Statistical CSI-Based Beamspace Transmission for Massive MIMO LEO Satellite Communications.

Area of Science:

• Satellite Communications
• Signal Processing

Background:

• Precoding is crucial for mitigating co-channel interference (CCI) and improving spectral efficiency (SE) in multibeam low-Earth-orbit (LEO) satellite systems.
• Practical implementation faces challenges due to the need for instantaneous channel state information (iCSI) and high computational complexity, hindering compatibility with existing beamforming schemes.

Purpose of the Study:

• To propose a beamspace transmission framework that leverages statistical CSI (sCSI) for reduced computational complexity.
• To develop a low-complexity beam selection algorithm and a beamspace weighted minimum mean square error (WMMSE) precoding scheme.

Main Methods:

• A low-complexity beam selection algorithm using user terminal (UT) angular information to select a subset of beams from a codebook.
• Derivation of a beamspace WMMSE precoding scheme utilizing an sCSI-based formulation (sWMMSE) based on an upper bound approximation of the ergodic sum rate.
• Comparison with antenna-domain precoding designs and traditional mean square error (MSE)-based methods.

Main Results:

• The proposed framework achieves reduced computational complexity compared to antenna-domain precoding.
• The sWMMSE formulation provides a tighter estimate for performance.
• Simulation results confirm a favorable trade-off between performance and computational complexity.

Conclusions:

• The sCSI-based beamspace transmission framework offers a practical solution for precoding in LEO satellite systems.
• The method effectively mitigates CCI and enhances SE while managing computational load.
• This approach presents a viable alternative to existing, more complex precoding techniques.