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Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

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Optimizing inset-fed rectangular micro strip patch antenna by improved particle swarm optimization and simulated

Jakkuluri Vijaya Kumar¹, S Maflin Shaby¹

¹School of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India.

Network (Bristol, England)

|May 28, 2024

Summary

This study introduces an improved Particle Swarm Optimization (PSO-SA) algorithm to optimize microstrip patch antennas (MPAs) for better gain and reduced return loss in wireless communication. The optimized antennas show improved performance metrics for Ku-band and C-band applications.

Keywords:

Microstrip patch antenna c-band feedforward neural network (FNN)ku-band particle swarm optimization (PSO)

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Area of Science:

Antenna theory and design
Electromagnetics
Wireless communication systems

Background:

Modern wireless systems demand antennas with high gain, low profile, and reliability.
Existing microstrip patch antenna (MPA) designs often suffer from low gain and high return loss.
Optimization of antenna geometric dimensions is crucial for improved performance.

Purpose of the Study:

To optimize the geometric dimensions (width and length) of inset-fed rectangular microstrip patch antennas.
To enhance antenna gain and minimize return loss for Ku-band and C-band applications.
To evaluate the effectiveness of the proposed PSO-SA optimization approach.

Main Methods:

Utilized an improved Particle Swarm Optimization (PSO) algorithm combined with Simulated Annealing (PSO-SA).
Optimized antenna parameters including substrate height, dielectric constant, resonant frequency, width, and height.
Employed a Feedforward Neural Network (FNN) to calculate fitness values within the PSO-SA algorithm.
Implemented antenna design and optimization in MATLAB software.

Main Results:

Achieved optimized antenna designs for Ku-band and C-band applications with improved gain and return loss.
Evaluated performance using radiation pattern, return loss, VSWR, gain, directivity, computation time, and convergence speed.
Demonstrated the effectiveness of the PSO-SA approach in enhancing MPA performance.

Conclusions:

The PSO-SA algorithm effectively optimizes microstrip patch antennas for enhanced performance in wireless communication.
The proposed method provides a viable solution for designing high-gain, low-loss antennas for specific frequency bands.
Further research can explore the application of this method to other antenna types and frequency ranges.