A Review of Synthesis Techniques for Phased Antenna Arrays in Wireless Communications and Remote Sensing

Electronically controlled antenna arrays, such as reconfigurable and phased antenna arrays, are essential elements of high-frequency 5G communication hardware. These antenna arrays are aimed at delivering specified communication scenarios and channel characteristics in the mm-wave parts of the 5G spectrum. At the same time, several challenges are associated with the development of such antenna structures, and these challenges mainly originate from their intended mass production, contemporary manufacturing technologies, integration with active RF chains, compact size, dense circuitry, and limitations in postmanufacturing tuning. Consequently, 5G antenna array designers are presented with contradictory design requirements and constraints. Furthermore, these designers need to handle large numbers of designable parameters of the antenna array models, which can be computationally expensive, especially for repetitive and adaptive simulations that are required in design optimization and tuning. Antenna array synthesis, namely, the process of finding positions, orientation, and excitation of the array radiators, is a challenging yet crucial part of antenna array development. This process ensures that the performance requirements of the antenna array are met. Therefore, there is a need for reliable yet fast automated computer-aided design (CAD) and synthesis tools that can support the development of 5G antenna array solutions, from the initial prototyping stage to the final manufacturing tolerance analysis. This paper presents an overview of recent advances in antenna array synthesis from the viewpoint of their applicability to the design of electronically reconfigurable and phased antenna arrays for wireless communications and remote sensing.

Cat Swarm Optimization Algorithm For Antenna Array Synthesis

Cat swarm optimization (CSO) is a developmental technique enlivened by the animals in Mother Nature for taking care of optimization issue. Short of what multi decade after CSO is proposed, it has been improved and applied in various fields by numerous scientists as of late. CSO is created by noticing the practices of cats, and made out of two sub-models, i.e., following mode and looking for mode, which model upon the practices of cats. The prerequisite of high directivity signal with extremely quick pillar guiding is preposterous by a solitary antenna. This imperative is aid by staged array antenna which is a mix of various little antennas that can create shaft with high directivity with quick electronic pillar guiding. The radiation example of an antenna array relies firmly upon the weighting technique and the math of the array. The issues related with pillar design causing high obstruction in communication which confine them use by and by. To beat these im-pediments, optimization innovation called Cat Swarm Optimization are applied in mix with the old style array blend strategies for staged array combination. An optimization issue is determined whose arrangement yields an ideal array for stifling impedance because of high side lobe level and grinding lobe. Results are introduced for ideal arrays of shifting array calculation, with various number of antenna components, and for distinct beamwidths and scan angles.

A Comparison of Swarm-Based Optimization Algorithms in Linear Antenna Array Synthesis

Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with low side lobe level (SLL) at a desired half power beam width (HPBW) in far-field. The amplitude and position values ​​of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Mayfly algorithm (MA) and Jellyfish Search (JS) algorithms are compared to realize optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10, 16, 24 and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers and statistical results show that performance of the novel algorithms, MA and JS, are better than well-known methods PSO and ABC.