SCA-GWO: A Hybrid Optimization Method Based on the PTS Technique for PAPR Mitigation in OFDM Systems

Résumé

This work suggests a newly developed partial transmit sequence (PTS) approach consisting of the hybridization of two efficient algorithms, namely, the grey wolf optimization (GWO) algorithm and the sine cosine algorithm (SCA). The SCAGWO-based PTS technique is investigated to conquer the exponentially increasing computational load of the ordinary PTS technique. The SCA searches for an initial global solution provided to the GWO to increase the wolves’ diversity, emphasize exploration, and, thus, avoid less efficient local solutions. The combined SCA-GWO is a balanced optimization variant used in the PTS technique to search for a salient combination of phase factors, which diminishes the peak-to-average power ratio (PAPR) effectively within a very reduced search complexity. The obtained results prove the superiority of the investigated approach in terms of PAPR mitigation and computational load. The SCA-GWO technique minimizes the orthogonal frequency division multiplexing (OFDM) system's PAPR by a rate of 46.07% while representing only 7.32% of the PTS’ complexity. A comparison with the state-of-the-art techniques also demonstrates the effective performance of the proposed SCA-GWO in lowering PAPR levels. The SCA-GWO outperforms the whale optimization algorithm (WOA), the genetic algorithm (GA), the harmony search optimization (HSA), and the ant lions optimization (ALO).

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