Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency
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 (...
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Elsevier
2025-06-01
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| author | Arun Kumar Nishant Gaur Aziz Nanthaamornphong |
| author_facet | Arun Kumar Nishant Gaur Aziz Nanthaamornphong |
| author_sort | Arun Kumar |
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| description | 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. |
| format | Article |
| id | doaj-art-24dc84e48e4341be88381b5f28a03d9f |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-24dc84e48e4341be88381b5f28a03d9f2025-08-20T03:10:34ZengElsevierResults in Engineering2590-12302025-06-012610489310.1016/j.rineng.2025.104893Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power EfficiencyArun Kumar0Nishant Gaur1Aziz Nanthaamornphong2Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, IndiaDepartment of Physics, JECRC University, Jaipur, IndiaCollege of Computing, Prince of Songkla University, Phuket, Thailand; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2590123025009697PAPR reduction5G NOMA waveformsParticle Swarm Optimization (PSO)Partial Transmit Sequences (PTS)Selective Mapping (SLM)computational complexity |
| spellingShingle | Arun Kumar Nishant Gaur Aziz Nanthaamornphong Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency Results in Engineering PAPR reduction 5G NOMA waveforms Particle Swarm Optimization (PSO) Partial Transmit Sequences (PTS) Selective Mapping (SLM) computational complexity |
| title | Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency |
| title_full | Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency |
| title_fullStr | Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency |
| title_full_unstemmed | Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency |
| title_short | Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency |
| title_sort | reducing papr in noma waveforms using genetic enhanced pts and slm a low complexity approach for improved throughput power spectral density and power efficiency |
| topic | PAPR reduction 5G NOMA waveforms Particle Swarm Optimization (PSO) Partial Transmit Sequences (PTS) Selective Mapping (SLM) computational complexity |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025009697 |
| work_keys_str_mv | AT arunkumar reducingpaprinnomawaveformsusinggeneticenhancedptsandslmalowcomplexityapproachforimprovedthroughputpowerspectraldensityandpowerefficiency AT nishantgaur reducingpaprinnomawaveformsusinggeneticenhancedptsandslmalowcomplexityapproachforimprovedthroughputpowerspectraldensityandpowerefficiency AT aziznanthaamornphong reducingpaprinnomawaveformsusinggeneticenhancedptsandslmalowcomplexityapproachforimprovedthroughputpowerspectraldensityandpowerefficiency |