2019/08/27 by Nhan T. Nguyen, Nguyen, Nhan Thanh, Kyungchun Lee +1 · 1 citation
Engineering · #Advanced MIMO Systems Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.10056
openalex publication_date 2019/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A variety of hybrid analog-digital beamforming architectures have recently\nbeen proposed for massive multiple-input multiple-output (MIMO) systems to\nreduce energy consumption and the cost of implementation. In the analog\nprocessing network of these architectures, the practical sub-connected\nstructure requires lower power consumption and hardware complexity than the\nfully connected structure but cannot fully exploit the beamforming gains, which\nleads to a loss in overall performance. In this work, we propose a novel\nunequal sub-connected architecture for hybrid combining at the receiver of a\nmassive MIMO system that employs unequal numbers of antennas in sub-antenna\narrays. The optimal design of the proposed architecture is analytically\nderived, and includes antenna allocation and channel ordering schemes.\nSimulation results show that an enhancement of up to 10% can be attained in the\ntotal achievable rate by unequally assigning antennas to sub-arrays in the\nsub-connected system at the cost of a marginal increase in power consumption.\nFurthermore, in order to reduce the computational complexity involved in\nfinding the optimal number of antennas connected to each radio frequency (RF)\nchain, we propose three low-complexity antenna allocation algorithms. The\nsimulation results show that they can yield a significant reduction in\ncomplexity while achieving near-optimal performance.\n