ItemArticle

Compact LoRa Patch Antenna Optimization Using Dual Random Starfish Aggregation Coupled Transformer Network for Vital Sign Detection in Breast Cancer WBANs

Title
Compact LoRa Patch Antenna Optimization Using Dual Random Starfish Aggregation Coupled Transformer Network for Vital Sign Detection in Breast Cancer WBANs
Creator
Raveendra, K.; Muniyandy, Elangovan; Sindhu, V.; Arunachalam, Krishna Prakash
Description
The rapid advancement of Wireless Body Area Networks (WBANs) has created a growing demand for compact, efficient, and reliable antenna systems to support continuous health monitoring, particularly for breast cancer applications. Recent methods, including CPW-fed patch antennas, artificial neural network (ANN)-driven models, and wearable textile antennas, have improved antenna design automation and flexibility. However, challenges such as signal distortion from body proximity, gain reduction under bending, Specific Absorption Rate (SAR) compliance, and lack of adaptive tuning continue to limit practical deployment. To overcome these limitations, this study presents a compact LoRa patch antenna optimized using a novel Dual Random Starfish Aggregation Coupled Transformer Network (Dual-Ran-SACTN) framework. This system combines the Starfish Optimization Algorithm (SFOA), a Random-Coupled Neural Network (RCCN), and a Dual-Aggregation Transformer Network (DuAT) to enhance convergence speed and learning efficiency. The antenna, designed in CST Microwave Studio, measures only 80נ60mm2 (0.23??נ0.17??), offering a lightweight and wearable structure for continuous vital sign monitoring. The proposed model exhibits a bidirectional radiation pattern in the E-plane and an omnidirectional pattern in the H-plane, achieving a peak gain of 2.12dBi and a high radiation efficiency of 99.8% at 868MHz. Additionally, the design maintains low SAR and stable performance under bending, making it robust for wearable WBAN applications. This work offers a real-time, energy-efficient solution for intelligent breast cancer monitoring through adaptive antenna optimization. This model supports practical applications such as continuous breast cancer monitoring, wearable health diagnostics, and real-time WBAN-based physiological signal tracking. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Source
Biomedical Materials and Devices;
Date
01-01-2025
Publisher
Springer Nature
Subject
Breast cancer monitoring; Dual random starfish aggregation coupled transformer network (Dual-Ran-SACTN); Dual-aggregation transformer network (DuAT); Long range (LoRa) patch antenna; Random-coupled neural network (RCCN); Wireless body area networks (WBANs)
Coverage
Raveendra K., Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Andhra Pradesh, Tirupati, India; Muniyandy E., Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, 602105, India, Applied Science Research Center, Applied Science Private University, Amman, Jordan; Sindhu V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Arunachalam K.P., Departamento de Ciencias de la Construcci, Facultad de Ciencias de la Construcci Ordenamiento Territorial, Universidad Tecnolica Metropolitana, Santiago, Chile
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 27314812;
Format
online
Language
English
Type
Article

Citation

Raveendra, K.; Muniyandy, Elangovan; Sindhu, V.; Arunachalam, Krishna Prakash, “Compact LoRa Patch Antenna Optimization Using Dual Random Starfish Aggregation Coupled Transformer Network for Vital Sign Detection in Breast Cancer WBANs,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22162.

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