# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 3 Overview of Previous Studies on IRS-Assisted UAV Communications
### Improvement of Other Performance  
In addition to the above-mentioned performance improvements, various aspects of the performance the the IRS-assisted UAV system can be improved, including reliability enhancement and latency reduction. This subsection introduces a few such works, and Table 4 briefly summarizes the main features of the works.  
The studies in [40, 86] considered scenarios where an IRS-assisted UAV system supports strict constraints of ultra-reliable low-latency communications (URLLC), which is one of the 5G use cases. URLLC requires high reliability when transmitting short data packets between devices [91]. To overcome the interference of dense networks, the authors of [86] proposed using the zero-forcing and time division multiple access methods, where each UAV can serve multiple users in its own cluster. The authors proposed and validated an optimization framework (in terms of UAV deployment), power allocation in the macro BS (MBS), phase shift in the IRS, and block length for URLLC [40]. To minimize decoding errors for short packets, the author considered manual beamforming, which optimizes the block length and UAV location. It was shown that the reliability improves as the number of IRS elements increases and that the location of the UAVs is important to achieve high reliability for short data packets. Reference [87] analyzed how signal gain varies according to the IRS tilt angle in the IRS-assisted UAV communications system. It was shown that the IRS gain is maximized when the tilt angle, IRS altitude, and distance from the BS are optimally selected. Reference [88] presented an algorithm for minimizing the weighted sum BER and the corresponding improvement in the signal accuracy in the IRS-assisted UAV system. In [89], the phase shift matrix of UAVs, transmission scheduling, and IRS elements were optimized to minimize the expected sum of the age of information (AoI). In this study, a novel relay system for remote IoT wireless networks was proposed by integrating UAVs and IRSs. Reference [90] investigated the symbol error rate (SER) and ergodic capacity in a system designed to maximize the SNR. The authors verified that the asymptotic SNR is within the region between the derived boundaries and approaches the boundaries as the number of reflective elements increases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Objective** & **Design Variables** & **Scenario** \\ \hline \multirow{3}{*}{[86]} & \multirow{3}{*}{Optimize resource allocation} & UAV deployment, & \\  & & IRS phase shift, & \\  & & transmit block length, & \\  & & MBS power & \\ \hline \multirow{3}{*}{[40]} & \multirow{3}{*}{Minimize the total decoding} & UAV position, & \\  & error rate & block length & \\ \hline \multirow{3}{*}{[87]} & \multirow{3}{*}{Maximize the signal gain} & IRS location, & \\  & & IRS altitude, & \\  & & IRS to BS distance & \\ \hline \multirow{3}{*}{[88]} & \multirow{3}{*}{Minimize the weighted sum BER} & UAV trajectory, & \\  & & IRS phase shift, & \\  & & IRS scheduling (for fairness) & \\ \hline \multirow{3}{*}{[89]} & \multirow{3}{*}{Minimizing the expected sum AoI} & UAV altitude, & \\  & & scheduling, & \\  & & IRS phases shift & \\ \hline \multirow{3}{*}{[90]} & Extend the network coverage and improve the communication reliability, as well as spectral efficiency & Ergodic capacity, & \\  & & SER, & \\ \cline{1-1}  & & average SNR & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of previous works relevant to improvement of other performance.