Tri-Band, Stable and Compact Patch Frequency Selective Surface Optimized via Hybrid Bioinspired Computing for Applications at 2.4, 3.5 and 5.8 GHz

Abstract

This work addresses the synthesis of a multi-band frequency selective surface (FSS) through bioinspired computing and a general regression neural network (GRNN). This hybrid computational method, which utilizes the multi-objective cuckoo search algorithm combined to a GRNN, determine the best physical dimensions of the FSS in order to achieve a multi-band filtering at the 2.4, 3.5 and 5.8 GHz spectrums. Therefore, the results are to be applied to aid the propagation of Wi-Fi, WLAN, WiMAX and future sub-6 GHz 5G systems. The resonant frequencies were measured and a -10 dB cutoff value has been considered for the transmission coefficient. The triple rectangular loop conductor geometry of the device is printed upon a glass epoxy (FR-4) substrate. Measurements were made for different wave incidence angles, from 0°up to 45°, to demonstrate how signal incidence would affect the device's functioning. The agreement between simulated and measured data display satisfactory results.

Hybrid computational and real data-based positioning of small cells in 5G networks

“…One of the most utilized bioinspired algorithms, the Cuckoo Search algorithm has been created by Yang & Deb (2009) . It has been used to optimize multiple applications ever since, mainly in the areas of mathematics and engineering, according to Shehab, Khader & Al-Betar (2017) and Ferreira et al (2021) . The main bioinspired idea behind this method is the computational modeling of the parasitic behavior of cuckoo-type birds, that often lay their eggs inside the nests of other types of birds.…”