while a lower reward would be given to an agent that is closer to the \(\delta_{min}\). 5. The DQN is trained by utilising the produced outcomes. The reported SINR from the UAVs is fed as input to the DQL algorithm. Firstly, the current states are observed. The algorithm starts with a higher value of \(\epsilon\), where the system selects random actions and learns about the best rewards. As it progresses, the \(\epsilon\) value decays and the system keeps choosing the action with the highest reward. After a reward is received, the effective SINR \(\delta_{\text{eff}}\) is calculated and checked against the minimum SINR \(\delta_{\text{min}}\). If \(\delta_{\text{eff}}<\delta_{\text{min}}\), then the episode is aborted; otherwise, the next state is observed and stored in the experience buffer \(\mathcal{D}\). From \(\mathcal{D}\), the data are minibatched and SGD is performed on it. The results are used to update the DQN and the states are updated to \(s^{\prime}\). The minibatching and the consequent updating of states via back-propagation occur within the neural network as shown earlier in Figure 9. Finally, it is checked if \(\delta_{\text{eff}}\geq\delta_{\text{target}}\), where \(\delta_{\text{target}}\) is the target SINR. If it is, then the power control sequence commands are generated, or else the algorithm repeats until the target SINR is achieved.