Analysis of Electrically Couple SRR EBG Structure for Sub 6 GHz Wireless Applications

[Purpose] For 5G wireless communication at frequencies below 6 GHz, this research describes an electromagnetic bandgap (EBG) structure based on an electrically coupled split-ring resonator (ECSRR). To create the EBG, the ECSRR is embedded within a structure similar to an interdigital capacitor. [Design/methodology/approach] The proposed EBG design is composed of an electrically coupled structure that resembles an interdigital capacitor and a structure built of split-ring resonators. The proposed EBG structure was printed on a FR4 substrate that had a 4.4, 1.6 millimeters thickness, and a tan =0.025. An interdigital capacitor-like structure is connected to the inner split-rings, and the top layer consists of two sets of split-ring resonators that are electrically connected. A wire-like structure is printed on the substrate's bottom layer. [Findings] The suggested ECSRR EBG structure has a reflection phase bandwidth of 2.65 GHz between 3.5 and 6.15 GHz, and also a bandgap property bandwidth of 2.9 GHz between 3.3 and 6.2 GHz. Without an EBG structure, the CPW-fed microstrip quarter wave monopole antenna has a gain(maximum) of 2.574 dBi at 4.15 GHz and a bandwidth of 4.6 GHz between 3.4 and 8 GHz. Gain(maximum)of 8.785 dBi is achieved at 4.15 GHz when the ECSRR EBG structure is combined with a CPW-fed microstrip quarter wave monopole antenna. [Originality/Value] The suggested ECSRR EBG structure is merged with a two-element ECSRR bow-tie antenna to verify its bandgap property. By inserting the ECSRR EBG structure's 2x4 array in between the two elements of the bow-tie antenna, we can decrease their mutual coupling. Maximum isolation is achieved at 4.9 GHz, with mutual coupling below -32 dB over the whole operational frequency range. Decision science enables antenna designers to analyze, optimize, and track the performance of the antenna characteristics. The following are some of the potential benefits of the proposed study: It is argued that statistical and regression properties can be used to create a powerful tool for feature extraction. To better understand how antenna design choices affect antenna performance, we compare different regression models. To accurately calculate the S parameters from the relevant UWB antenna dimensions, a random forest classifier that has been optimized for this task has been developed.