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@article{174299,
        author = {R. S. Monika and T. A. K. Chaitanya and K. Prashanth kumar and K. Rojamani},
        title = {Design and Optimization of a Terahertz Antenna for 6G Applications using CSRR and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3452-3458},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174299},
        abstract = {The rapid evolution of sixth-generation (6G) wireless networks necessitates high-performance terahertz (THz) antennas with optimized design parameters. This paper presents the design and performance enhancement of a THz antenna by incorporating a Complementary Split Ring Resonator (CSRR) on the ground plane to improve return loss and impedance matching. A dataset of various design parameters is generated and used to train a machine learning model, enabling the prediction of optimal configurations. The proposed methodology reduces design iterations and enhances antenna efficiency. Simulation results show that before applying machine learning, the CSRR-based antenna achieved a return loss of -39.6 dB at 3.6–3.8 THz. After optimization, the return loss was -32.7 dB at 1.6–1.7 THz, indicating a trade-off between impedance matching and frequency tuning. These findings highlight the potential of ML-driven antenna design for next-generation wireless communication systems.},
        keywords = {Antenna design, machine learning optimization, return loss, terahertz (THz) communication.},
        month = {March},
        }