# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 2 Related Work
### IRS on UAV  
Both the IRS and the UAV are highly compatible with today's advanced technology and are increasingly expected to be utilized in the future. Equipping the UAV with an IRS allows for more flexible deployment in three dimensions compared to conventional deployment on a building side. [PERSON] et al. [23] compiled the advantages and previous research of an RIS-carried UAV for 6G and mentioned that power consumption, channel estimation, the physical shape of the RIS, environmental factors like wind, and eavesdropper access can be issues. Many researchers are working on the above issues. Some use deep learning. [PERSON] et al. [24] formulated an RIS-carried UAV federated learning  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Aspect** & **Ref.** & **Main Contribution** \\ \hline IRS only or integrated to BS & [18; 19; 20] & Introduces what IRS is, its recent trends and future opportunities. \\ \hline \multirow{3}{*}{IRS only or integrated to BS} & [21] & Jointly optimizes BS–RIS–user association and beamforming design for cellular networks. \\ \cline{2-3}  & [16; 22] & Product commercialization efforts. Demonstration tests. \\ \hline \multirow{3}{*}{IRS on UAV} & [23] & Compiles current research, challenges, and future trends on RIS-carried UAVs. \\ \cline{2-3}  & [24; 25] & Applies deep learning for RIS-assisted UAVs. \\ \cline{2-3}  & [26] & Designs a new multifunctional RIS architecture. \\ \cline{2-3}  & [27] & Jointly optimizes the UAV trajectory, RIS passive beamforming, and source power allocation. \\ \hline \multirow{3}{*}{IRS collaborated with UAV-BS} & [28] & Proposes RIS selection scheme for UAV-based multi-RIS-aided multiuser. \\ \cline{2-3}  & [29; 30] & \begin{tabular}{l} Presents a framework to examine the performance of multihop RIS-assisted UAV communication. \\ \end{tabular} \\ \cline{2-3}  & [31; 32] & \
\begin{tabular}{l} Applies for IoT networks like timely data collection \\ and wireless power transfer. \\ \end{tabular} \\ \cline{2-3}  & [33] &
\begin{tabular}{l} Verifies satellite–UAV–terrestrial networks under the consideration of \\ hardware impairments and interference. \\ \end{tabular} \\ \cline{2-3}  & [34; 35; 36] & Jointly optimizes RIS phase shift and UAV position \\ \cline{2-3}  & & to improve power consumption. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related technical works on several perspectives.  
model, proposed resource allocation in an RIS controller, and achieved a maximum data rate. [PERSON] et al. (2018) showed that the RIS can significantly improve the system sum rate in the added signal reflection mode. [PERSON] et al. (2018) designed a new multifunctional RIS architecture and contributed to combating eavesdropping. A multifunctional RIS can simultaneously reflect and amplify the desired signal and emit friendly jamming. [PERSON] et al. (2018) accomplished a higher average downlink throughput by jointly optimizing the UAV trajectory, RIS passive beamforming, and source power allocation for each time slot. They indicated the effect of RIS-UAV relaying communication and the possibility of mounting many elements consisting of IRSs on UAVs.