Abstract
This paper presents a multiband rectangular microstrip antenna using spiral-shaped configurations. The antenna has been designed by combining two configurations of microstrip and spiral with consideration of careful selection of the substrate material, the dimension of the rectangular microstrip, the distance between the turned spiral, and the number of turns of the spiral. The efficiency and accuracy have been improved using machine learning algorithms as well. Machine learning has been studied to model the proposed antenna based on the performance requirements, which requires a sufficient training data to improve the accuracy. Three different machine learning models are applied to improve the accuracy and generalization performance and compared to simulation and measurement results. Simulation, measurement, and machine learning results confirm that the proposed antenna is a new electrically small and operating over a wide range of high-frequency bands between 1 GHz–4 GHz. Machine learning models have the best prediction ability with a mean square error (MSE) of 0.03, and 0.05. The antenna structure and size are compatible and suitable for several multi-band wireless mobile systems operating in L-band and S-band. The results, such as directivity, Half-Power Beamwidth, Voltage Standing Wave Ratio (VSWR), and S-parameter curves, are analysed and compared with the numerical formulation for both spiral and microstrip antennas.
Highlights
In recent years, the need for antennas have widely increased
Because of the attractive similarity between the properties of microstrip patch and spiral antennas, they have been greatly applied in wireless communications, biological medicine, radar and electronic counter measurements [2], Manuscript received 2 September, 2020; accepted 11 January, 2021
It has been noticed that microstrip and spiral antennas have simple and same condition of the configuration, consisting of a very thin radiating element t o on a side of a substrate material, while the ground plane is on the other side [5]
Summary
The need for antennas have widely increased. It can certainly be considered as the main leading power behind the progressions being achieved in the field of modern communication and wireless technologies. It may be single, double or more windings directed right or left with different configurations, which are logarithmic, planar circular, rectangular, selfcomplementary, and Archimedean spirals [6] Both of the two types of the proposed may be electrically small and an element of a set of an array [7]–[9]. In [18], multistage collaborative ML (MS-CoML) methods, such as single-output Gaussian process regression (SOGPR) and symmetric multi-output Gaussian process regression (MOGPR) methods are introduced to collaboratively construct extremely accurate multi-task surrogate solutions/models for different antennas. The main contribution of this work is developing a novel configuration to obtain an electrically small lateral size antenna, multiple operating frequencies to fulfil the coverage of 1 GHz–4 GHz and return losses with the directivity (D) of 7 dBi, a Half Power Beam Width (HPBW) lower than 90 °, and good propagation characteristics while maintaining matching of VSWR ≤ 2. The accuracy and generalization for predicted, simulated, and measured models are compared, as well as differences and agreements between the obtained models have been cleared up
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