# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications
### Channel Estimation  
First of all, accurate CSI estimation is of paramount importance to realize performance gain with joint designs of UAVs and IRSs [18; 22; 30; 42]. In general, the accuracy of the channel estimation can be improved at the cost of increased training overhead and power consumption, especially when the IRS is large [101]. Therefore, it is worth pursuing ways to improve the accuracy of channel estimation, while minimizing the training overhead and power consumption at the same time.  
Wireless channels are affected by many factors, such as fading, scattering, and shadowing. In IRS-assisted UAV communications systems, various external parameters will also affect the channel because UAVs fly in the air [102]. Typically, the direct link between the UAV and the user, the number of elements in the IRS, the location of the IRS, and the material on the metasurface affect the effective wireless channel [22]. Furthermore, UAV mobility in real-world scenarios complicates channel modeling. Jittering, as well as the movement of UAVs can cause errors in location estimation and channel estimation [103]. Therefore, a thorough evaluation and study of the channel model should precede to identify and understand the effect of various factors in various application scenarios. Furthermore, studies on channel estimation algorithms robust to external factors will be necessary.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Reference** & **Objective** & **Concerned Variables** &
\begin{tabular}{l} **Machine Learning** \\ **Technique** \\ \end{tabular} \\ \hline
[65] &
\begin{tabular}{l} Minimum decoding error \\ probability of the worst-case \\ user and optimize the \\ resource allocation \\ \end{tabular} & UAVs deployment & DNN \\ \hline
[99] & \
\begin{tabular}{l} Improvement of channel \\ estimation performance \\ \end{tabular} &
\begin{tabular}{l} UAV trajectory and speed, \\ IRS phase shift, \\ movement of users, \\ propagation delay, \\ time delay \\ \end{tabular} & DNN \\ \hline
[100] & \
\begin{tabular}{l} Dynamic resource block \\ allocation \\ \end{tabular} &
\begin{tabular}{l} UAV location, \\ IRS phase shift, \\ RB allocation \\ \end{tabular} & Cooperative DNN \\ \hline
[69] & \
\begin{tabular}{l} Minimize the energy \\ consumption of UAV \\ \end{tabular} &
\begin{tabular}{l} UAV trajectory, \\ IRS phase shift, \\ power allocation policy \\ \end{tabular} & D-DQN \\ \hline
[58] & \
\begin{tabular}{l} Maximize the average sum-rate \\ \end{tabular} &
\begin{tabular}{l} UAV trajectory, \\ IRS configuration, \\ power control \\ \end{tabular} & DRL \\ \hline
[66] & \
\begin{tabular}{l} Maximize the total throughput \\ \end{tabular} &
\begin{tabular}{l} UAV trajectory, \\ transmit power control \\ \end{tabular} & MADRL \\ \hline
[84] & \
\begin{tabular}{l} Maximize the sum secrecy rate \\ of all legitimate users \\ \end{tabular} &
\begin{tabular}{l} UAV trajectory, \\ UAV active beamforming, \\ coefficients of the IRS elements \\ \end{tabular} & Twin-DDPG DRL \\ \hline
[89] & \
\begin{tabular}{l} Minimizing the expected \\ sum AoI \\ \end{tabular} &
\begin{tabular}{l} UAV altitude, \\ IRS phase shift, \\ communication patterns \\ \end{tabular} & DRL-based PPO \\ \hline
[77] & \
\begin{tabular}{l} Minimize the total \\ transmit power \\ \end{tabular} &
\begin{tabular}{l} UAV trajectory, \\ IRS phase shift, \\ subcarrier allocation for MBS \\ \end{tabular} & Dueling deep \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main features of the previous works based on ML.