Unmanned Aerial Vehicles (UAVs) equipped with Multiple-Input Multiple-Output (MIMO) communication systems are increasingly deployed to restore or extend connectivity in forested and remote regions where terrestrial infrastructure is unavailable. However, radio propagation through vegetation is strongly affected by polarization-dependent scattering, attenuation, and depolarization, which can severely degrade link reliability. This study investigates polarization-aware UAV deployment as a means to enhance air-to-ground communication performance under dense canopy conditions. A vegetation-aware propagation model is developed using the Debye relaxation framework combined with Kramers–Kronig relations to capture the dielectric response of moist foliage. Cross-Polarization Discrimination (XPD) is identified as a dominant factor influencing signal quality, exhibiting non-monotonic variations that complicate UAV positioning. To address this, the Crow Search Algorithm (CSA) is employed to determine optimal UAV locations that minimize XPD between orthogonal polarization channels. Simulation results demonstrate that polarization-aware optimization significantly improves link robustness compared to traditional path-loss-based strategies, particularly at higher frequencies. The findings highlight the importance of integrating polarization awareness into UAV communication planning for critical missions such as search-and-rescue and post-disaster recovery in vegetated environments.
Mohamed Shalaby (2026) studied this question.