In this letter, we study the dynamic antenna grouping and the hybrid beamforming for high altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. We first exploit the fact that the ergodic sum rate is only related to statistical channel state information (SCSI) in the large-scale array regime, and then we utilize it to perform the dynamic antenna grouping and design the RF beamformer. By applying the Gershgorin Circle Theorem, the dynamic antenna grouping is realized based on the novel statistical distance metric instead of the value of the instantaneous channels. The RF beamformer is designed according to the singular value decomposition of the statistical correlation matrix according to the obtained dynamic antenna group. Dynamic subarrays mean each RF chain is linked with a dynamic antenna sub-set. The baseband beamformer is derived by utilizing the zero forcing (ZF). Numerical results demonstrate the performance enhancement of our proposed dynamic hybrid precoding (DHP) algorithm.

High altitude platform (HAP), known as line-of-sight dominated communications, effectively enhance the spectral efficiency of wireless networks. However, the line-of-sight links, particularly in urban areas, may be severely deteriorated due to the complex communication environment. The reconfigurable intelligent surface (RIS) is employed to establish the cascaded-link and improve the quality of communication service by smartly reflecting the signals received from HAP to users without direct-link. Motivated by this, the joint precoding scheme for a novel RIS-aided beamspace HAP with non-orthogonal multiple access (HAP-NOMA) system is investigated to maximize the minimum user signal-to-leakage-plus-noise ratio (SLNR) by considering user fairness. Specifically, the SLNR is utilized as metric to design the joint precoding algorithm for a lower complexity, because the isolation between the precoding obtainment and power allocation can make the two parts be attained iteratively. To deal with the formulated non-convex problem, we first derive the statistical upper bound on SLNR based on the random matrix theory in large scale antenna array. Then, the closed-form expressions of power matrix and passive precoding matrix are given by introducing auxiliary variables based on the derived upper bound on SLNR. The proposed joint precoding only depends on the statistical channel state information (SCSI) instead of instantaneous channel state information (ICSI). NOMA serves multi-users simultaneously in the same group to compensate for the loss of spectral efficiency resulted from the beamspace HAP. Numerical results show the effectiveness of the derived statistical upper bound on SLNR and the performance enhancement of the proposed joint precoding algorithm.