SavedAdd your journal

Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 1

DMOA-Driven Channel Estimation for OFDM: Robust Performance Across Modulation Orders, Pilot Densities and 3GPP Fading Models

Yüksel Tokur Bozkurt(GAZİANTEP İSLAM BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0000-0003-3195-132X
Pages
—
Publication Date
2026-03-24
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This shows DMOA to be a potential method for channel estimation in future OFDM systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This work proposes an innovative channel estimation technique for OFDM systems using the Dwarf Mongoose Optimization Algorithm (DMOA) without requiring any channel statistics.
📊
Key Findings & Data:Simulation results prove that the proposed method consistently performs better than the conventional LS method in all modulation formats (QPSK, 16-QAM, 64-QAM, and 256-QAM), pilot densities (1/3, 1/6, 1/9, and 1/12), and 3GPP channel models (TDLC-300, EPA, EVA, and ETU).
💡
Conclusion & Impact:This shows DMOA to be a potential method for channel estimation in future OFDM systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Channel Estimation · Dwarf Mongoose Optimization Algorithm · Metaheuristic Optimization

Abstract

This work proposes an innovative channel estimation technique for OFDM systems using the Dwarf Mongoose Optimization Algorithm (DMOA) without requiring any channel statistics. In this technique, the DMOA algorithm is used to search for the optimal parameters of the effective SNR, RMS delay spread, and Doppler frequency to minimize the pilot domain estimation error. Unlike the conventional MMSE method, which requires channel correlation matrices to be known in advance, this method adaptively estimates the parameters using the metaheuristic search algorithm. Simulation results prove that the proposed method consistently performs better than the conventional LS method in all modulation formats (QPSK, 16-QAM, 64-QAM, and 256-QAM), pilot densities (1/3, 1/6, 1/9, and 1/12), and 3GPP channel models (TDLC-300, EPA, EVA, and ETU). The results are particularly significant in the medium to high SNR region, in which the LS method shows significant error floor. In addition, the proposed method shows robust results in sparse pilot environments and effectively reduces the degradation caused by increasing pilot spacing. In all scenarios, DMOA achieves near-optimal results compared to the ideal MMSE method without requiring any statistical information. The proposed method also shows better results compared to PSO and DE in terms of stable convergence and reduced estimation error for the entire range of SNR. This shows DMOA to be a potential method for channel estimation in future OFDM systems.

🔖

Cite This Article

Bozkurt, Y. T. (2026). DMOA-Driven Channel Estimation for OFDM: Robust Performance Across Modulation Orders, Pilot Densities and 3GPP Fading Models. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 1. https://doi.org/10.17798/bitlisfen.1831126

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (28)

