A novel beamforming technique using mmWave antenna arrays for 5G wireless communication networks

Abstract

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) and beamforming technologies are most likely the integral parts and the key enablers of 5G-and-Beyond (5G-B) wireless communication systems. From that perspective, this manuscript proposes an efficient digital beamforming technique for 5G applications. The suggested approach is formulated based on a reliable sampling technique that effectively samples the interference-plus-noise covariance matrix (IPNCM) to construct an efficient projection matrix (PM). The proposed technique is therefore named interference-plus-noise PM (IPNPM) beamformer. Next, a compact-size rectangular microstrip antenna operating at 49.3 GHz having 1.7 GHz potential bandwidth is designed using CST microwave studio. The designed antenna is subsequently used to model a 32-element uniform linear array (ULA) representing a typical 5G base station. The elements of the modeled ULA are then manually fed with the complex-weights generated from the proposed beamformer to produce high-power beams and deep nulls toward the desired and undesired locations, respectively. To further improve the beamformer performance in terms of minimizing radiating power within undesired regions, an iterative sidelobe level (SLL) suppression technique is developed and integrated with the proposed beamformer. The conducted theoretical analysis justifies that the IPNPM beamformer requires less arithmetic operations than the well-known beamforming approaches. To reveal the benefits of the proposed technique, the array radiation pattern based on the proposed beamformer is plotted and compared with those for the three widely used approaches. An intensive Monte Carlo simulation is also carried out in which the IPNPM beamformer performance is systemically compared with the popular approaches based on the output signal to interference-plus-noise ratio (SINR) criterion. The achieved results demonstrate that the proposed approach is superior and surpasses the rival algorithms in terms of generating deeper nulls, enhanced output SINR, and lower execution time. It is also demonstrated that, unlike the existing SLL reduction techniques, the newly developed SLL suppressor reduces the SLLs of the proposed beamformer to a predefined threshold value (i.e., −24 dB) after few iterations.