PAPR mitigation in GFDM using an efficient SLM technique based JAYA algorithm

Abstract

Significant research efforts have focused on selective mapping (SLM), a widely adopted technique for reducing the peak-to-average power ratio (PAPR) in multicarrier communication systems. Although integrating SLM with metaheuristic optimization algorithms can significantly improve PAPR reduction, it generally introduces additional computational complexity due to the optimization process. To address this limitation, this paper proposes an improved SLM scheme, referred to as JAYA-SLM, for generalized frequency division multiplexing (GFDM) systems. The proposed approach employs the JAYA algorithm as an efficient phase-factor optimizer within the SLM framework, aiming to achieve an effective trade-off between PAPR reduction and computational complexity. The performance of JAYA-SLM is evaluated through extensive simulations and compared with conventional SLM-based GFDM, as well as SLM schemes assisted by artificial bee colony (ABC), genetic algorithm (GA), and biogeography-based optimization (BBO). Three low-complexity SLM variants are also considered for comparison. Simulation results demonstrate that the proposed JAYA-SLM achieves an average PAPR reduction of approximately 42% with rapid convergence and substantially lower computational complexity than conventional metaheuristic-assisted SLM approaches. Moreover, it provides notable power savings while maintaining an acceptable bit error rate (BER). These results confirm the potential of JAYA-SLM as an efficient and low-complexity PAPR reduction technique for GFDM-based 5G communication systems.