Design analysis and performance enhancement of a 2-element MIMO implantable antenna using multi-relational Hamiltonian quantum nutcracker generative graph adversarial attention networks for medical applications

Abstract The rapid advancements in wearable and implantable biomedical technologies have led to an increased demand for high-performance, miniaturized antennas capable of facilitating real-time health monitoring and seamless wireless data transmission. Conventional implantable antennas face challenges like mutual coupling, signal degradation, high SAR, and limited radiation efficiency, affecting medical communication in applications like ECG monitoring and neurostimulation. This study introduces a novel 2-element MIMO implantable antenna optimized using a Multi-Relational Hamiltonian Quantum Nutcracker Generative Graph Adversarial Attention Network (MR-HQNG-GAANet). The optimization framework integrates multi-relational graph learning with quantum-assisted adversarial tuning, enhancing radiation performance, impedance matching, and energy efficiency. By leveraging quantum-assisted adversarial tuning and multi-relational graph learning, the proposed design significantly reduces SAR levels while enhancing radiation efficiency, ensuring safe operation and robust wireless performance in implantable biomedical environments. Performance evaluation demonstrates superior characteristics, including a reflection coefficient of radiation efficiency of 86.4 % at 2.4 GHz and 81.2 % at 5.8 GHz, mutual coupling below −18 dB, −28.9 dB at 5.8 GHz, −32.6 dB at 2.4 GHz, and a max gain of −14.7dBi. Safety standards set by the FDA and IEEE (≤ 1.6 W/kg in 1g tissue) are met by SAR compliance, ensuring safe usage. The proposed optimization framework enhances impedance bandwidth, link reliability, and energy efficiency in wearable and implantable biomedical sensors, incorporating quantum-assisted graph learning for reliable data transmission.