Enhancing PAPR Reduction in MIMO-OFDM Systems through Grey Wolf Optimization and Hybrid Algorithms

The exponential growth in demand for wireless communication necessitates the continual improvement of transmission efficiency and spectral utilization. Multi-Input, Multi-Output (MIMO) systems have emerged as a promising solution, offering channel capacity enhancement without requiring an expansion of the frequency range. However, the presence of inter-symbol interference poses a significant challenge, which can be effectively mitigated through Orthogonal Frequency Division Multiplexing (OFDM). MIMO-OFDM systems boast numerous advantages, including increased data transfer rates, resilience to multipath fading, improved spectral efficiency, and robust equalization capabilities. Nonetheless, a notable drawback encountered is the increased Peak-to-Average Power Ratio (PAPR), which can impact system performance and efficiency. This research proposes a novel approach to address the PAPR issue in MIMO-OFDM systems by leveraging the Selective Mapping (SLM) technique. Traditionally, SLM has been utilized to reduce PAPR by multiplying the data sequence with a phase sequence. However, the computational complexity associated with this method presents a significant challenge. To tackle this issue, we introduce the integration of Grey Wolf Optimization (GWO) and a hybrid algorithm combining GWO and Artificial Bee Colony (ABC) algorithms. This innovative combination aims to mitigate the computational intricacies inherent in the traditional SLM approach, thereby enhancing the performance of PAPR reduction in MIMO-OFDM systems.