Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

Kah Jing Wong, Filbert H Juwono, Regina Reine

Abstract


The goal of 6G communication networks requires higher transmission speeds, tremendous data processing, and low-latency communication. Orthogonal frequency-division multiplexing (OFDM), which is widely utilized in 5G communication systems, may be a viable alternative for 6G. It significantly reduces inter symbol interference (ISI) in the frequency-selective fading environment. Channel estimation is critical in OFDM to optimize system performance. Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions. The simulation results show that the signal bit error (SER) is equivalent to and better than that of the minimum mean squared error (MMSE) and least square (LS) methods.


Keywords


OFDM, machine learning, deep learning, channel estimation

Full Text:

PDF

References


A. Goldsmith, S. A. Jafar, N. Jindal, and S. Vishwanath, “Capacity limits of MIMO channels,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 5, pp. 684–702, Jun. 2003, doi: 10.1109/JSAC.2003.810294.

“Fundamentals of Wireless Communication - David Tse, Pramod Viswanath - Google Books.” https://books.google.com.my/books?hl=en&lr=&id=66XBb5tZX6EC&oi=fnd&pg=PR15&dq=Fundamentals+of+wireless+communication&ots=0cvEvpQwYE&sig=rncg8GtEQAaDTKiiGOl6KlZ0CPc&redir_esc=y#v=onepage&q=Fundamentals%20of%20wireless%20communication&f=false (accessed Mar. 28, 2022).

G. L. Stüber, J. R. Barry, S. W. Mclaughlin, Y. E. Li, M. A. Ingram, and T. G. Pratt, “Broadband MIMO-OFDM wireless communications,” Proceedings of the IEEE, vol. 92, no. 2, pp. 271–293, 2004, doi: 10.1109/JPROC.2003.821912.

P. Patil, M. R. Patil, S. Itraj, and U. L. Bomble, “A Review on MIMO OFDM Technology Basics and More,” International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, pp. 119–124, Sep. 2018, doi: 10.1109/CTCEEC.2017.8455114.

J. Armstrong, “OFDM for optical communications,” Journal of Lightwave Technology, vol. 27, no. 3, pp. 189–204, Feb. 2009, doi: 10.1109/JLT.2008.2010061.

M. A. Lema et al., “Business Case and Technology Analysis for 5G Low Latency Applications,” IEEE Access, vol. 5, pp. 5917–5935, 2017, doi: 10.1109/ACCESS.2017.2685687.

F. H. Juwono and R. Reine, “Future OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches,” Green Intelligent Systems and Applications, vol. 1, no. 1, pp. 19–25, Nov. 2021, doi: 10.53623/GISA.V1I1.34.

R. Farhat, Y. Mourali, M. Jemni, and H. Ezzedine, “An overview of machine learning technologies and their use in e-learning,” Proceedings of 2020 International Multi-Conference on: Organization of Knowledge and Advanced Technologies, OCTA 2020, Feb. 2020, doi: 10.1109/OCTA49274.2020.9151758.

S. Ray, “A Quick Review of Machine Learning Algorithms,” Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, pp. 35–39, Feb. 2019, doi: 10.1109/COMITCON.2019.8862451.

W. Liu et al., “Deep Learning Methods in Communication Systems: A Review You may also like Screen-Camera Communication System Based on Dynamic QR Code Deep Learning Methods in Communication Systems: A Review,” p. 12024, 2020, doi: 10.1088/1742-6596/1617/1/012024.

H. Ye, G. Y. Li, and B. H. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114–117, Feb. 2018, doi: 10.1109/LWC.2017.2757490.

S. Hong, Y. Zhang, Y. Wang, H. Gu, G. Gui, and H. Sari, “Deep Learning-Based Signal Modulation Identification in OFDM Systems,” IEEE Access, vol. 7, pp. 114631–114638, 2019, doi: 10.1109/ACCESS.2019.2934976.

M. Soltani, V. Pourahmadi, A. Mirzaei, and H. Sheikhzadeh, “Deep Learning-Based Channel Estimation,” IEEE Communications Letters, vol. 23, no. 4, pp. 652–655, Apr. 2019, doi: 10.1109/LCOMM.2019.2898944.

M. S. A. Hassan and R. B. Ahmad, “Variable pilot channels estimation based on blocktype and comb-type pilot arrangement in OFDM system,” 2016 3rd International Conference on Electronic Design, ICED 2016, pp. 99–102, Jan. 2017, doi: 10.1109/ICED.2016.7804615.

