Machine learning-optimized compact wearable frequency reconfigurable antenna - for sub-6 GHz/mm-wave 5G integration

Aug 3, 2026

Future 5G wireless systems face integration challenges when combining sub-6 GHz and millimeter-wave (mm-wave) bands with large frequency ratios. The study presents a machine learning-optimized compact wearable frequency-reconfigurable antenna designed for sub-6 GHz/mm-wave 5G integration. The prototype uses a flexible Rogers Duroid substrate, achieves dual-band operation via an H-shaped slot and PIN-diode ON/OFF switching, and maintains stable on-body performance under bending tests while meeting FCC and ICNIRP SAR limits. A supervised ML regression framework predicts S11, with a decision tree delivering leading accuracy (R2=97.80%).

LanguageEnglish

CategoryTechnology

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

5G frequency bands and integration challenges

Reconfigurable antennas for multi-application wireless systems

Antenna design and operation

Compact wearable structure and flexible substrate

Dual-band realization and PIN-diode switching

Performance evaluation

Measured bandwidth, gain, and radiation efficiency

Radiation patterns across bands

SAR compliance for human tissue

Bending tests and on-body stability

Machine learning for S11 prediction

Supervised ML regression framework

Decision tree accuracy and error metrics

Conclusion

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Machine learning-optimized compact wearable frequency reconfigurable antenna for sub- 6 GHz/mm-wave 5G integration Abubakar Salisu1􀀍, Mahmud Abd Elwanis1, Issa Elfergani1,2, Umar Musa3􀀍, AbdulgaforAlfares4, Ibrahim Gharbia1, Jonathan Rodriguez2, Chan H. See5 & Raed Abd-alhameed1,6􀀍 Future 5G wireless systems will have substantial challenges in integrating the sub-6 GHz and millimeter-wave (mm-wave) bands due to their massive frequency ratios. This paper proposes a machine learning-optimized compact wearable frequency-reconfigurable antenna for sub-6 GHz/mmwave 5G integration. Fabricated on a flexible Rogers Duroid substrate (27.8 × 14 × 0.508 mm3), the antenna initially employs a circular structure resonating at 28 GHz. Dual-band operation (3.5 GHz and 28 GHz) is achieved by etching an H-shaped slot into the rectangular patch. A PIN diode is employed toreconfigure the proposed antenna in theON and OFF states. In the ON state, the antenna operates at 3.5 GHz and 28 GHz, achieving measured bandwidths of 25.4% and 73.2%, gains of 3.63 dBi and 5.25 dBi, and radiation efficiencies of 90.5% and 88%, respectively. In the OFF state, the antenna operates at 28 GHz, achieving a measured bandwidth of 72.9%, gain of 6.2 dBi, and a radiation efficiency of 89% . Bidirectional E-plane and omnidirectional H-plane radiation patterns are maintained across both bands. At 3.5 GHz, the specific absorption rate (SAR) value for 1 g and 10 g of human tissue is 0.438 W/ kg and 0.0147 W/kg, while at 28 GHz, the SAR value is 0.801 W/kg and 1.09 W/kg, which comply with the FCC and ICNIRP standards. Bending tests (lap, chest, arm) demonstrate stable on-body performance. The antenna’s S11 was predicted using a supervised ML regression framework. Among tested algorithms, the decision tree achieved state-of-the-art accuracy (R2: 97.80%) with minimal errors (MAE: 0.72, MSE: 0.28, MSLE: 0.56, RMSLE: 0.81, RMSE: 0.66). The proposed antenna system is suitable for future 5G devices. Keywords Frequency reconfigurability, Machine learning, SAR, Bending investigation, PIN diode The growing demand for high-speed multimedia data transmission has become increasingly critical in today’s digital landscape1,2. To address the need for rapid data transfer and low latency, numerous countries have deployed fifth-generation (5G) communication systems3–5. As a leading future communication technology, 5G is expected to surpass the limitations of existing mobile and local area network technologies6. The Federal Communications Commission (FCC) has allocated specific frequency bands for 5G applications, including 3.5 GHz (sub-6 GHz range) and 24–30 GHz, 37 GHz, 39 GHz, and 64–71 GHz for the mm-wave range7,8. Among these, the 3.5 GHz and 24–30 GHz bands are among the most widely adopted 5G frequencies globally, including in countries like China9. To leverage the broader bandwidth available at higher frequencies, cellular systems are transitioning to mm-wave bands, which enable increased data rates. However, while mm-wave antennas offer faster data transmission compared to sub-6 GHz antennas, they also face significant challenges such as propagation losses10–13. In contrast, mm-wave technology is anticipated to improve data capacity and connection throughput within short-range coverage areas, despite challenges such as signal attenuation14. While mm-wave 1Department of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK. 2Instituto de Telecomunicações, Campus Universitário de Santiago, Aveiro 3810-193, Portugal. 3Department of Electrical Engineering, Bayero University Kano, Kano 700006, Nigeria. 4Department Department of Electrical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia. 5 School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, UK. 6Department of Communication and Inf

FAQs

How does the proposed antenna achieve frequency reconfigurability for sub-6 GHz/mm-wave 5G integration?

It uses a compact wearable design with an H-shaped slot etched into the rectangular patch for dual-band operation, and a PIN diode to switch the antenna between ON and OFF states.

What performance metrics are reported for the antenna in the ON and OFF states?

In the ON state it operates at 3.5 GHz and 28 GHz with reported bandwidths, gains, and radiation efficiencies, and in the OFF state it operates at 28 GHz with a high measured bandwidth, gain, and radiation efficiency.

How is human safety verified, and what role does machine learning play in the study?

The study evaluates specific absorption rate (SAR) at 1 g and 10 g tissue and confirms compliance with FCC and ICNIRP standards. It also predicts the antenna S11 using supervised machine learning regression, where a decision tree achieves the best reported accuracy.

Aug 3, 2026

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