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.

The combination of large-scale antenna arrays and simultaneous wireless information and power transfer (SWIPT), which can provide enormous increase of throughput and energy efficiency is a promising key in next generation wireless system (5G). This paper investigates efficient transceiver design to minimize transmit power, subject to users' required data rates and energy harvesting, in large-scale SWIPT system where the base station utilizes a very large number of antennas for transmitting both data and energy to multiple users equipped with time-switching (TS) or power-splitting (PS) receive structures. We first propose the well-known semidefinite relaxation (SDR) and Gaussian randomization techniques to solve the minimum transmit power problems. However, for these large-scale SWIPT problems, the proposed scheme, which is based on conventional SDR method, is not suitable due to its excessive computation costs, and a consensus alternating direction method of multipliers (ADMM) cannot be directly applied to the case that TS or PS ratios are involved in the optimization problem. Therefore, in the second solution, our first step is to optimize the variables of TS or PS ratios, and to achieve simplified problems. After then, we propose fast algorithms for solving these problems, where the outer loop of sequential parametric convex approximation (SPCA) is combined with the inner loop of ADMM. Numerical simulations show the fast convergence and superiority of the proposed solutions.