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Deep Learning Based Channel Estimation and Secure data Transmission Using IEHO-DLNN and MECC Algorithm in Mu-MIMO OFDM System

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Abstract

Channel Estimation (CE) is extremely important in estimating accurately the Channel Impulses Responses (CIR) in disparate conditions. Thus, CE is an extremely vital procedure in the Multiple Users Multiples-Input Multiples-Output–Orthogonal Frequency-Divisions Multiplexing (MU-MIMO-OFDM). However, Inter-Symbols Interferences (ISI) and Inter-Users Interferences (IUI) are the major challenges that the MU-MIMO-OFDM has to face. Obtaining Channel States Information (CSI) is very hard on account of the occurrence of the ISI and IUI in the wireless communication (WC) channel. Conversely, one of the constrictions of MU-MIMO-OFDM is secure signal transmission. To address all these issues, this work proposes a deep learning-centered CE and secures Data Transmission (DT) utilizing IEHO-DCNN and MECC algorithm in the MU-MIMO-OFDM. Initially, the MECC algorithm encrypts the input signals at the transmitter’s side for rendering secure DT. Next, to shun ISI and accurately estimate the CIR, the Cyclic Prefix (CP) along with pilot symbols is interleaved into the signal. The channel is evaluated via IEHO-DLNN. Additionally, the fuzzy-centered priority scheduling is adopted to shun the IUI. It scheduled the manifold users at the received side centred on their waiting time. The proposed work estimates the channel with a minimal cost function, which is experimentally proved via comparing it with prevailing methods.

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Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

CE:

Channel Estimation

CIR:

Channel Impulses Responses

MU-MIMO-OFDM:

Multiple Users Multiples-Input Multiples-Output–Orthogonal Frequency-Divisions Multiplexing

ISI:

Inter-Symbols Interferences

IUI:

Inter-Users Interferences

CSI:

Channel States Information

WC:

Wireless communication

DT:

Data Transmission

CP:

Cyclic Prefix

OFDM:

Orthogonal frequency division multiplexing

BPSK:

Binary Phase Shift Keying

GSM:

Generalized Spatial Modulation

MIMO:

Multiple-inputs multiple-outputs

PSNR:

Peak signal-to-noises ratio

SD:

Signal Decomposition

VMD:

Variational Modes Decomposition

VMF:

Variational Modes Functions

ECC:

Elliptic curves encryption

ISMS:

Information security managements systems

MCS:

Modulated Carrier Signal

IEHO-DLNN:

Improved Elephant Herd Optimization-centered Deep Learning Neural Network

HL:

Hidden Layers

OL:

Output Layers

NN:

Neural Networks

WV:

Weight values

EHO:

Elephant Herding Optimizations

FBPS:

Fuzzy based Priority Scheduling

FL:

Fuzzy Logic

References

  1. Patil, P., Patil, M. R., Itraj, S., & Bomble, U. L. (2017). A review on MIMO OFDM technology basics and more. In IEEE international conference on current trends in computer, electrical, electronics and communication (CTCEEC). https://doi.org/10.1109/CTCEEC.2017.8455114.

  2. Wilzeck, A., & Kaiser, T. (2008). Antenna subset selection for cyclic prefix assisted MIMO wireless communications over frequency selective channels. EURASIP Journal on Advances in Signal Processing. https://doi.org/10.1155/2008/716826

    Article  Google Scholar 

  3. Uwaechia, A. N., Mahyuddin, N. M., Ain, M. F., Latiff, N. M. A., & Za’bah, N. (2019). Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels. Physical Communication., 36, 1–16. https://doi.org/10.1016/j.phycom.2019.100771

    Article  Google Scholar 

  4. Sure, P., & Bhuma, C. M. (2015). A pilot aided channel estimator using DFT based time interpolator for massive MIMO-OFDM systems. AEU-International Journal of Electronics and Communications., 69(1), 321–327.

    Google Scholar 

  5. Hammarberg, P., Rusek, F., & Edfors, O. (2012). (2012) Iterative receivers with channel estimation for multi-user MIMO-OFDM: Complexity and performance. EURASIP Journal on Wireless Communications and Networking., 1, 1–17.

    Google Scholar 

  6. Sure, P., Narendra Babu, C., & Bhuma, C. M. (2018). Large random matrix‐based channel estimation for massive MIMO‐OFDM uplink. IET Communications, 12(9), 1035–1041. https://doi.org/10.1049/iet-com.2017.0854

    Article  Google Scholar 

  7. Kaur, H., Khosla, M, & Sarin, R. K. (2018). Channel estimation in MIMO-OFDM system: A review. In 2018 Second international conference on electronics, communication and aerospace technology (ICECA) pp. 974–980, https://doi.org/10.1109/ICECA.2018.8474747.

  8. Senthilkumar, S., & Geetha Priya, C. (2016). A review of channel estimation and security techniques for CRNS. Automatic Control and Computer Sciences., 50(3), 187–210.

