Database-Guided Local Refinement and Truncated Power Estimation for LEO Uplink Anti-Interference Beamforming.

Area of Science:

• Satellite Communications
• Signal Processing
• Electromagnetics

Background:

• Low Earth Orbit (LEO) satellite uplink emergency communications face significant co-channel interference from dense terrestrial networks.
• Accurate interference localization and power estimation are critical for effective signal reception.

Purpose of the Study:

• To propose a database-aided robust adaptive beamforming framework for LEO satellite uplink emergency communications.
• To enhance signal-to-interference-plus-noise ratio (SINR) performance under severe interference and limited snapshots.

Main Methods:

• A three-stage framework: (1) local spatial refinement using constrained Capon search for interferer localization, (2) truncated adaptive amplitude (TAA) estimation for interference power recovery and noise suppression, and (3) interference-plus-noise covariance matrix (INCM) reconstruction for Minimum Variance Distortionless Response (MVDR) beamforming.
• Database angles are used as coarse priors to compensate for spatial mismatches.

Main Results:

• The proposed method achieves near-optimal SINR under challenging conditions, including limited snapshots and strong interference.
• Consistent performance gains were observed over existing methods like DL-MVDR, ESB, and IAA in simulations.
• The framework demonstrated strong robustness across varying signal-to-noise ratio (SNR), interference-to-noise ratio (INR), and spatial mismatch conditions.

Conclusions:

• The database-aided robust adaptive beamforming framework effectively mitigates co-channel interference in LEO satellite uplink emergency communications.
• The method's ability to avoid global angular scanning and improve spatial statistics estimation makes it highly robust and practically applicable.