Hybrid Optimization Framework for Minimizing Return Loss in Graphene-Based Antennas for Next-Generation Wireless Communication

Resumo

Graphene antennas have received extensive attention as promising technologies for terahertz and next-generation 6G wireless communication networks due to their extraordinary electrical tunability, high carrier mobility, and advantageous electromagnetic properties. However, for most of the existing designs, there is a major challenge: return loss is often high; operational bandwidth often is narrow; and impedance performance may vary under changing conditions. There also exists a gap in a computationally efficient optimization technique that is generally adaptable to complex, high-order engineered design criteria. In light of these challenges, this research proposes an intelligent hybrid optimization framework for the design and optimization of high-performance graphene antennas for efficient THz communication. The framework combines data-driven predictive modeling with iterative optimization to minimize return loss, maximize gain, and maximize radiation efficiency, considering stability in multiband operation in complex design configurations. The Intelligent Hybrid Optimization Framework is, for the first time, validated in the results of the simulations, returning a maximum gain of 9.86 dBi, an average radiation efficiency of 93.06%, and a return loss of -47.55 dB at a predictive accuracy of R² = 0.9999. The findings of this research suggest that the proposed approach significantly reduces the computer-aided optimization design time and cost and facilitates a scalable and intelligent platform for broadband, low-loss, energy-efficient graphene antennas for next-generation THz and 6G communication systems.