Adaptive Dual-Beam Tracking for IRS-Assisted High-Speed Multi-UAV Communication Networks

Area of Science:

• Wireless Communication
• Aerial Networks
• Signal Processing

Background:

• High-speed multiple unmanned aerial vehicle networks (HSMUAVNs) face performance degradation due to dynamic environments and channel variations.
• Intelligent reflecting surface (IRS) assisted communication is a promising solution for enhancing aerial network performance.
• Beam misalignment and severe channel fading are critical challenges in IRS-assisted HSMUAVNs.

Purpose of the Study:

• To design an adaptive dual-beam tracking scheme for IRS-assisted HSMUAVNs.
• To mitigate beam misalignment and improve the performance of the worst-case unmanned aerial vehicle (UAV).
• To ensure reliable communication links in dynamic high-speed aerial networks.

Main Methods:

• An attention-based double-layer long short-term memory network is used for UAV spatial angle prediction.
• A signal-to-interference-plus-noise ratio (SINR) maximization problem is formulated for the worst-case UAV.
• Optimization is achieved by decoupling the problem and employing semi-definite relaxation, bisection method, and eigenvalue decomposition.
• Adaptive dual beams are generated by linearly weighting optimized beam components.

Main Results:

• The proposed scheme effectively predicts UAV spatial angle information for optimal beam coverage.
• The joint optimization of beam components and IRS phase shifts enhances the worst-case UAV's SINR.
• Numerical results demonstrate significant performance improvements for the worst-case UAV.
• The scheme guarantees sufficient SINR across the entire HSMUAVN.

Conclusions:

• The adaptive dual-beam tracking scheme effectively addresses challenges in IRS-assisted HSMUAVNs.
• The proposed method enhances communication reliability and performance for all UAVs, especially the worst-case one.
• This work provides a robust solution for maintaining stable communication in high-speed aerial networks.