## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Problem Statement** & **Technique/Scheme Used** & **Improvement Observed** \\ \hline
[5] & An optimal ABS platform to maximize coverage by finding the optimum altitude & Analytical approach & Maximum coverage was achieved at optimal low altitude \\ \hline
[10] & A 3D placement of UAV-BS by decoupling vertical from horizontal dimensions to maximize the coverage of users by minimizing the transmit power & Optimal placement algorithm & Savings in transmit power and maximized coverage were achieved \\ \hline
[11] & Deployment of multiple UAVs having directional antennas and optimization of the altitude of UAVs to maximize the coverage area and lifetime of UAVs & Circle-packing theory & The optimum altitude can be obtained on the basis of the number of UAVs and beam width of directional antennas \\ \hline
[12] & UAV-aided cellular communication network against jamming & Reinforcement learning & A minimized bit error rate and energy saving for the cellular network \\ \hline
[13] & Single UAV to provide wireless coverage for indoor users when cellular network goes down & Gradient descent algorithm & A minimum transmit power with maximum path loss was obtained \\ \hline
[14] & Optimizing the height of a UAV to maximize coverage and minimizing outage probability & Decode and forward-relaying method & Maximum coverage with minimum outage was obtained by finding the optimum height of a UAV \\ \hline
[17] & A 3D deployment of UAV to improve throughput in overloaded and outage situations & Reinforcement learning & A maximum performance gain in terms of throughput was achieved \\ \hline
[18] & A 3D deployment of UAV to maximize revenue of the network & Bisection search algorithm & The maximized revenue of the network was achieved \\ \hline
[19] & Throughput maximization by adjusting the position of a UAV in software-defined disaster areas & Centralized algorithm & The throughput was improved by 26\% \\ \hline
[26] & Channel model of backhaul and delay-aware was taken into account to minimize delay by finding the optimum height of a UBS & Backhaul and delay-aware positioning of UBS & It was observed that the delay was less for low arrival rates and increased for high arrival rates \\ \hline
[27] & Optimal UAV placement to maximize the sum rate by using a minimum transmit power & Genetic algorithm & The optimal placement of UAV was achieved with minimum transmit power and minimum path loss \\ \hline
[21] & Deployment of a UAV equipped with intelligent reflecting surface (IRS) to maximize the sum rate by optimizing the power allocation of a base station (BS), phase shift of the intelligent reflecting surface (IRS), and horizontal position of the UAV & Deep reinforcement learning & An enhanced sum rate was obtained \\ \hline
[22] & Efficient placement of a UAV-BS serving as a relay node to maximize throughput & Equal power allocation method, water filling method and modified water filling method & Results showed that water filling method gives better results as compared to other two methods \\ \hline
[23] & Deployment of a UAV by optimizing its trajectory to maximize the mean opinion score (MOS) & Deep Q-learning & The maximized mean opinion score (MOS) was achieved \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related papers. Hence, RL was adopted to solve the problems of maximizing user association and sum rate. RL is the best option to optimize location of the ABS according to the environment and users' density. In RL, the agent (ABS) will optimize its location according to the requirements.