Cover
Vol. 21 No. 1 (2025)

Published: September 19, 2025

Pages: 264-273

Articles

An Enhanced Deployment Approach of Adaptive Equalizer for Multipath Fading Channels

Haider Al-Kanan 1
1Department of Medical Instruments Technology, Al-Kut University College, Wasit, Iraq
History
  • Received: February 10, 2024
  • Revised: August 13, 2024
  • Accepted: October 27, 2024
  • Available Online: May 2, 2025
DOI

https://doi.org/10.37917/ijeee.21.1.25

Abstract

Inter-symbol interference (ISI) exhibits major distortion effect often appears in digital storage and wireless communica- tion channels. The traditional decision feedback equalizer (DFE) is an efficient approach of mitigating the ISI effect using appropriate digital filter to subtract the ISI. However, the error propagation in DFE is a challenging problem that degrades the equalization due to the aliasing distorted symbols in the feedback section of the traditional DFE. The aim of the proposed approach is to minimize the error propagation and improve the modeling stability by incorporating adequate components to control the training and feedback mode of DFE. The proposed enhanced DFE architecture consists of a decision and controller components which are integrated on both the transmitter and receiver sides of communication system to auto alternate the DFE operational modes between training and feedback state based on the quality of the received signal in terms of signal-to-noise ratio SNR. The modeling architecture and performance validation of the proposed DFE are implemented in MATLAB using a raised-cosine pulse filter on the transmitter side and linear time-invariant channel model with additive gaussian noise. The equalizer capability in compensating ISI is evaluated during different operational stages including the training and DFE based on different channel distortion characteristics in terms of SNR using both 0.75 and 1.5 symbol duration in unit delay fraction of FIR filter. The simulation results of eye-diagram pattern showed significant improvement in the DFE equalizer when using a lower unit delay fraction in FIR filter for better suppressing the overlay trails of ISI. Finally, the capability of the proposed approach to mitigate the ISI is improved almost double the number of symbol errors compared to the traditional DFE.

Keywords

References

  1. S. Benedetto and E. Biglieri, “Principles of digital trans- mission, ser,” Information Technology: Transmission, Processing, and Storage. Boston: Kluwer Academic Pub- lishers, 2002.
  2. J. G. Proakis and M. Salehi, Digital communications. 272 | Al-Kanan estimating model coefficients using different channel SNR. McGraw-hill, 2008.
  3. H. Zhao, X. Zeng, X. Zhang, J. Zhang, Y. Liu, and T. Wei, “An adaptive decision feedback equalizer based on the combination of the fir and flnn,” Digital Signal Processing, vol. 21, no. 6, pp. 679–689, 2011.
  4. F. Lounoughi and M. Djendi, “Decision feedback equal- izer based affine projection and normalized least mean square algorithms,” in 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS), pp. 1–6, IEEE, 2023.
  5. G. Malik and A. S. Sappal, “Adaptive equalization algo- rithms: an overview,” International Journal of Advanced Computer Science and Applications, vol. 2, no. 3, 2011.
  6. L. Canese, G. C. Cardarilli, R. La Cesa, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Re, and S. Span`o, “A novel digital equalizer based on rf sampling beyond ghz,” IEEE Access, 2024.
  7. N. Tandon, “Analysis of adaptive equalization tech- niques for burst digital transmission,” in 2023 IEEE 12th International Conference on Communication Sys- tems and Network Technologies (CSNT), pp. 377–383, IEEE, 2023.
  8. M. G¨um¨us¸ and T. M. Duman, “Channel estimation and symbol demodulation for ofdm systems over rapidly varying multipath channels with hybrid deep neural net- works,” IEEE Transactions on Wireless Communica- tions, vol. 22, no. 12, pp. 9361–9373, 2023.
  9. B. Razavi, “The decision-feedback equalizer [a circuit for all seasons],” IEEE Solid-State Circuits Magazine, vol. 9, no. 4, pp. 13–132, 2017.
  10. R. C. Mishra and R. Bhattacharjee, “Performance analy- sis of adaptive dfe using set-membership binormalized data-reusing lms algorithm for frequency selective mimo channels,” AEU-International Journal of Electronics and Communications, vol. 77, pp. 91–99, 2017.
  11. F.-L. Luo, “Machine learning for future wireless com- munications,” 2020.
  12. S. V. Vaseghi, Advanced digital signal processing and noise reduction. John Wiley & Sons, 2008.
  13. J.-J. Jia, K.-C. Lai, and J.-Y. Pan, “Hybrid-domain par- allel decision feedback equalization for single-carrier block transmission,” IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1454–1469, 2017.
  14. N. K. Yadav, A. Dhawan, M. Tiwari, and S. K. Jha, “Modified model of rls adaptive filter for noise cancella- tion,” Circuits, Systems, and Signal Processing, vol. 43, no. 5, pp. 3238–3260, 2024.
  15. M. Yang, S. Shahramian, H. Shakiba, H. Wong, P. Krot- nev, and A. C. Carusone, “Statistical ber analysis of wireline links with non-binary linear block codes sub- ject to dfe error propagation,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 1, pp. 284–297, 2019.
  16. K. Bagadi, C. Ravikumar, K. Sathish, M. Alibakhshike- nari, B. S. Virdee, L. Kouhalvandi, K. N. Olan-Nu˜nez, G. Pau, C. H. See, I. Dayoub, et al., “Detection of sig- nals in mc–cdma using a novel iterative block decision feedback equalizer,” IEEE Access, vol. 10, pp. 105674– 105684, 2022.
  17. V. Ingle, S. Kogon, and D. Manolakis, Statistical and adaptive signal processing. Artech, 2005.
  18. G. Eynard and C. Laot, “Implementation of a blind adap- tive decision feedback equalizer,” in 2006 14th European Signal Processing Conference, pp. 1–5, IEEE, 2006.
  19. A.-S. El-Mahdy, “Adaptive channel estimation and equalization for rapidly mobile communication chan- nels,” IEEE Transactions on Communications, vol. 52, no. 7, pp. 1126–1135, 2004.
  20. L. Ghadei and H. K. Sahoo, “Ber performance analy- sis for outdoor wireless channels using low complexity 273 | Al-Kanan neural equalizer,” in 2024 IEEE 3rd International Con- ference on Control, Instrumentation, Energy & Commu- nication (CIEC), pp. 367–371, IEEE, 2024.
  21. D. Abdelrahman and G. E. Cowan, “Noise analysis and design considerations for equalizer-based optical receivers,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 8, pp. 3201–3212, 2019.