Development and enhancement of metamaterial-inspired Ag-GaAs THz MIMO antenna with optimized diversity metrics using data-driven machine learning algorithms for future 6G networks

Abstract

The MIMO antenna design is specifically engineered to support optimized performance in emerging 6G networks. Utilizing advanced techniques such as metamaterials and machine learning algorithms, the antenna system achieves high data rates, improved diversity, and robust signal reliability, making it ideal for next-generation ultra-fast and intelligent wireless communication technologies. Our advanced metamaterial configuration demonstrates high gain and bandwidth. A low ECC value 0.0004 shows minimal correlation, ensuring better signal diversity and improved system performance. Similarly, a high diversity gain confirms the antenna's efficiency in maintaining robust signal reception under varying conditions. The CCL values of 0.0916 bits/Hz bits/Hz provide insight into the information-carrying capacity of the MIMO configuration. The MIMO antenna design achieves a maximum gain of 8.9 dBi and a wide bandwidth of 30 THz. This performance is attained through a combination of parametric optimization and machine learning techniques, enhancing both efficiency and operational range. The machine learning algorithms used for optimization yield a high R-2 value of 0.99, indicating excellent prediction accuracy. The proposed antenna, featuring metamaterial characteristics, demonstrates strong potential for next-generation 6G networks, offering enhanced performance, efficiency, and compact design integration.