Spatial-Sampling-Based Spectrum Aliasing Analysis and Antenna Array Structure Optimization for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) arrays have emerged as pivotal technology for 5G wireless communication systems, finding widespread implementation and deployment. However, despite their significant potential, the performance gains achieved in practical environments do not consistently scale with the accompanying rise in hardware costs. To address this issue, we delve into the design of rectangular array structures for massive MIMO with varying parameters. The core idea is to optimize the array structure to suit diverse propagation characteristics. Our approach treats the massive MIMO array as a spatial sampling system. A 2-D Fourier transform concerning the elevation and azimuth steering factors is employed to derive the angular spectrum of incoming signals at the base station. Building on spatial spectrum analysis, we unveil the relationship between antenna array parameters, such as the number of antennas and the vertical/horizontal antenna spacings, and the spatial resolution and spectral aliasing inherent to the massive MIMO system. Furthermore, we investigate how array structure parameters impact channel capacity in multiuser scenarios and propose effective strategies for enhancing capacity while mitigating aliasing through parameter adjustments. Finally, we present numerical results that validate the effectiveness of our proposed approaches. The outcomes of this study establish a solid foundation for optimizing the design, deployment, and spatial resource allocation of practical massive MIMO systems.