Reconfigurable intelligent surface and UAV coordination for reliable THz wireless networks

Area of Science:

• Wireless communication systems
• Next-generation wireless networks
• Terahertz (THz) communication

Background:

• Terahertz (THz) communication offers ultra-high data rates for future wireless networks.
• Challenges include severe path loss, molecular absorption, and blockage sensitivity, limiting coverage and reliability.

Purpose of the Study:

• To propose a Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) positioning (RAVP) framework.
• To enhance Terahertz (THz) communication performance by jointly optimizing RIS configuration and UAV deployment.

Main Methods:

• Integration of RIS for controllable signal reflection and UAVs for flexible RIS placement.
• A reinforcement learning (RL)-based strategy combining modified K-means clustering and gradient-based optimization.
• Coordinated optimization of user grouping, RIS phase-shift, and UAV positioning.

Main Results:

• Consistent improvements in link robustness, achievable data rate, and user connectivity.
• Demonstrated performance gains compared to conventional THz systems without RIS or UAV optimization.
• Effective coordination of RIS and UAVs enhances overall communication performance.

Conclusions:

• The proposed RIS-UAV coordinated optimization framework significantly enhances THz communication.
• Highlights the potential for 6G-enabled applications like smart cities and the Internet of Things (IoT).
• Coordinated RIS-UAV deployment is crucial for overcoming THz communication limitations.