Polarization-aware UAV deployment for reliable MIMO communication in forested environments

Area of Science:

• Electrical Engineering
• Wireless Communications
• Remote Sensing

Background:

• Terrestrial infrastructure is often unavailable in remote, forested regions.
• Unmanned Aerial Vehicles (UAVs) with Multiple-Input Multiple-Output (MIMO) systems offer a solution for connectivity.
• Radio propagation through vegetation is complex, affected by polarization-dependent effects like scattering and attenuation.

Purpose of the Study:

• To investigate polarization-aware UAV deployment for enhanced air-to-ground communication in dense canopy environments.
• To develop a vegetation-aware propagation model accounting for foliage dielectric properties.
• To optimize UAV positioning to mitigate signal degradation caused by Cross-Polarization Discrimination (XPD).

Main Methods:

• Developed a vegetation-aware propagation model using Debye relaxation and Kramers-Kronig relations.
• Identified Cross-Polarization Discrimination (XPD) as a key factor for signal quality.
• Employed the Crow Search Algorithm (CSA) to find optimal UAV locations minimizing XPD.

Main Results:

• Polarization-aware UAV deployment significantly enhances link robustness compared to path-loss strategies.
• The proposed method shows particular effectiveness at higher frequencies.
• Non-monotonic variations in XPD complicate traditional UAV positioning.

Conclusions:

• Integrating polarization awareness into UAV communication planning is crucial for reliable performance in vegetated areas.
• Optimal UAV placement strategies are essential for missions like search-and-rescue and disaster recovery.
• The developed model and optimization technique improve communication resilience in challenging environments.