User Grouping and Beamforming for HAP Massive MIMO Systems Based on Statistical-Eigenmode

Abstract

In this letter, we study user grouping and beamforming for high altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. We first exploit the fact that the signal power mainly concentrates on statistical-eigenmode (SE), and we then use it to perform user grouping and design the outer beamformer. The user grouping is realized based on the average chordal distance between the SEs instead of the chordal distance between the covariance eigenspaces of the users in the same group. In this case, the complexity of a singular value decomposition is avoided. The outer beamformer is designed using reduced-dimensional SEs of the users, and the computational complexity is lower than in the case where the statistical correlation matrix is used. Numerical results demonstrate the performance enhancement of the proposed beamforming scheme in HAP massive MIMO systems compared to the existing schemes based on channel correlation matrix.