Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communications
It addresses channel estimation for 6G networks using multi-UAV systems, focusing on near-field scenarios, but is incremental as it builds on existing OMP methods with adaptations for tensor formulations.
This paper tackles channel estimation for distributed near-field multi-UAV communication systems by deriving SNR expressions under different wave models and proposing two algorithms, SD-OMP and tensor-OMP, with tensor-OMP achieving comparable NMSE performance while reducing computational complexity and improving scalability.
Extremely large antenna array (ELAA) is key to enhancing spectral efficiency in 6G networks. Leveraging the distributed nature of multi-unmanned aerial vehicle (UAV) systems enables the formation of distributed ELAA, which often operate in the near-field region with spatial sparsity, rendering the conventional far-field plane wave assumption invalid. This paper investigates channel estimation for distributed near-field multi-UAV communication systems. We first derive closed-form signal-to-noise ratio (SNR) expressions under the plane wave model (PWM), spherical wave model (SWM), and a hybrid spherical-plane wave model (HSPWM), also referred to as the cross-field model, within a distributed uniform planar array (UPA) scenario. The analysis shows that HSPWM achieves a good balance between modeling accuracy and analytical tractability. Based on this, we propose two channel estimation algorithms: the spherical-domain orthogonal matching pursuit (SD-OMP) and the tensor-OMP. The SD-OMP generalizes the polar domain to jointly consider elevation, azimuth, and range. Under the HSPWM, the channel is naturally formulated as a tensor, enabling the use of tensor-OMP. Simulation results demonstrate that tensor-OMP achieves normalized mean square error (NMSE) performance comparable to SD-OMP, while offering reduced computational complexity and improved scalability.