An Improved Hybrid GA-PSO-Based PTS Scheme for PAPR Reduction in OFDM Systems

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : Kadiatou Balde, Elijah Mwangi, Nicholas Oyie

10.14445/23488549/IJECE-V13I1P105

How to Cite?

Kadiatou Balde, Elijah Mwangi, Nicholas Oyie, "An Improved Hybrid GA-PSO-Based PTS Scheme for PAPR Reduction in OFDM Systems," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 55-63, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P105

Abstract:

Orthogonal Frequency Division Multiplexing (OFDM) has become one of the most widely used modern wireless communications as a result of its inherent capabilities of efficiently utilizing spectrum and resisting multipath fading. High Peak-to-Average Power Ration (PAPR) is one of the significant challenges of OFDM systems, resulting in power efficiency reduction and signal quality degradation. This paper presents an enhanced Partial Transmit Sequence (PTS) technique that leverages a hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) to lower PAPR while keeping computational demands low effectively. By combining the broad exploration capability of GA with the fast convergence of PSO, the hybrid method efficiently identifies optimal phase factors. MATLAB simulations with 10,000 OFDM frames and 16-QAM modulation show that the GA-PSO approach achieves PAPR reduction comparable to conventional PTS, outperforming GA-PTS and PSO-PTS alone. The method reached a PAPR of 5.16 dB at a CCDF of 10-4 with only 1200 iterations, demonstrating its practicality and efficiency for OFDM systems.

Keywords:

Genetic Algorithm, Orthogonal Frequency Division Multiplexing, Partial Transmit Sequence, Peak to Average Power Ratio, Particle Swarm Optimization.

References:

[1] Yasir Rahmatallah, and Seshadri Mohan, “Peak-To-Average Power Ratio Reduction in OFDM Systems: A Survey and Taxonomy,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 1567-1592, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Hyun-Seung Joo et al., “New PTS Schemes for PAPR Reduction of OFDM Signals without Side Information,” IEEE Transactions on Broadcasting, vol. 63, no. 3, pp. 562-570, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xingsi Xueet al., “A Novel Partial Sequence Technique Based Chaotic Biogeography Optimization for PAPR Reduction in GFDM Waveform,” Heliyon, vol. 9, no. 9, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Taewon Hwang et al., “OFDM and Its Wireless Applications: A Survey,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 1673-1694, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jing Yan, Jianping Wang, and Zhen He, “A Modified Scheme for PAPR Reduction in OFDM System Based on Clipping Method,” Conference Proceeding of the 6th International Workshop on Multiple Access Communications Conference, Vilnius, Lithuania, pp. 1-7, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[6] G. Kranthi Kumar, S. Lenin Kumar Reddy, and K. Santhosh Kumar, “PAPR Reduction of OFDM Using an Exponential Companding Technique,” International Journal of Engineering and Computer Science, vol. 4, no. 4, pp. 11182-11187, 2015.
[Google Scholar] [Publisher Link]
[7] Saruti Gupta, and Ashish Goel, “Improved Selected Mapping Technique for Reduction of PAPR in OFDM Systems,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11, no. 10, pp. 117-122, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Hongmei Wang et al., “Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization,” Mathematics, vol. 12, no. 1, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yazhou Yuan et al., “Adaptive PTS Scheme Based on Fuzzy Neural Network for PAPR Reduction in OFDM System,” Digital Signal Processing, vol. 126, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mehdi Hosseinzadeh Aghdam, and Abbas Ali Sharifi, “PAPR Reduction in OFDM Systems: An Efficient PTS Approach based on Particle Swarm Optimization,” ICT Express, vol. 5, no. 3, pp. 178-181, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Taba Somia et al., “A New Hybrid IF-PSO Algorithm Based PTS Technique for PAPR Mitigation in OFDM Systems,” Conference: International Conference on Advances in Electrical and Computer Engineering (ICAECE'20230), Tebessa, Algeria, 2023.
[Publisher Link]
[12] Yajun Wang, Wen Chen, and Chintha Tellambura, “A PAPR Reduction Method based on Artificial Bee Colony Algorithm for OFDM Signals,” IEEE Transactions on Wireless Communications, vol. 9, no. 10, pp. 2994-2999, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Taba Somiaet al., “SCA-GWO: A Hybrid Optimization Method Based on the PTS Technique for PAPR Mitigation in OFDM Systems,” SETSCI Conference Proceedings of the Cognitive Models and Artificial Intelligence Conference, Ankara, Türkiye, vol. 15, pp. 104-109, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zeyid T. Ibraheem et al., “PTS Method with Combined Partitioning Schemes for Improved PAPR Reduction in OFDM System,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 12, no. 11, pp. 7845-7853, 2014.
[Google Scholar] [Publisher Link]
[15] Yuh-ren Tsai, and Sin-jhih Huang, “PTS with Non-Uniform Phase Factors for PAPR Reduction in OFDM Systems,” IEEE Communications Letters, vol. 12, no. 1, pp. 20-22, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ahmed G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 29, pp. 2531-2561, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Bushra Alhijawi, and Arafat Awajan, “Genetic Algorithms: Theory, Genetic Operators, Solutions, and Applications,” Evolutionary Intelligence, vol. 17, pp. 1245-1256, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Xumei Fan et al., “Review and Classification of Bio-inspired Algorithms and Their Applications,” Journal of Bionic Engineering, vol. 17, pp. 611-631, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Tareq M. Shami et al., “Particle Swarm Optimization: A Comprehensive Surveys,” IEEE Access, vol. 10, pp. 10031-10061, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jingzhong Fang et al., “A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization,” International Journal of Network Dynamics and Intelligence, vol. 2, no. 1, pp. 24-50, 2023.
[​​​​​​​CrossRef] [Google Scholar] [Publisher Link]