# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 6 Conclusions and Future Work  
This paper explored the potential for extending the coverage area of millimeter wave UAV-BS wireless networks by incorporating IRSs. We initially set forth distinct criteria for selecting the appropriate IRS and UE associations in an environment abundant with IRS installations. Subsequently, we proposed an algorithm to optimize the communication path between UAVs and UEs. Utilizing real-world field data as a basis, we modeled a three-dimensional environment in MATLAB and conducted numerical analysis to evaluate the effectiveness of our proposed algorithm. The results affirm that our algorithm, when used to integrate IRSs into UAV networks, enhances the received SNR for multiple UEs, thereby contributing to an expansion in a coverage area. Although the SNR at the physical layer was used as an evaluation index in this paper, throughput and other parameters at the upper layers can be calculated by introducing the network simulator ns3 [45]. Also, it will be possible to realize more complex and advanced simulations by setting up multiple access UAVs and designing multiplexing schemes, resource control, and cell partitioning among them.  
As for future research, we aim to assess the quantitative effectiveness of our proposed algorithm in varied environments, including suburban and rural areas. Since this paper focused only on an ideal IRS functionality of being able to reflect radio waves in any direction, more practical IRS settings remain as our future works. It is also necessary to consider simulation with random nonfunctioning IRSs to imitate such scenarios when disaster occurs. Furthermore, incorporating different optimization methods for UAV positioning is expected to reduce computation time and help to determine the optimal location of the UAV more exactly.  
Conceptualization, G.K.T.; Formal analysis, S.Y., J.N. and G.K.T.; Funding acquisition, G.K.T.; Investigation, S.Y.; Methodology, S.Y. and J.N.; Project administration, G.K.T.; Resources, G.K.T.; Software, S.Y.; Supervision, G.K.T.; Validation, S.Y.; Writing--original draft, S.Y.; Writing--review and editing, J.N. and G.K.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Strategic Information and Communications R&D Promotion Programme (SCOPE) through the Ministry of Internal Affairs and Communications of Japan (receipt number JP235003015). The authors are sincerely grateful to DOCOMO InsightMarketing, INC. for their technical support on this work.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest. The founders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ UAV & Unmanned Aerial Vehicle \\ NTN & Nonterrestrial Network \\ mmWave & Millimeter Wave \\ IRS & Intelligent Reflective Surface \\ LoS & Line-of-Sight \\ UE & User Equipment \\ IoT & Internet of Things \\ BS & Base Station \\ AF & Amplified-and-Forward \\ DF & Decode-and-Forward \\ \end{tabular}