A Hybrid RSS-TOA Based Localization for Distributed Indoor Massive MIMO Systems

  • Vankayala Chethan PrakashEmail author
  • G. Nagarajan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)


The advancement of wireless technologies with increasing number of devices has paved way for Massive Multiple Input Multiple Output (MIMO) systems. In 5G technologies, there is an integration of network domains such as Internet of Things, mm-waves, device 2 device (D2D) communications, machine type communications, vehicular networks, cognitive radio networks, etc. With hundreds of antennas at the base station, data rates and capacity increases for devices connected. To enhance quality of service to all connected devices, location identification has its importance. Based on received signal strength (RSS) and time of arrival (TOA) technique, hybrid RSS-TOA based energy detection is proposed. To reduce computational complexity, a channel densification process is designed using energy detector where signals with highest received signal power and arrival of signal at the first- time instance are considered. Performance of the proposed technique is evaluated with Root mean square error and it is compared with cramer rao lower bound. Simulation results show that the probability of detection around 0.80 is achieved for identification of Line of sight (LOS) and Non-Line of Sight (NLOS) conditions .


Energy detection Received signal strength Time of arrival 


  1. 1.
    Garcia, N., Wymeersch, H., Larsson, E., Haimovich, A., Coulon, M.: Direct localization for massive MIMO. IEEE Trans. Signal Process. 65(10), 2475–2487 (2017)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Sun, X., Gao, X., Li, G.Y., Han, W.: Fingerprint based single-site localization for massive MIMO-OFDM Systems. In: IEEE Global Communications Conference, GLOBECOM 2017, pp. 1–7 (2017)Google Scholar
  3. 3.
    Vieira, J., Leitinger, E., Sarajlic, M., Li, X., Tufvesson, F.: Deep convolutional neural networks for massive MIMO fingerprint-based positioning. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp 1–6 (2017)Google Scholar
  4. 4.
    Mendrzik, R., Wymeersch, H., Bauch, G., Abu-Shaban, Z.: Harnessing NLOS components for position and orientation estimation in 5G mmWave MIMO. arXiv preprint arXiv:1712.01445 (2017)
  5. 5.
    Arnold, M., Hoydis, J., ten Brink, S.: Novel massive MIMO channel sounding data applied to deep learning-based indoor positioning. arXiv preprint arXiv:1810.04126 (2018)
  6. 6.
    Mendrzik, R., Wymeersch, H., Bauch, G.: Joint localization and mapping through millimeter wave MIMO in 5G systems-extended version. arXiv preprint arXiv:1804.04417 (2018)
  7. 7.
    Hu, B., Wang, Y., Shi, Z.: Simultaneous position and reflector estimation (SPRE) by single base-station. In: IEEE Wireless Communications and Networking Conference (WCNC) (2018)Google Scholar
  8. 8.
    Prasad, K.N.R.S.V., Hossain, E., Bhargava, V.K., Mallick, S.: Analytical approximation-based machine learning methods for user positioning in distributed massive MIMO. IEEE Access 6, 18431–18452 (2018)CrossRefGoogle Scholar
  9. 9.
    Zeng, T., Chang, Y., Zhang, Q., Hu, M., Li, J.: CNN based LOS/NLOS identification in 3D massive MIMO systems. IEEE Commun. Lett. 22, 1–4 (2018)CrossRefGoogle Scholar
  10. 10.
    Decurninge, A., Ordóñez, L.G., Ferrand, P., Gaoning, H., Bojie, L., Wei, Z., Guillaud, M.: CSI-based outdoor localization for massive MIMO: experiments with a learning approach. arXiv preprint arXiv:1806.07447 (2018)
  11. 11.
    Arnold, M., Dörner, S., Cammerer, S., Brink, S.T.: On deep learning-based massive MIMO indoor user localization. arXiv preprint arXiv:1804.04826 (2018)
  12. 12.
    Kumar, D., Saloranta, J., Destino, G., Tölli, A.: On trade-off between 5G positioning and mmWave communication in a multi-user scenario. In: 8th International Conference on Localization and GNSS (ICL-GNSS), pp. 1–5 (2018)Google Scholar
  13. 13.
    Abu-Shaban, Z., Zhou, X., Abhayapala, T., Seco-Granados, G., Wymeersch, H.: Performance of location and orientation estimation in 5G mmWave systems: uplink vs downlink. In: Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2018)Google Scholar
  14. 14.
    Abu-Shaban, Z., Wymeersch, H., Abhayapala, T., Seco-Granados, G.: Single-anchor two-way localization bounds for 5G mmWave systems: two protocols. arXiv preprint arXiv:1805.02319 (2018)
  15. 15.
    Shahmansoori, A., Uguen, B., Destino, G., Seco-Granados, G., Wymeersch, H.: Tracking position and orientation through millimeter wave lens MIMO in 5G systems. arXiv preprint arXiv:1809.06343 (2018)
  16. 16.
    Prakash, V.C., Nagarajan, G.: Indoor channel characterization with multiple hypothesis testing in massive MIMO. In: Innovative Technologies in Electronics, Information and Communication (INTELINC 2018) (2018)Google Scholar
  17. 17.
    Mailaender, L., Molev-Shteiman, A., Qi, X.-F.: Direct positioning with channel database assistance. In: IEEE International Conference on Communications (ICC Workshops), pp. 1–6 (2018)Google Scholar
  18. 18.
    Li, X., Cai, X., Hei, Y., Yuan, R.: NLOS identification and mitigation based on channel state information for indoor WiFi localization. IET Commun. 11(4), 531–537 (2016)CrossRefGoogle Scholar
  19. 19.
    Chen, C., Chen, Y., Han, Y., Lai, H.-Q., Liu, K.J.R.: Achieving centimeter-accuracy indoor localization on WiFi platforms: a frequency hopping approach. IEEE Internet Things J. 4(1), 111–121 (2017)Google Scholar
  20. 20.
    Sung, C.K., de Hoog, F., Chen, Z., Cheng, P., Popescu, D.C.: Interference mitigation based on bayesian compressive sensing for wireless localization systems in unlicensed band. IEEE Trans. Veh. Technol. 66(8), 7038–7049 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of ECEPondicherry Engineering CollegePondicherryIndia

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