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Channel Estimation for Sparse mm-Wave MIMO System

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Abstract

The fifth-generation (5G) cellular networks will provide gigabit-per-second data rates from massive antenna array combined with the emerging use of large and unexploited millimeter wave (mm-Wave) bands (30–300 GHz) in small cells. Channel estimation for sparse mm-Wave MIMO systems is a difficult task. This is because of a large number of coefficients to be estimated, lower scattering nature, and blockage of mm-Wave by many materials in the environment. This paper will be the opportunity to implement the sparse channel estimation in the 5G cellular networks. In this work, we propose compressed-sensing (CS) based solutions and implements hybrid MIMO architecture for the proposed algorithm, OMP algorithm, and oracle estimator with different mm-Wave MIMO setups. Simulation results show that as compared to the OMP algorithm, proposed algorithm requires 16.9 times less computation time, and significant improvement is seen in normalized mean squared error (NMSE). Also, in the analysis, we found that the performance of the hybrid MIMO approaches near-optimal to conventional fully digital precoder.

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Data Availability

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

Code availability

Custom code in MATLAB has been used for simulation.

References

  1. Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., & Gutierrez, F. (2013). Millimeter wave mobile communications for 5G cellular: It will work! IEEE Access, 1, 335–349. https://doi.org/10.1109/ACCESS.2013.2260813

    Article  Google Scholar 

  2. Ghosh, A., Member, S., Thomas, T. A., Cudak, M. C., Ratasuk, R., Moorut, P., & Member, S. (2014). Millimeter-wave enhanced local area systems: A high-data-rate approach for future wireless networks. IEEE Journal on Selected Areas in Communications, 32(6), 1152–1163.

    Article  Google Scholar 

  3. Rangan, S., Rappaport, T. S., & Erkip, E. (2014). Millimeter-wave cellular wireless networks: Potentials and challenges. In Proceedings of the IEEE (Vol. 102, pp. 366–385). https://doi.org/10.1109/JPROC.2014.2299397

  4. Xiao, M., Mumtaz, S., Huang, Y., Dai, L., Li, Y., Matthaiou, M., Karagiannidis, G. K., Bjornson, E., Yang, K., Chih-Lin, I., & Ghosh, A. (2017). Millimeter wave communications for future mobile networks. IEEE Journal on Selected Areas in Communications, 35(9), 1909–1935.

    Article  Google Scholar 

  5. Siddiqi, M. A., Yu, H., & Joung, J. (2019). 5G ultra-reliable low-latency communication implementation challenges and operational issues with IoT devices. Electronics (Switzerland), 8(9), 1–18. https://doi.org/10.3390/electronics8090981

    Article  Google Scholar 

  6. Sahoo, B. P. S., Chou, C. C., Weng, C. W., & Wei, H. Y. (2019). Enabling millimeter-wave 5g networks for massive IoT applications: A closer look at the issues impacting millimeter-waves in consumer devices under the 5g framework. IEEE Consumer Electronics Magazine, 8(1), 49–54. https://doi.org/10.1109/MCE.2018.2868111

    Article  Google Scholar 

  7. Siddiqi, M. A., Yu, H., & Joung, J. (2019). 5G ultra-reliable low-latency communication implementation challenges and operational issues with IoT devices. Electronics, 8(9), 981.

    Article  Google Scholar 

  8. Hemadeh, I. A., Satyanarayana, K., El-Hajjar, M., & Hanzo, L. (2018). Millimeter-wave communications: Physical channel models, design considerations, antenna constructions, and link-budget. IEEE Communications Surveys and Tutorials, 20(2), 870–913. https://doi.org/10.1109/COMST.2017.2783541

    Article  Google Scholar 

  9. Busari, S. A., Huq, K. M. S., Mumtaz, S., Dai, L., & Rodriguez, J. (2018). Millimeter-wave massive MIMO communication for future wireless systems: A survey. IEEE Communications Surveys and Tutorials, 20(2), 836–869. https://doi.org/10.1109/COMST.2017.2787460

    Article  Google Scholar 

  10. Ahmed, I., Khammari, H., Shahid, A., Musa, A., Kim, K. S., De Poorter, E., & Moerman, I. (2018). A survey on hybrid beamforming techniques in 5G: Architecture and system model perspectives. IEEE Communications Surveys and Tutorials, 20(4), 3060–3097. https://doi.org/10.1109/COMST.2018.2843719

    Article  Google Scholar 

  11. Uwaechia, A. N., & Mahyuddin, N. M. (2020). A comprehensive survey on millimeter wave communications for fifth-generation wireless networks: Feasibility and challenges. IEEE Access, 8, 62367–62414. https://doi.org/10.1109/ACCESS.2020.2984204

    Article  Google Scholar 

  12. Niu, Y., Li, Y., Jin, D., Su, L., & Vasilakos, A. V. (2015). A survey of millimeter wave communications (mmWave) for 5G: Opportunities and challenges. Wireless Networks, 21(8), 2657–2676. https://doi.org/10.1007/s11276-015-0942-z

    Article  Google Scholar 

  13. Björnson, E., Larsson, E. G., & Marzetta, T. L. (2016). Massive MIMO: Ten myths and one critical question. IEEE Communications Magazine, 54(2), 114–123. https://doi.org/10.1109/MCOM.2016.7402270