    Article  Google Scholar 

  9. Sharma, D. (2014). Recursive least square technique for channel estimation for transmit diversity case in MIMO-OFDM. International Journal of Computing, 4(4), 18–24.

    Google Scholar 

  10. Sedghi, R., & Azghani, M. (2019). Sparsity-based MIMO interference suppression technique in the presence of imperfect channel state information. IET Communications, 13(19), 3201–3206. https://doi.org/10.1049/iet-com.2019.0420

    Article  Google Scholar 

  11. Qiu, S., Xue, L., & Peng, W. (2018). Improved interference cancelation channel estimation method in OFDM/OQAM system. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/7076967

    Article  Google Scholar 

  12. Faizan Qamar, M. H. D., Hindia, N., Dimyati, K., Noordin, K. A., & Amiri, I. S. (2019). Interference management issues for the future 5G network: a review. Telecommunication Systems, 71(4), 627–643. https://doi.org/10.1007/s11235-019-00578-4

    Article  Google Scholar 

  13. Dineshkumar, T., & Venkatesan, P. (2018). A review of equalizers for frequency selective fading over OFDM channels. International Journal of Advance Research in Science and Engineering, 7(2), 729–733.

    Google Scholar 

  14. Shu, F., Zhu, W., Zhou, X., Li, J., & Jinhui, Lu. (2017). Robust secure transmission of using main-lobe-integration-based leakage beamforming in directional modulation MU-MIMO systems. IEEE Systems Journal, 99, 1–11. https://doi.org/10.1109/JSYST.2017.2764142

    Article  Google Scholar 

  15. Sa'Adah, N., Astawa, I. G. P., & Sudarsono, A. (2017). Asymmetric cryptography for synchronization on mimo-ofdm system, In international electronics symposium on engineering technology and applications (IES-ETA), IEEE, 2017. https://doi.org/10.1109/ELECSYM.2017.8240379.

  16. Zeadally, S., Das, A. K., & Sklavos, N. (2021). Cryptographic technologies and protocol standards for internet of things. Internet of Things, 14, 100075. https://doi.org/10.1016/j.iot.2019.100075

    Article  Google Scholar 

  17. Efstathiou, D., Papadopoulos, G. D., Tsipouridou, D., & Pavlidou, F. N. (2017). Enhancement of transmission security for OFDM based systems, In IEEE symposium on computers and communications (ISCC), IEEE, https://doi.org/10.1109/ISCC.2017.8024585.

  18. Jeya, R., & Amutha, B. (2019). Optimized semiblind sparse channel estimation algorithm for MU-MIMO OFDM system. Computer Communications, 146, 103–109. https://doi.org/10.1016/j.comcom

    Article  Google Scholar 

  19. Yang, Y., Chen, Y., Wang, W., & Yang, G. (2020). Securing channel state information in multiuser mimo with limited feedback. IEEE Transactions on Wireless Communications, 19(5), 3091–3103.

    Article  Google Scholar 

  20. Qin, Q., Gui, L., Cheng, P., & Gong, Bo. (2018). Time-varying channel estimation for millimeter wave multiuser MIMO systems. IEEE Transactions on Vehicular Technology., 67(10), 9435–9448.

    Article  Google Scholar 

  21. Kaur, H., Khosla, M., & Sarin, R. K. (2019). Hybrid Type-2 fuzzy based channel estimation for MIMO-OFDM system with doppler offset influences. Wireless Personal Communications., 108(2), 1131–1143.

    Article  Google Scholar 

  22. Singh, H., & Bansal, S. (2017). Channel estimation with ISFLA based pilot pattern optimization for MIMO OFDM system. AEU-International Journal of Electronics and Communications, 81, 143–149. https://doi.org/10.1016/j.aeue.2017.07.024

    Article  Google Scholar 

  23. Park, S., Choi, J. W., & Seol, J.-Y. (2017). Byonghyo Shim, Expectation-maximization-based channel estimation for multiuser MIMO systems. IEEE Transactions on Communications, 65(6), 2397–2410.

    Article  Google Scholar 

  24. Li, M., Ti, G., & Liu, Q. (2018). Secure beamformer designs in MU-MIMO systems with multiuser interference exploitation. IEEE Transactions on Vehicular Technology, 67(9), 8288–8301. https://doi.org/10.1109/TVT.2018.2841387

    Article  Google Scholar 

  25. Gao, Z., Shen, Di., & Liao, X. (2019). Secrecy enhancement in MIMO-OFDM-IM systems with limited RF chains. Physical Communication, 35, 1–6. https://doi.org/10.1016/j.phycom.2019.100706

    Article  Google Scholar 

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Acknowledgements

We thank the anonymous referees for their useful suggestions.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RC, PER. The first draft of the manuscript was written by RC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Rajeshbabu Chitikena.

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Chitikena, R., Esther Rani, P. Deep Learning Based Channel Estimation and Secure data Transmission Using IEHO-DLNN and MECC Algorithm in Mu-MIMO OFDM System. Wireless Pers Commun 129, 2269–2289 (2023). https://doi.org/10.1007/s11277-023-10172-2

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