  1. A. Goldsmith, Wireless Communications, Cambridge: Cambridge University Press, 2005.
  2. Y. Li and G. L. Stuber, Orthogonal Frequency Division Multiplexing for Wireless Communications, Boston, MA: Springer Science & Business Media, 2006.
  3. T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd ed., Upper Saddle River, NJ: Prentice Hall PTR, 2002.
  4. M. Morelli and U. Mengali, “A comparison of pilot-aided channel estimation methods for OFDM systems,” IEEE Trans. Signal Process., vol. 49, no. 12, pp. 3065–3073, 2001.
  5. K. Liu and K. Xing, “Research of MMSE and LS channel estimation in OFDM systems,” in Proc. 2nd Int. Conf. Information Science and Engineering (ICISE), Dec. 2010, pp. 2308–2311.
  6. Y. Zhao and A. Huang, “A novel channel estimation method for OFDM mobile communication systems based on pilot signals and transform-domain processing,” in Proc. IEEE 47th Veh. Technol. Conf. (VTC), vol. 3, May 1997, pp. 2089–2093.
  7. J. J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O. Börjesson, “On channel estimation in OFDM systems,” in Proc. IEEE 45th Veh. Technol. Conf. (VTC), vol. 2, Jul. 1995, pp. 815–819.
  8. O. Edfors, M. Sandell, J. J. van de Beek, S. K. Wilson, and P. O. Börjesson, “OFDM channel estimation by singular value decomposition,” IEEE Trans. Commun., vol. 46, no. 7, pp. 931–939, 1998.
  9. A. R. Bahai, B. R. Saltzberg, and M. Ergen, Multi-Carrier Digital Communications: Theory and Applications of OFDM, Boston, MA: Springer US, 2004.
  10. M. N. Seyman and N. Taşpınar, “Particle swarm optimization for pilot tones design in MIMO-OFDM systems,” EURASIP J. Adv. Signal Process., vol. 2011, Art. no. 10, 2011.
  11. M. N. Seyman and N. Taşpınar, “Pilot tones optimization using artificial bee colony algorithm for MIMO-OFDM systems,” Wireless Pers. Commun., vol. 71, no. 1, pp. 151–163, 2013.
  12. Ş. Şimşir and N. Taşpınar, “Pilot tones design using grey wolf optimizer for OFDM-IDMA system,” Phys. Commun., vol. 25, pp. 259–267, 2017.
  13. Ş. Şimşir and N. Taşpınar, “Advanced pilot design procedure based on HS algorithm for OFDM-IDMA system,” IET Commun., vol. 12, no. 10, pp. 1155–1162, 2018.
  14. N. Taşpınar and Ş. Şimşir, “An efficient technique based on firefly algorithm for pilot design process in OFDM-IDMA system,” Turk. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 817–829, 2018.
  15. N. Taşpınar, A. Ergeç, and B. K. Gül, “Pilot tones design for channel estimation using elephant herding optimization algorithm in massive MIMO systems,” Wireless Pers. Commun., vol. 133, no. 3, pp. 1917–1934, 2023.
  16. H. Ye, G. Y. Li, and B. H. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Commun. Lett., vol. 7, no. 1, pp. 114–117, 2018.
  17. C. Wen, W. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748–751, 2018.
  18. X. S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd ed., Frome, UK: Luniver Press, 2010.
  19. J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press, 1975.
  20. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, vol. 4, 1995, pp. 1942–1948.
  21. M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, 2007.
  22. S. Mirjalili, “Evolutionary algorithms and neural networks,” in Evolutionary Algorithms and Neural Networks, S. Mirjalili, Ed. Cham, Switzerland: Springer, 2019, pp. 43–53.
  23. R. Alghamdi, R. Alhadrami, D. Alhothali, H. Almorad, A. Faisal, S. Helal, and M.-S. Alouini, “Intelligent surfaces for 6G wireless networks: A survey of optimization and performance analysis techniques,” IEEE Access, vol. 8, pp. 202795–202818, 2020.
  24. F. Kara and H. Kaya, “BER performances of downlink and uplink NOMA in the presence of SIC errors over fading channels,” IET Commun., vol. 12, no. 15, pp. 1834–1844, 2018.
  25. C. Knievel, Z. Shi, P. A. Hoeher, and G. Auer, “On particle swarm optimization for MIMO channel estimation,” J. Electr. Comput. Eng., vol. 2012, Art. no. 614384, 2012.
  26. J. Sujitha and K. Baskaran, “Genetic grey wolf optimizer based channel estimation in wireless communication system,” Wireless Pers. Commun., vol. 99, no. 2, pp. 965–984, 2018.
  27. J. O. Agushaka, A. E. Ezugwu, and L. Abualigah, “Dwarf mongoose optimization algorithm,” Comput. Methods Appl. Mech. Eng., vol. 391, Art. no. 114570, 2022.
  28. J. O. Agushaka, A. E. Ezugwu, L. Abualigah, S. K. Alharbi, and H. Alharbi, “Advanced dwarf mongoose optimization for solving CEC 2011 and CEC 2017 benchmark problems,” PLoS One, vol. 17, no. 11, Art. no. e0275346, 2022.