A Novel Distributive Population-Based Differential Evolution Algorithm for SLM Scheme to Reduce PAPR in Massive MIMO-OFDM Systems

To achieve the ever rising demand for high data rates with high spectral efficiency, the energy efficient massive Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) will be essential and useful for future wireless communication systems as well as Industrial network applications to reduce the operational costs significantly. But the high Peak to Average Power Ratio (PAPR) in a massive MIMO-OFDM system is identified as a critical issue that causes the non-linear signal distortion and restricts the efficiency of the power amplifier. Among different PAPR reduction schemes, Selected Mapping (SLM) is a distortionless technique and highly attractive due to its simple implementation capability. However, the conventional SLM scheme is inefficient for the massive MIMO-OFDM antenna system for its enormous Side Information (SI) burden and large computational complexity to search the best set of phase factors. In this paper, a Distributive Population based Switching Differential Evolution strategy is incorporated in the SLM scheme which eventually enhances the searching ability. The algorithm is employed in different antenna grouping (homogeneous and heterogeneous) based massive MIMO systems. The antenna grouping concept in the massive MIMO system minimizes the SI burden. The proposed algorithm for SLM techniques outperforms the existing techniques in terms of PAPR, Bit Error Rate, SI burden, energy efficiency and computational complexity.