A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

Authors

  • Saja S Hanoon Maysan _collage of engineering
  • Hasan F Khazal
  • Thamer M Jamel

DOI:

https://doi.org/10.31185/ejuow.Vol12.Iss2.478

Keywords:

Deep learning (DL), minimal mean square error (MMSE), zero forcing (ZF), Recurrent neural network (RNN), convolution neural network (CNN), Pedestrian channel, Vehicular channel, ETU channels.

Abstract

Channel equalization is crucial to the efficiency of wireless network systems. To improve communication reliability and decrease computing complexity, 5G networks have made great progress with the help of deep learning (DL). When applied to 5G and future networks, deep learning has been proven to increase system performance while decreasing computational complexity. ZF is often used to acquire a channel equalizer because of its inexpensive cost and lack of statistical expertise; nonetheless, ZF has a large equalizer error. Because of the limitations of the Minimum Mean Square Error (MMSE) method, deep learning models provide a more convenient solution for solving the channel equalizer issue. Since deep learning may give a better performance-complexity trade-off, it can be used to enhance MMSE and ZF channel equalizers. Its generalization and resilience also make it an attractive tool for use in this context. To address the shortcomings of the ZF and MMSE equalizers, this thesis focuses on developing a DL-based channel equalizer. An Orthogonal Frequency-Division Multiplexing (OFDM)-based single-input-single-output (SISO) system is used to measure the DL-based equalizer's efficacy. Simulation results show that the DL-based channel equalizer can achieve at least a 3 dB gain in SNR over the MMSE equalizer for a SER = 10-3 in low and high selective channel models, respectively, validating the performance of the benchmarked equalizer using different frequency selectivity levels.   Furthermore, the DL-based equalizer results in a drastic decrease in computing complexity in contrast to the ZF and MMSE equalization techniques.

References

S. A. Ghauri, S. Alam, M. F. Sohail, A. Ali and F. Saleem, “Implementation of OFDM and channel estimation using LS and MMSE estimators,” International Journal of Computer and Electronics Research (IJCER), vol. 2, no. 1, pp. 41–46, 2013.

G. Banupriya and D. Thilagavathi, “Features and principles of OFDM : a brief study,” International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), vol. 4, no. 8, pp. 15209–15213, 2016.

A. Kumar, A. Tiwari and R. S. Mishra, “Linear block equalizers in rayleigh fading channel with normalized channel impulse response,” International Journal of Computer Applications, vol. 93, no. 6, pp. 21– 26, 2014

O. Kaiwartya, S. Kumar, and R. Kasana, “Traffic light-based time stable geocast (T-TSG) routing for urban VANETs,” in 2013 Sixth International Conference on Contemporary Computing (IC3), pp. 113–117, Noida, India, 2013.

M. Prasad, Y.-T. Liu, D.-L. Li, C.-T. Lin, R. R. Shah, and O. P. Kaiwartya, “A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule-based system,” Journal of Artificial Intelligence and Soft Computing Research, vol. 7, no. 1, pp. 33–46, 2017.

O. Kaiwartya and S. Kumar, “Geocast routing: recent advances and future challenges in vehicular adhoc networks,” in 2014 International Conference on Signal Processing and Integrated Networks (SPIN), pp. 291–296, Noida, India, 2014.

S. Iqbal, A. H. Abdullah, and K. N. Qureshi, “Channel quality and utilization metric for interference estimation in wireless mesh networks,” Computers Electrical Engineering, vol. 64, pp. 420–435, 2017.

S. Iqbal, H. Maryam, K. N. Qureshi, I. T. Javed, and N. Crespi, “Automised flow rule formation by using machine learning in software defined networks based edge computing,” Egyptian Informatics Journal, 2021.

H. O. Alanazi, A. H. Abdullah, and K. N. Qureshi, “A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care,” Journal of Medical Systems, vol. 41, no. 4, p. 69, 2017.

H. Ye and G. Y. Li, "Initial results on deep learning for joint channel equalization and decoding," in 2017 IEEE 86th vehicular technology conference (VTC-Fall), 2017: IEEE, pp. 1-5.

J. Liu, K. Mei, X. Zhang, D. Ma, and J. Wei, "Online extreme learning machine-based channel estimation and equalization for OFDM systems," IEEE Communications Letters, vol. 23, no. 7, pp. 1276-1279, 2019.

Q. Chen and L. Li, "A Deep Learning Based Equalization Scheme for Bandwidth-compressed Non-orthogonal Multicarrier Communication," Journal of Internet Technology, vol. 22, no. 5, pp. 1001-1009, 2021.

D. Tian, P. Miao, H. Peng, W. Yin, and X. Li, "Volterra-aided neural network equalization for channel impairment compensation in visible light communication system," in Photonics, 2022, vol. 9, no. 11: MDPI, p. 845.

Z. Zhao, M. C. Vuran, F. Guo, and S. D. Scott, "Deep-waveform: A learned OFDM receiver based on deep complex-valued convolutional networks," IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2407-2420, 2021.

S. Katwal and V. Bhatia, "Improved Channel Equalization using Deep Reinforcement Learning and Optimization," EAI Endorsed Transactions on Scalable Information Systems, vol. 9, no. 35, pp. e4- e4, 2022.

A. Logins, J. He, and K. Paramonov, "Block-Structured Deep Learning-Based OFDM Channel Equalization," IEEE Communications Letters, vol. 26, no. 2, pp. 321-324, 2021.

L. Ge, C. Qi, Y. Guo, L. Qian, J. Tong, and P. Wei, "Classification Weighted Deep Neural Network Based Channel Equalization for Massive MIMO-OFDM Systems," Radioengineering, vol. 31, no. 3, p. 347, 2022.

S. Hassan, N. Tariq, R. A. Naqvi, A. U. Rehman, and M. K. Kaabar, "Performance evaluation of machine learning-based channel equalization techniques: new trends and challenges," Journal of Sensors, vol. 2022, pp. 1-14, 2022.

Luo, Fa-Long, ed. "Machine learning for future wireless communications." (2020).‏

Kadhem, Oras Faiz, Thamer Muhammed Jamel, and Hasan Fahad Khazaal. "An Overview for Channel Equalization Techniques in Filter Bank." 2023 Second International Conference on Advanced Computer Applications (ACA). IEEE, 2023.‏

Hanoon, Saja Saleem, Hasan Fahad Khazaal, and Thamer Muhammed Jamel. "Overview on Deep learning aided channel Equalizer Techniques." 2023 Second International Conference on Advanced Computer Applications (ACA). IEEE, 2023.

S. S. Hanoon, T. M. Jamel and H. F. Khazal, "Performance Evaluation of SISO-OFDM Channel Equalization Utilizing Deep Learning," 2023 16th International Conference on Developments in eSystems Engineering (DeSE), Istanbul, Turkiye, 2023, pp. 1-6, doi: 10.1109/DeSE60595.2023.10468848.

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Published

2024-04-01

How to Cite

S Hanoon, S., F Khazal, H. ., & M Jamel, T. . (2024). A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization. Wasit Journal of Engineering Sciences, 12(2), 110-124. https://doi.org/10.31185/ejuow.Vol12.Iss2.478