# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 5 Related Works
In 2018 and 2021 [47; 103], it was found that UAVs are especially vulnerable to LoS propagation, which is required for mm-wave communications to work. Use of mm-wave communications was suggested as a possible option. Larger path loss reduces inter-cell interference for mm-wave frequencies, while small antenna aperture size allows a large number of antennas to be used in the antenna array. Arrays can be employed to provide beamforming which compensates for the user's high path loss while simultaneously reducing interference. The application of mm-waves opens up a significant usage spectrum. The high throughput previously mentioned can be easily achieved by using a large bandwidth. In 2018, 2019, and 2020 [104; 105; 106], the authors used simulations to further investigate this problem, discovering two issues. The first issue is that high levels of interference will make it impossible to maintain connection and complete successful HOs, resulting in a high percentage of radio link and HO failures. The focus shifted to LTE-M, a technology that allows users to communicate in a low SINR. The authors were able to reduce the number of radio connection and HO failures by simply increasing the number of ping-pong HOs. The second issue is that the default HO strategy will fail when aerial users transmit antenna pattern nulls. The volume must be kept low to avoid a radio connection failure. Fine-tuning parameters of the HO mechanism, such as the reaction time, was suggested to solve this problem. The introduction of 5G networks will alter people's communication habits. Several tests were conducted with a UAV connected to a 5G BS at a frequency of sub-6 GHz. HO to the 4G network automatically occurred. The UAVs experienced more HOs than land users, lowering the overall throughput. Researchers believe this will be corrected with the deployment of more 5G BSs. In 2019-2020 [107; 108], the authors considered equipping UAVs with highly directional antennas. They suggested the use of 5G's massive MIMO capabilities since it allows the BS to geographically separate users while simultaneously producing nulls for other users to prevent interference. In 2021 [109], the authors mainly focused on static users, however, new issues emerge when mobile circumstances are considered. Beam training and tracking become more difficult, resulting in significant amounts of overhead. However, this overhead is lower than expected in the simulations, allowing mobile users to be serviced at standard rates. Another issue with using mm-waves is the large Doppler frequency changes that are proportional to the center frequency. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**No**} & **Author** & **Contribution** & **Limitations\(\backslash\)Research Areas** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{[PERSON]} & & To overcome the HO and Radio Resource Management & It concentrated on UAV as a user \\  & & & (H–RRM) problem, a deep reinforcement learning & while ignoring UAV implementation \\  & & & approach was developed [84]. & as fly BS. \\ \hline \multirow{6}{*}{2} & \multirow{6}{*}{[PERSON]} & A unique HO framework was offered to provide & \multirow{6}{*}{It did not address the inclusion of 3D UAV mobility in the present framework.} \\  & & & competent mobility support and a reliable wireless & \\ \cline{1-1}  & & & network to UAVs that are supported by a terrestrial & It did not address the inclusion of 3D cellular network. A deep Q-learning strategy was & UAV mobility in the present framework. \\ \cline{1-1}  & & & created to powerfully optimize HO decisions, ensuring & present framework. \\ \cline{1-1}  & & & robust network for UAV users using instruments & \\ \cline{1-1}  & & & from deep reinforcement learning [110]. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of previous contributions. \begin{table} \begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**No** & **Author** & **Contribution** & **Limitations\textbackslash{}**Research Areas** \\ \hline  
begin{tabular}{} \end{tabular}  &