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Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework

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Abstract

Channel estimation is essential in a Multiple Input Multiple Output (MIMO) wireless communication in 5G. In the MIMO system, numerous antennas are utilized on the sender and receiver sides for enhancing spectral efficiency and reliability. The channel estimation can improve the exactness of the received signal. Increasing the number of channel coefficients can make channel estimation fairly complex. Transmitting multiple paths have some delay and signal echoes. Therefore, channel assessment is very necessary for efficiently receiving the transmitted signals. A novel Enhanced Convolution Neural African Buffalo approach was developed for channel estimation purposes to overcome such issues. Moreover, the additive white Gaussian noise channel is created for MIMO communication. The simulation of this research is done using the network simulator platform. Sequentially, the proposed system outcomes are compared with other techniques in terms of throughput, accuracy, signal to noise ratio, mean square error, and bit error rate. The comparison results also proved the effectiveness of the proposed approach.

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References

  1. Almalki, F.A., Angelides, M.C.: An enhanced design of a 5G MIMO antenna for fixed wireless aerial access. Cluster Comput. (2021). https://doi.org/10.1007/s10586-021-03318-z

    Article  Google Scholar 

  2. Khairi, S., Raouyane, B., Bellafkih, M.: Novel QoE monitoring and management architecture with eTOM for SDN-based 5G networks. Cluster Comput. 23, 1–12 (2020). https://doi.org/10.1007/s10586-018-02903-z

    Article  Google Scholar 

  3. Clement, J.C., Indira, N., Vijayakumar, P., Nandakumar, R.: Deep learning based modulation classification for 5G and beyond wireless systems. Peer-to-Peer Netw. Appl. 14, 319–332 (2021). https://doi.org/10.1007/s12083-020-01003-3

    Article  Google Scholar 

  4. Prakasam, P., Sayeed, M.S., Ajayan, J.: Guest editorials: P2P computing for 5G, beyond 5G (B5G) networks and internet-of-everything (IoE). Peer-to-Peer Netw. Appl. 14, 240–242 (2021). https://doi.org/10.1007/s12083-020-01001-5

    Article  Google Scholar 

  5. Khan, W.U., Li, X., Ihsan, A., Ali, Z., Elhalawany, B.M., Sidhu, G.A.S.: Energy efficiency maximization for beyond 5G NOMA-enabled heterogeneous networks. Peer-to-Peer Netw. Appl. (2021). https://doi.org/10.1007/s12083-021-01176-5

    Article  Google Scholar 

  6. Liu, Z., Zhang, L., Ding, Z.: Overcoming the channel estimation barrier in massive MIMO communication via deep learning. IEEE Wirel. Commun. 27(5), 104–111 (2020). https://doi.org/10.1109/MWC.001.1900413

    Article  Google Scholar 

  7. Chun, C., Kang, J., Kim, I.: Deep learning-based channel estimation for massive MIMO systems. IEEE Wirel. Commun. Lett. 8(4), 1228–1231 (2019). https://doi.org/10.1109/LWC.2019.2912378

    Article  Google Scholar 

  8. Jin, Y., Zhang, J., Jin, S., Ai, B.: Channel estimation for cell-free mmWave massive MIMO through deep learning. IEEE Trans. Veh. Technol. 68(10), 10325–10329 (2019). https://doi.org/10.1109/TVT.2019.2937543

    Article  Google Scholar 

  9. Rao, K.P., Sridevi, P.V.: An adaptive LMS estimation for channel variant condition in real time communication. In: 2013 Annual IEEE India Conference (INDICON), pp. 1–5 (2013). https://doi.org/10.1109/INDCON.2013.6725866

  10. Ke, M., Gao, Z., Wu, Y., Gao, X., Schober, R.: Compressive sensing-based adaptive active user detection and channel estimation: massive access meets massive MIMO. IEEE Trans. Signal Process. 68, 764–779 (2020). https://doi.org/10.1109/TSP.2020.2967175

    Article  MathSciNet  Google Scholar 

  11. Feng, J., Yang, C., Hou, J., Long, H., Chen, S.: Performance enhancement for indoor visible light communication system with an improved inter-symbol interference model using optimized hemispherical optical-angle-diversity-receivers. Opt. Commun. 454, 124488 (2020). https://doi.org/10.1016/j.optcom.2019.124488

    Article  Google Scholar 

  12. Hosney, M., Selmy, H.A.I., Srivastava, A., Elsayed, K.M.F.: Interference mitigation using angular diversity receiver with efficient channel estimation in MIMO VLC. IEEE Access 8, 54060–54073 (2020). https://doi.org/10.1109/ACCESS.2020.2981137

    Article  Google Scholar 

  13. Rao, K.P., Sridevi, P.V.: Estimation coding based on non-stationary feature for MIMO OFDM system. In: 2016 International Conference on Inventive Computation Technologies (ICICT), pp. 1–5 (2016). https://doi.org/10.1109/INVENTIVE.2016.7830168

  14. Fang, X., Suo, Z., Zhang, L., Zhang, Q., Zhang, F.: Combined phase offset channel estimation method for optical OFDM/OQAM. Opt. Fiber Technol. 61, 102390 (2021). https://doi.org/10.1016/j.yofte.2020.102390

    Article  Google Scholar 

  15. Nandi, S., Pathak, N.N., Nandi, A.: A novel adaptive optimized fast blind channel estimation for cyclic prefix assisted space-time block coded MIMO-OFDM systems. Wirel. Pers. Commun. 115, 1317–1333 (2020). https://doi.org/10.1007/s11277-020-07629-z

