Authors : G. V. Subrahmanyam; K. Lakshmi; B. Chandu; G. Sai Kumar; K. Harshitha; V. V. S. S. N. Veerreddy

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/3d46m8x3

Scribd : https://tinyurl.com/4ddk7bxc

DOI : https://doi.org/10.38124/ijisrt/26May077

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Abstract : Orthogonal Frequency Division Multiplexing (OFDM) is widely adopted in modern broadband wireless systems due to its high spectral efficiency and robustness against frequency‑selective fading. However, the inherently high peak‑to‑average power ratio (PAPR) of OFDM signals induces nonlinear distortion in high‑power amplifiers, degrading bit error rate (BER) and spectral efficiency. Selected mapping (SLM) and partial transmit sequence (PTS) are distortion‑free PAPR‑reduction schemes that trade‑off complexity and redundancy for improved envelope statistics. Conventional SLM‑PTS hybrids often incur excessive computational load or require exhaustive search over large phase‑vector spaces .This paper proposes an adaptive hybrid SLM‑PTS technique enhanced by Grey Wolf Optimization (GWO) for PAPR reduction in OFDM systems. The method combines SLM‑type phase rotations on a subset of sub‑blocks with a PTS‑like weighted combination stage whose phase vector is optimized by an adaptive GWO engine. Detailed mathematical modeling is provided for the OFDM signal, PAPR, SLM and PTS transformations, and the GWO search dynamics. Simulation results show that the proposed scheme reduces the PAPR from 7.88 dB (original OFDM) to 4.61dB at a complementary cumulative distribution function (CCDF) level of 10{−3} , outperforming SLM (6.79 dB), PTS (5.25dB), and conventional hybrid SLM‑PTS (5.25dB) while maintaining negligible BER degradation. A comprehensive complexity analysis verifies that the adaptive GWO‑based framework achieves superior PAPR‑performance with significantly lower computational overhead than exhaustive hybrid SLM‑PTS .

Keywords : OFDM, PAPR Reduction, Selected Mapping (SLM), Partial Transmit Sequence (PTS), Hybrid SLM‑PTS, Grey Wolf Optimization (GWO), Adaptive Optimization, BER, Computational Complexity.

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