Performance Parameters Of Microstrip Antennas Research Articles

Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

Artificial neural networks (ANNs) have acquired enormous importance in computing of the performance parameters of microstrip antennas due to their generalized and adaptive features. However, recently the concept of support vector machines (SVMs) has become very much popular in performance parameters computation due to several attractive features over ANNs. Specifically, SVMs outreach ANNs noticeably in terms of execution time. Likewise, ANNs are having multiple local minima problem, whereas a global and unique solution is provided by SVMs. In this paper, several performance parameters like: resonant frequency, gain, directivity, and radiation efficiency of slotted microstrip antennas with modified ground plane are computed with the help of SVM formulation. Comparisons of different parameters of simulated and computed values are illustrated. The achieved radiation patterns at particular resonant frequency in different planes are included as well. A prototype of the optimized antenna is also fabricated and characterized. A good agreement is attained among the computed, simulated, and measured results.

Neurocomputational Models for Parameter Estimation of Circular Microstrip Patch Antennas

Neurocomputational models eliminates the complex, lengthy and time consuming mathematical procedures for design, analysis and calculating performance parameters of Microstrip antenna. No single ANN based model has been proposed till date for calculating all parameters of circular microstrip antennas simultaneously. This paper presents a Neuro-Computational (NC) approach for estimation of all performance parameters such as Return Loss (RL), Voltage Standing Wave Ratio (VSWR), resonant frequency (fr), Band-Width (BW), Gain(G), Directivity(D) and antenna efficiency(η) of Circular Microstrip Patch Antenna (CMPA) simultaneously. The difficulty in calculating the parameters of these antennas lies due to the involvement of a large number of physical parameters including their associated optimal values. It is indeed very difficult to formulate an exact numerical solution merely on practical observations based empirical studies. In order to circumvent this problem, an alternative solution is achieved using artificial neural network (ANN). Feed-Forward Back-Propagation Artificial Neural Network (FFBP-ANN) trained with Levenberg-Marquardt algorithm is used for estimation of different performance parameters of CMPA. The results of NC estimation are in very agreement with simulated, measured and theoretical results.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Simulation-based analysis of performance parameters of microstrip antennas with criss-cross metamaterial-based artificial substrate

In this paper, we present the design of a metamaterial-based microstrip patch antenna, optimized for bandwidth and multiple frequency operations. A criss-cross structure has been proposed, this shape has been inspired from the famous Jerusalem cross. The theory and design formulas to calculate various parameters of the proposed antenna have been presented. Design starts with the analysis of the proposed unit cell structure, and validating the response using software– HFSS Version 13, to obtain negative response of ε and μ– metamaterial. Following this, a metamaterial-based-microstrip-patch-antenna is designed. A detailed comparative study is conducted exploring the response of the designed patch made of metamaterial and that of the conventional patch. Finally, antenna parameters such as gain, bandwidth, radiation pattern, and multiple frequency responses are investigated and optimised for the same and present in table and response graphs. It is also observed that the physical dimension of the metamaterial-based patch antenna is smaller compared to its conventional counterpart operating at the same fundamental frequency. The challenging part was to develop metamaterial based on some signature structures and techniques that would offer advantage in terms of BW and multiple frequency operation, which is demonstrated in this paper. The unique shape proposed in this paper gives improvement in bandwidth without reducing the gain of the antenna.

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slot-dimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger’s substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.

Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

A Generalized ANN Model for Analyzing and Synthesizing Rectangular, Circular, and Triangular Microstrip Antennas

Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases.

Prediction of Slot-Size and Inserted Air-Gap for Improving the Performance of Rectangular Microstrip Antennas Using Artificial Neural Networks

Artificial neural networks have been getting popularity for predicting various performance parameters of microstrip antennas due to their learning and generalization features. In this letter, a neural-networks-based synthesis model is presented for predicting the “slot-size” on the radiating patch and inserted “air-gap” between the ground plane and the substrate sheet, simultaneously. Different performance parameters like resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual resonance are observed by varying the dimensions of slot and inserted air-gap. For validation, a prototype of microstrip antenna is fabricated using Roger's substrate, and its performance parameters are measured. Measured results show a very good agreement to their predicted and simulated values.