Randomly overlap subarray feeding network to reduce number of phase shifter in 28GHz

Abstract

Synthesizing antenna arrays for fifth-generation communication technology is the most significant issue in the electromagnetic industry and academia. This paper focused on a comprehensive algorithm for developing a 5G base station antenna array. The suggested algorithm aims to provide a high-gain array antenna with a continuous wide scan angle without a grating lobe, as much as a compact size, low cost, and simplicity of fabrication, especially in the array feeding network system. The best architecture is specified by comparing the array factor of numerous subarray combinations to achieve the grating lobe’s minimum level. By considering additional limitations in our approach, such as different subarray symmetric architecture, complex weighting function, minimal number of overlapped elements, and an optimal number of microstrip layers, we improve the specification over previous research and lower the runtime procedure. The proposed method is also used to construct a linear array antenna with 49 radiating elements for a 5G base station antenna operating at 28 GHz. Consequently, the number of phase shifters has been reduced by more than 53%, significantly improving over earlier efforts. Then a hybrid genetic algorithm and a particle swarm optimization technique are applied to determine the optimal values of excitation coefficients to control side lobe level(SLL) and beam scanning. The amplitude and phase step variations are calculated as 0.1 and 1°, respectively. HPBW of 2.8°, gain of 28 dB, scanning up to ± 25° in one direction, and SLL below -24 dB are the electromagnetic properties of the designed aperiodic linear array. An example of implementing the suggested method, a 16-element array with a random overlap subarray structure, including the feeding network and microstrip antenna element, will be modeled using a full-wave simulator. The simulation results show that the proposed algorithm is efficient for designing array topology.