    Article  Google Scholar 

  14. Sohrabi, F., & Yu, W. (2016). Hybrid digital and analog beamforming design for large-scale antenna arrays. IEEE Journal on Selected Topics in Signal Processing, 10(3), 501–513. https://doi.org/10.1109/JSTSP.2016.2520912

    Article  Google Scholar 

  15. Lim, S. H., Won Choi, J., & Shim, B. (2019). Greedy Sparse Channel Estimation for Millimeter Wave Communications. In IEEE Region 10 annual international conference, proceedings/TENCON (Vol. 2018-Octob, pp. 1628–1632). IEEE. https://doi.org/10.1109/TENCON.2018.8650065

  16. Lee Swindlehurst, A., Ayanoglu, E., Heydari, P., & Capolino, F. (2014). Millimeter-wave massive MIMO: The next wireless revolution? IEEE Communications Magazine, 52(9), 56–62.

    Article  Google Scholar 

  17. Wang, X., Kong, L., Kong, F., Qiu, F., Xia, M., Arnon, S., & Chen, G. (2018). Millimeter wave communication: A comprehensive survey. IEEE Communications Surveys and Tutorials, 20(3), 1616–1653. https://doi.org/10.1109/COMST.2018.2844322

    Article  Google Scholar 

  18. Liu, X., Li, X., Cao, S., Deng, Q., Ran, R., Nguyen, K., & Tingrui, P. (2019). Hybrid precoding for massive mmWave MIMO systems. IEEE Access, 7(c), 33577–33586. https://doi.org/10.1109/ACCESS.2019.2903166

    Article  Google Scholar 

  19. Tsai, C. R., Liu, Y. H., & Wu, A. Y. (2018). Efficient compressive channel estimation for millimeter-wave large-scale antenna systems. IEEE Transactions on Signal Processing, 66(9), 2414–2428. https://doi.org/10.1109/TSP.2018.2811742

    Article  MathSciNet  MATH  Google Scholar 

  20. Crespo Marques, E., Maciel, N., Naviner, L., Cai, H., & Yang, J. (2019). A review of sparse recovery algorithms. IEEE Access, 7, 1300–1322. https://doi.org/10.1109/ACCESS.2018.2886471

    Article  Google Scholar 

  21. Khan, I., & Singh, D. (2018). Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems. AEU - International Journal of Electronics and Communications, 89, 181–190. https://doi.org/10.1016/j.aeue.2018.03.038

    Article  Google Scholar 

  22. Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2017). Overview of millimeter wave communications for fifth-generation (5G) wireless networks—With a focus on propagation models. IEEE Transactions on Antennas and Propagation, 65(12), 6213–6230.

    Article  Google Scholar 

  23. Alkhateeb, A., El Ayach, O., Leus, G., & Heath, R. W. (2014). Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE Journal on Selected Topics in Signal Processing, 8(5), 831–846. https://doi.org/10.1109/JSTSP.2014.2334278

    Article  Google Scholar 

  24. Schniter, P., & Sayeed, A. (2015). Channel estimation and precoder design for millimeter-wave communications: The sparse way. Conference Record - Asilomar Conference on Signals, Systems and Computers, 2015-April(5), 273–277. https://doi.org/10.1109/ACSSC.2014.7094443

  25. Lee, J., Gil, G. T., & Lee, Y. H. (2016). Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave communications. IEEE Transactions on Communications, 64(6), 2370–2386. https://doi.org/10.1109/TCOMM.2016.2557791

    Article  Google Scholar 

  26. Rusu, C., Mendez-Rial, R., Gonzalez-Prelcic, N., & Heath, R. W. (2016). Low complexity hybrid precoding strategies for millimeter wave communication systems. IEEE Transactions on Wireless Communications, 15(12), 8380–8393. https://doi.org/10.1109/TWC.2016.2614495

    Article  Google Scholar 

  27. Cheng, X., Wang, M., & Li, S. (2017). Compressive sensing-based beamforming for millimeter-wave OFDM systems. IEEE Transactions on Communications, 65(1), 371–386. https://doi.org/10.1109/TCOMM.2016.2616390

    Article  MathSciNet  Google Scholar 

  28. Nguyen, D. H. N., Le, L. B., Le-Ngoc, T., & Heath, R. W. (2017). Hybrid MMSE precoding and combining designs for mmWave multiuser systems. IEEE Access, 5, 19167–19181. https://doi.org/10.1109/ACCESS.2017.2754979

    Article  Google Scholar 

  29. Chopra, S., & Kakkar, A. (2021). Capacity analysis of hybrid MIMO using sparse signal processing in mmW 5G heterogeneous wireless networks. Wireless Personal Communications, 116(3), 2651–2673. https://doi.org/10.1007/s11277-020-07815-z

    Article  Google Scholar 

  30. Ayach, O. E., Rajagopal, S., Abu-Surra, S., Pi, Z., & Heath, R. W. (2014). Spatially sparse precoding in millimeter wave MIMO systems. IEEE Transactions on Wireless Communications, 13(3), 1499–1513. https://doi.org/10.1109/TWC.2014.011714.130846

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

    Naresh Purohit & Namit Gupta

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  1. Naresh Purohit
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Correspondence to Naresh Purohit.

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Purohit, N., Gupta, N. Channel Estimation for Sparse mm-Wave MIMO System. Wireless Pers Commun 129, 2123–2140 (2023). https://doi.org/10.1007/s11277-023-10227-4

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