Peak-to-Average Power Ratio Reduction in PLC-OFDM Systems Using Genetic Algorithm

In Power Line Communication (PLC) systems, long training sequences are essential for channel estimation and synchronization, ensuring reliable and accurate data transmission. However, these sequences often lead to high Peak-to-Average Power Ratio (PAPR), which increases power consumption and induces nonlinear distortion, thereby degrading system performance. To address this issue, this study proposes a Genetic Algorithm (GA)-based approach to optimize the PAPR of long training sequences. The proposed method achieves a suboptimal yet highly effective reduction in PAPR, balancing computational efficiency and performance improvement. Experimental results demonstrate that the GA-based approach significantly mitigates PAPR while maintaining the robustness of channel estimation and synchronization, offering a practical and efficient solution for enhancing the performance of PLC systems.