“MIMO-OFDM Wireless Communications with MATLAB - Yong Soo Cho, Jaekwon Kim, Won Y. Yang, Chung G. Kang - Google Books.” https://books.google.com.my/books?hl=en&lr=&id=6HwAoeuMr3kC&oi=fnd&pg=PR7&dq=MIMO-OFDM+wireless+communications+with+MATLAB&ots=mEU42cONUS&sig=RmSJeYhQJ92_4D5YP7K5w-rUXsI&redir_esc=y#v=onepage&q=MIMO-OFDM%20wireless%20communications%20with%20MATLAB&f=false (accessed Mar. 28, 2022).

H. Ye, G. Y. Li, and B. H. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114–117, Feb. 2018, doi: 10.1109/LWC.2017.2757490.

K. Liu and K. Xing, “Research of MMSE and LS channel estimation in OFDM systems,” 2nd International Conference on Information Science and Engineering, ICISE2010 - Proceedings, pp. 2308–2311, 2010, doi: 10.1109/ICISE.2010.5688562.

A. R. James, R. S. Benjamin, S. John, T. M. Joseph, V. Mathai, and S. S. Pillai, “Channel estimation for OFDM systems,” 2011 - International Conference on Signal Processing, Communication, Computing and Networking Technologies, ICSCCN-2011, pp. 587–591, 2011, doi: 10.1109/ICSCCN.2011.6024619.

P. Ongsulee, “Artificial intelligence, machine learning and deep learning,” International Conference on ICT and Knowledge Engineering, pp. 1–6, Jan. 2018, doi: 10.1109/ICTKE.2017.8259629.

A. Goel, C. Tung, Y. H. Lu, and G. K. Thiruvathukal, “A Survey of Methods for Low-Power Deep Learning and Computer Vision,” IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings, Jun. 2020, doi: 10.1109/WF-IOT48130.2020.9221198.

A. R. Sharma and P. Kaushik, “Literature survey of statistical, deep and reinforcement learning in natural language processing,” Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017, vol. 2017-January, pp. 350–354, Dec. 2017, doi: 10.1109/CCAA.2017.8229841.

B. A. Jebur, S. H. Alkassar, M. A. M. Abdullah, and C. C. Tsimenidis, “Efficient machine learning-enhanced channel estimation for OFDM systems,” IEEE Access, vol. 9, pp. 100839–100850, 2021, doi: 10.1109/ACCESS.2021.3097436.

Y. Duan, “An object recognition method based on deep learning,” 2021 International Conference of Optical Imaging and Measurement, ICOIM 2021, pp. 257–261, Aug. 2021, doi: 10.1109/ICOIM52180.2021.9524342.

D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, Dec. 2014, doi: 10.48550/arxiv.1412.6980.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015. doi: 10.1038/nature14539.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222–2232, Oct. 2017, doi: 10.1109/TNNLS.2016.2582924.

S. Wang, R. Yao, T. A. Tsiftsis, N. I. Miridakis, and N. Qi, “Signal Detection in Uplink Time-Varying OFDM Systems Using RNN with Bidirectional LSTM,” IEEE Wireless Communications Letters, vol. 9, no. 11, pp. 1947–1951, Nov. 2020, doi: 10.1109/LWC.2020.3009170.

J. Gonzalez and W. Yu, “Non-linear system modeling using LSTM neural networks,” IFAC-PapersOnLine, vol. 51, no. 13, pp. 485–489, Jan. 2018, doi: 10.1016/J.IFACOL.2018.07.326.

B. Hrnjica and O. Bonacci, “Lake Level Prediction using Feed Forward and Recurrent Neural Networks,” Water Resources Management, vol. 33, no. 7, pp. 2471–2484, May 2019, doi: 10.1007/s11269-019-02255-2.




DOI: http://dx.doi.org/10.26418/elkha.v14i1.53962

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 ELKHA

Editorial Office/Publisher Address:
Editor Jurnal Elkha, Department of Electrical Engineering, Faculty of Engineering, Universitas Tanjungpura,
Jl. Prof. Dr. Hadari Nawawi, Pontianak 78124, Indonesia

website : http://jurnal.untan.ac.id/index.php/Elkha
email : jurnal.elkha@untan.ac.id

ORCID iD : https://orcid.org/0000-0002-0779-1277

Assiciated with :

MoU FORTEI - ELKHA

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.