PAPR Reduction in MIMO-OFDM Using Refracted Opposition-Based Archerfish Hunting Optimization Algorithm

The combination of the Multiple-Input Multiple-Output technique with Orthogonal Frequency Division Multiplexing (OFDM) is a widely used technique to improve Quality of Service (QoS) in wireless communication. However, a high Peak-to-Average Power Ratio (PAPR) in OFDM leads to signal distortion when passing through a High-Power Amplifier (HPA) into its nonlinear operating region. Moreover, this distortion degrades the OFDM system performance by increasing the Bit Error Rate (BER). To address these problems, a Refracted Opposition-Based Archerfish Hunting Optimization (ROB-AHO) Algorithm is proposed to minimize PAPR in OFDM systems. The AHO Algorithm dynamically adapts to various scenarios, and the proposed ROBL helps AHO select the most suitable phase factors to minimize PAPR efficiently across operating scenarios. Experimental results demonstrate that the ROB-AHO achieved a BER of 3!!!x.7; ×!!!x 10;⁻¹ for Signal-to-Noise Ratio (SNR) at 10 dB that outperforms prior methods, namely the Asymmetrical Auto Encoder (AAE).