REDUCING PAPR IN OTFS 6G WAVEFORMS USING PARTICLE SWARM OPTIMIZATION-BASED PTS AND SLM TECHNIQUES WITH 64, 256, AND 512 SUB-CARRIERS IN RICIAN AND RAYLEIGH CHANNELS

The search complexity for partial transmit sequence (PTS) and selective mapping (SLM) techniques increases exponentially with the number of sub-blocks, necessitating a comprehensive search over all possible combinations of phase-weighting variables. This paper proposes a novel complex system modeling approach for PTS and SLM in an Orthogonal Time Frequency Space (OTFS) system, utilizing phase factors and a sub-block partition scheme. We describe an OTFS system that achieves low computational complexity in identifying optimal phase-weighting factors and reducing the peak-to-average power ratio (PAPR) using sub-optimal PTS and SLM based on the particle swarm optimization (PSO) algorithm. Parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) were analyzed for 64, 256, and 512 sub-carriers in Rayleigh and Rician channels. The experimental outcome reveals that the proposed approaches can effectively regulate the optimal phase-weighting factors, substantially lessening PAPR with modest complexity. Fractals enhance complex modeling by optimizing PAPR reduction in OTFS 6G waveforms using fractal-influenced PSO for sub-carrier efficiency. The proposed method incorporates fractal modeling to enhance the optimization process in complex environments. Fractals, known for their intricate patterns and self-similarity, provide a robust framework for exploring vast and complex search spaces, crucial in PSO. This approach improves the efficiency of the framework.