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Machine learning enabled compact flexible full ground UWB antenna for wearable applications

Published online by Cambridge University Press:  31 October 2024

Alladi Praveena
Affiliation:
Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, India
Govindaswamy Umamaheswari
Affiliation:
Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, India
Jayant Kumar Rai
Affiliation:
Department of Electrical and Electronics Engineering, ABV Indian Institute of Information Technology and Management, Gwalior, India
Pinku Ranjan*
Affiliation:
Department of Electrical and Electronics Engineering, ABV Indian Institute of Information Technology and Management, Gwalior, India
*
Corresponding author: Pinku Ranjan; Email: pinkuranjan@iiitm.ac.in
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Abstract

This work introduces a novel compact ultra-wideband (UWB) antenna designed for wearable applications, employing a bioinspired structure and machine learning (ML) techniques to achieve exceptional performance in the 3.10–10.42 GHz range. The antenna is fabricated by positioning conductive fabric on a polydimethylsiloxane polymer of 1 mm thickness to augment high flexibility and durability. Additionally, it pioneers integrating a complete ground plane to mitigate back radiation toward the human body, presenting a compact (35.5 × 30.5 × 1 mm3) UWB antenna design compliant with IEEE 802.15.6 standards. The design methodology includes using bandwidth enhancement techniques such as chamfering edges, slots, and adding stubs in the feed, along with applying ML to optimize the antenna’s dimensions for desired return loss characteristics. The proposed antenna demonstrates exceptional resilience to human body loading and physical deformation. The simulation and measurement results have good agreement. The K-nearest neighbour method beat the other ML algorithms maximum accuracy of 99.62% to predict the S11. According to the author’s best knowledge, this is the first compact UWB antenna with full ground specified by IEEE.802.15.6 with ML reported.

Information

Type
Research Paper
Copyright
© The Author(s), 2024. Published by Cambridge University Press in association with The European Microwave Association.
Figure 0

Figure 1. (a) Circular patch with radius “R” =10 mm (b) maple leaf patch with maximum edge length “r” of 10 mm from its center.

Figure 1

Figure 2. Evolutionary stages and their return loss characteristics.

Figure 2

Table 1. Details of the evolutionary stages and reflection characteristics

Figure 3

Figure 3. Flow chart of Mmachine learning in the proposed UWB antenna.

Figure 4

Figure 4. Actual and predicted values of ML algorithms.

Figure 5

Figure 5. Training time of ML algorithms.

Figure 6

Figure 6. Testing time of ML algorithms.

Figure 7

Figure 7. Accuracy of ML algorithms.

Figure 8

Figure 8. Comparison of KNN ML with HFSS.

Figure 9

Table 2. MSE, MAE, and R2 score of ML algorithms

Figure 10

Figure 9. Dimension (in mm) (a =35.56, b =13.79, c =4.06, d =5.59, e =7.9, f =3.9, g =3, h =8.5, i =5.1, j =3, k =5.9, l =3.9, m =2.2, n =2.7).

Figure 11

Figure 10. Prototype of the UWB antenna (a) front view and (b) rear view.

Figure 12

Figure 11. Flexibility of the UWB antenna (a) bent in the Y (b) X-axis.

Figure 13

Figure 12. Surface current distribution at 3.1, 5.5, and 9.5 GHz.

Figure 14

Figure 13. Anechoic chamber (a) pictorial representation, and (b) measurement setup inside the chamber.

Figure 15

Table 3. Dielectric properties of human tissue layers at various frequencies

Figure 16

Figure 14. Comparison of the measured and simulated return loss in free space.

Figure 17

Figure 15. Comparison of measured and simulated return loss free space in X and Y axis bending.

Figure 18

Figure 16. Simulated and measured E plane at various frequencies (a) 4.8 GHz, (b) 6.2 GHz, (c) 8.6 GHz, (d) 9.2 GHz and (e) 10.2 GHz.

Figure 19

Figure 17. Simulated and measured H plane radiation pattern at (a) 4.8 GHz, (b) 6.2 GHz, (c) 8.6 GHz, (d) 9.2 GHz and (e) 10.2 GHz.

Figure 20

Figure 18. Antenna setup for group delay analysis (a) side by side (b) face to face.

Figure 21

Figure 19. Group delay plot of the proposed MFG antenna.

Figure 22

Figure 20. Human tissue layer model (a) flat (b) cylindrical.

Figure 23

Figure 21. On body bending in X and Y orientations.

Figure 24

Figure 22. Return loss comparison of the simulated and measured return loss at 0 mm on flat human body model.

Figure 25

Figure 23. SAR values at (a) 3.8 GHz (b) 7.5 GHz and (c) 10 GHz.

Figure 26

Table 4. Comparison with state-of-art works