    Article  Google Scholar 

  16. Khan, I., Singh, D.: Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems. AEU Int J. Electron. Commun. 89, 181–190 (2018). https://doi.org/10.1016/j.aeue.2018.03.038

    Article  Google Scholar 

  17. Shen, X., Liao, Y., Dai, X., Zhao, M., Liu, K., Wang, D.: Joint channel estimation and decoding design for 5G-enabled V2V channel. China Commun. 15(7), 39–46 (2018). https://doi.org/10.1109/CC.2018.8424581

    Article  Google Scholar 

  18. Chuang, Y., Ueng, F.B., Shen, Y.S.: A cyclic prefix free multiple input multiple output generalized frequency division multiplexing system design. Wirel. Pers. Commun. 117, 311–336 (2021). https://doi.org/10.1007/s11277-020-07870-6

    Article  Google Scholar 

  19. Lopez-Morales, M.J., Chen-Hu, K., Garcia-Armada, A.: Differential data-aided channel estimation for up-link massive SIMO-OFDM. IEEE Open J. Commun. Soc. 1, 976–989 (2020). https://doi.org/10.1109/OJCOMS.2020.3008634

    Article  Google Scholar 

  20. Balevi, E., Doshi, A., Jalal, A., Dimakis, A., Andrews, J.G.: High dimensional channel estimation using deep generative networks. IEEE J. Sel. Areas Commun. 39(1), 18–30 (2021). https://doi.org/10.1109/JSAC.2020.3036947

    Article  Google Scholar 

  21. Albataineh, Z., Hayajneh, K., Salameh, H.B., Dang, C., Dagmseh, A.: Robust massive MIMO channel estimation for 5G networks using compressive sensing technique. AEU Int. J. Electron. Commun. 120, 153197 (2020). https://doi.org/10.1016/j.aeue.2020.153197

    Article  Google Scholar 

  22. Xu, L., Qian, C., Gao, F., Zhang, W., Ma, S.: Angular domain channel estimation for mmWave massive MIMO with one-bit ADCs/DACs. IEEE Trans. Wirel. Commun. 20(2), 969–982 (2021). https://doi.org/10.1109/TWC.2020.3029400

    Article  Google Scholar 

  23. Balevi, E., Doshi, A., Andrews, J.G.: Massive MIMO channel estimation with an untrained deep neural network. IEEE Trans. Wirel. Commun. 19(3), 2079–2090 (2020). https://doi.org/10.1109/TWC.2019.2962474

    Article  Google Scholar 

  24. Bhandari, R., Jadhav, S.: Novel spectral efficient technique for MIMO-OFDM channel estimation with reference to PAPR and BER analysis. Wirel. Pers. Commun. 104, 1227–1242 (2019). https://doi.org/10.1007/s11277-018-6077-7

    Article  Google Scholar 

  25. Sheikh, J.A., Mustafa, F., Sidiq, S., Parah, S.A., Bhat, G.M.: A new optimization technique in massive MIMO and LSAS using hybrid architecture and channel estimation algorithm for 5G networks. Wireless Pers. Commun. 120, 771–785 (2021). https://doi.org/10.1007/s11277-021-08489-x

    Article  Google Scholar 

  26. Liu, C., Liu, X., Ng, D.W.K., Yuan, J.: Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications. IEEE Trans. Wirel. Commun. (2021). https://doi.org/10.1109/TWC.2021.3100148

    Article  Google Scholar 

  27. Melo, D.M.V., Landau, L.T.N., de Lamare, R.C.: Zero-crossing precoding with maximum distance to the decision threshold for channels with 1-bit quantization and oversampling. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5120–5124 (2020). https://doi.org/10.1109/ICASSP40776.2020.9053632

  28. Jeong, W., Jung, J., Wang, Y., Wang, S., Yang, S., Yan, Q., Yi, Y., Kim, S.M.: SDR receiver using commodity wifi via physical-layer signal reconstruction. In: MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, No. 32, pp. 1–14 (2020). https://doi.org/10.1145/3372224.3419189

  29. Xiao, L., Chen, D., Hemadeh, I.A., Xiao, P., Jiang, T.: Graph theory assisted bit-to-index-combination gray coding for generalized index modulation. IEEE Trans. Wirel. Commun. 19(12), 8232–8245 (2020). https://doi.org/10.1109/TWC.2020.3020692

    Article  Google Scholar 

  30. Kumar, M.A., Siddaiah, P.: Spectral efficiency enhancement of green metric cognitive radio network using novel channel design and intellectual African buffalo optimization. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03159-z

    Article  Google Scholar 

  31. Huang, S., Qiu, D., Tran, T.D.: Bayesian massive MIMO channel estimation with parameter estimation using low-resolution ADCs. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2021). https://doi.org/10.1109/ICASSP39728.2021.9413477

  32. Chen, L., Liu, A., Yuan, X.: Structured turbo compressed sensing for massive MIMO channel estimation using a Markov prior. IEEE Trans. Veh. Technol. 67(5), 4635–4639 (2018). https://doi.org/10.1109/TVT.2017.2787708

    Article  Google Scholar 

  33. Rao, K.P., Sridevi, P.V.: An integrated cross layer approach for throughput improvement in wireless sensor networks. In: 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–6 (2016). https://doi.org/10.1109/ISCO.2016.7727114

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India

    Prabhakara Rao Kapula

  2. Department of Electronics and Communication Engineering, A U College of Engineering, Visakhapatnam, Andhra Pradesh, India

    P. V. Sridevi

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  1. Prabhakara Rao Kapula
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Correspondence to Prabhakara Rao Kapula.

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Kapula, P.R., Sridevi, P.V. Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework. Cluster Comput 25, 3517–3530 (2022). https://doi.org/10.1007/s10586-022-03587-2

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