Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency - Arun Kumar; Nishant Gaur; Aziz Nanthaamornphong (Results in Engineering)
The High Peak-to-Average Power Ratio (PAPR) remains a critical challenge in Non-Orthogonal Multiple Access (NOMA) waveforms, particularly with growing subcarrier configurations. Traditional PAPR reduction methods, including clipping, filtering, and pre-coding, tend to compromise the bit error rate (BER), add spectral inefficiency, or incur high computational complexity, rendering them unsuitable for real-time use in 5G and future wireless systems. Current hybrid solutions, such as Genetic Algorithm (GA)-based optimization, provide better performance but are still computationally intensive. To overcome these issues, this paper introduces new Particle Swarm Optimization (PSO)-improved partial transmit sequence (PTS) and Selective Mapping (SLM) schemes that optimally choose phase factors with much lower search complexity. In contrast to the traditional PTS, which involves an exhaustive search, our PSO-based method converges to optimal phase factors efficiently, saving computational overhead while preserving spectral efficiency. Performance evaluation verifies that the proposed scheme results in up to 60% of power savings, a reduction of up to 6.7 dB in PAPR, and a 2–5 dB SNR improvement while maintaining BER and power spectral density (PSD) performance. The low complexity with respect to GA-based techniques and compatibility with time-varying channel conditions make the envisioned scheme highly feasible for next-generation NOMA-based wireless communications.