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Neural Network-Based Receiver in MIMO-OFDM System for Multiuser Detection in UWA Communication

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Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 185))

Abstract

High quality of service and high transmission rate is the demand for future underwater acoustic (UWA) communication which is achieved through the implementation of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the UWA communication. However, the quality of the MIMO-OFDM system has faced multiaccess interference (MAI) at the receiver due to the interference from co-channel users. Therefore, multiuser detection (MUD) technique is needed at the receiver of the MIMO-OFDM system to suppress the effect of MAI. The novelty in this research is that MUD is achieved using multilayer perceptron (MLP)-based neural network (NN) detector at the receiver of the MIMO-OFDM system in the UWA communication. The MLPNN detector achieved the MUD at the receiver of the MIMO-OFDM system through the adaptation of NN weights and bias weights in the backpropagation (BP) algorithm. The transceiver model of the MIMO-OFDM system in underwater is implemented using BELLHOP simulation system. The bit error rate (BER) performance of the MLPNN detector towards MUD is analysed and is compared with that of existing detectors (matched filter (MF) detector, decorrelating detector (DD), minimum mean square error (MMSE) detector, and multistage conventional parallel interference cancellation (PIC) detector) in the UWA network. Proposed MLPNN detector outperforms in BER analysis over existing detectors in the UWA network.

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References

  1. Qu, F., Wang, Z., Yang, L., Wu, Z.: A journey toward modeling and resolving doppler in underwater acoustic communications. IEEE Commun. Mag. 54(2), 49–55 (2016)

    Google Scholar 

  2. Khan, M.R., Das, B., Pati, B.B.: Channel estimation strategies for underwater acoustic (UWA) communication: an overview. J. Franklin Inst. 357(11), 7229–7265 (2020)

    Google Scholar 

  3. Verd, S. Minimum probability of error for asynchronous Gaussian multiple-access channels. IEEE Trans. Inform. Theor. 32(1), 85–96 (1986)

    Google Scholar 

  4. Yan, T.C.: Spatially multiplexed CDMA multiuser underwater acoustic communications. IEEE J. Ocean Eng. 41(1), 217–231 (2016)

    Article  Google Scholar 

  5. Cho, S.E., Song, H.C., Hodgkiss, W.S.: Successive interference cancellation for underwater acoustic communications. IEEE J. Oceanic Eng. 36(4), 490–501 (2011)

    Article  Google Scholar 

  6. Ma, L., Zhou, S., Qiao, G., Liu, S., Zhou, F.: Superposition coding for downlink underwater acoustic OFDM. IEEE J. Ocean. Eng. 42(1), 175–187 (2017)

    Google Scholar 

  7. Yeo, H.K., Sharif, B.S., Adams, A.E., Hinton, O.R.: Implementation of multiuser detection strategies for coherent underwater acoustic communication. IEEE J. Oceanic Eng. 27, 17–27 (2002)

    Article  Google Scholar 

  8. Shabnam, R., Sofiene, A.: ANN-based detection in MIMO-OFDM systems with low-resolution ADCs. Signal Processing (eess.SP). arXiv: 2001.11643 (2020)

    Google Scholar 

  9. Li, L., Hou, H., Meng, W.: Convolutional neural network based detection algorithm for uplink multiuser massive MIMO systems. IEEE Access 8, 64250–64265 (2020)

    Article  Google Scholar 

  10. Chen, Z., He, Z., Niuet, K.: Neural network-based symbol detection in high-speed ofdm underwater acoustic communication. In: 2018 10th International Conference on wireless Communications and Signal Processing (WCSP), pp. 1–5 (2018)

    Google Scholar 

  11. Zhang, Y., Li, J., Zakharov, Y., Li, X., Li, J.: Deep learning based underwater acoustic OFDM communications. Appl. Acoust. 154, 53–58 (2019)

    Google Scholar 

  12. Raleigh, G.G., Cioffi, J.M.: Spatio–temporal coding for wireless communications. IEEE Trans. Commun. 46, 357–366 (1998)

    Google Scholar 

  13. Zhang, Y.J., Lataief, K.B.: An Efficient resource allocation scheme for spatial multiuser access in MIMO/OFDM systems. IEEE Trans. Commun. 53, 107–116 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, VSS University of Technology, Burla, Odisha, India

    Md Rizwan Khan & Bikramaditya Das

Authors
  1. Md Rizwan Khan
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  2. Bikramaditya Das
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Corresponding author

Correspondence to Bikramaditya Das .

Editor information

Editors and Affiliations

  1. School of computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Siba K. Udgata

  2. IGIT Sarang, Dhenkanal, Odisha, India

    Srinivas Sethi

  3. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Satish N. Srirama

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Khan, M.R., Das, B. (2021). Neural Network-Based Receiver in MIMO-OFDM System for Multiuser Detection in UWA Communication. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_43

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