Skip to main content
SpringerLink
Log in

Achieving Maximum Sum Spectral Efficiency with Channel Estimation

  • Conference paper
  • First Online:
Advanced Computing and Intelligent Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 218))

Abstract

The major challenges faced by the mobile users are the poor quality of communication and the fast battery drainage. The varying channel characteristics in wireless communication is the main reason for huge signal drops and poor signal quality. The channel estimation plays an important role and is crucial for determining the accuracy of the system. In this paper, the two channel estimation techniques, namely least square (LS) and minimum mean square error (MMSE) are analyzed for multi-antenna communication scenario. The different practical channel scenarios are considered to evaluate the performance of the proposed system in terms of average sum spectral efficiency (SE). It is observed that the maximum average sum SE is obtained with MMSE channel estimation for Rician fading channels. The impact of varying channel statistics with spatial correlation is also evaluated for different pilot reuse factors. Further, the comparison of different receive combiners based on the channel estimates for signal detection is performed in terms of computational complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Erik, G.L., Edfors, O., Tufvesson, F.: Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)

    Article  Google Scholar 

  2. Junyoung, N., Caire, G., Debbah, M.: Capacity scaling of massive MIMO in strong spatial correlation regimes. IEEE Trans. Inf. Theory 66(5), 3040–3064 (2019)

    MathSciNet  MATH  Google Scholar 

  3. Abbas, Al.W., Rizzo, Al.H.: Efficient evaluation of massive MIMO channel capacity. IEEE Syst. J. 14(1), 614–620 (2019)

    Google Scholar 

  4. Jiayi, Z., Chen, S., Lin, Y.: Cell-free massive MIMO: A new next-generation paradigm. IEEE Access 7, 99878–99888 (2019)

    Article  Google Scholar 

  5. Emil, B., Hoydis, J., Sanguinetti, L.: Massive MIMO networks: Spectral, energy, and hardware efficiency. Found. Trends Signal Process. 11(3–4), 154–655 (2017)

    Google Scholar 

  6. Chuili, K., Zhong, C., Matthaiou, M., Zhang, Z.: Performance of downlink massive MIMO in ricean fading channels with ZF precoder. In: IEEE International Conference on Communications (ICC), pp. 1776–1782. IEEE, London (2015)

    Google Scholar 

  7. Apoorva, C., Patel, A., Jagannatham, A.K.: Distributed detection in massive MIMO wireless sensor networks under perfect and imperfect CSI. IEEE Trans. Signal Process. 67(15), 4055–4068 (2019)

    Article  MathSciNet  Google Scholar 

  8. Rajat, T., Karaman, S., Modiano, E.: Improving age of information in wireless networks with perfect channel state information. IEEE/ACM Trans. Networking 28(4), 1765–1778 (2020)

    Article  Google Scholar 

  9. Trinh, V.C., Mollén, C., Björnson, E.: Large-scale-fading decoding in cellular massive MIMO systems with spatially correlated channels. IEEE Trans. Commun. 67(4), 2746–2762

    Google Scholar 

  10. Yu, H., Jin, S., Wen, C.K.: Channel estimation for extremely large-scale massive MIMO systems. IEEE Wirel. Commun. Lett. 9(5), 633–637 (2020)

    Article  Google Scholar 

  11. Dian, F., Gao, F., Liu, Y., Deng, Y., Wang, G., Zhong, Z.: Angle domain channel estimation in hybrid millimeter wave massive MIMO systems. IEEE Trans. Wireless Commun. 17(12), 8165–8179 (2018)

    Article  Google Scholar 

  12. Amin, K., Minn, H.: On channel estimation for massive MIMO with pilot contamination. IEEE Commun. Lett. 19(9), 1660–1663 (2015)

    Article  Google Scholar 

  13. Peng, X., Wang, J., Qi, F.: Analysis and design of channel estimation in multicell multiuser MIMO OFDM systems. IEEE Trans. Veh. Technol. 64(2), 610–620 (2014)

    Article  Google Scholar 

  14. Yanrong, Z., Zhao, W., Wang, G., Ai, B., Putra, H.H., Juliyanto, B.: AoA-based channel estimation for massive MIMO OFDM communication system on high speed rails. China Commun. 17(3), 90–100 (2020)

    Article  Google Scholar 

  15. Mingming, F., Jin, H.: An improved channel estimation algorithm based on DFT in OFDM systems. In: IEEE International Conference on Computer Information and Big data Applications, pp. 321–325. IEEE (2020)

    Google Scholar 

  16. Zhao, Y., Zou, W.: A novel NE-DFT channel estimation scheme for milli-meter wave massive mimo vehicular communication. IEEE Access 8, 74965–74976 (2020)

    Article  Google Scholar 

  17. Zhaocheng, W., Zhao, P., Qian, C., Chen, S.: Location-aware channel estimation enhanced TDD based massive MIMO. IEEE Access 7(4), 7828–7840 (2016)

    Google Scholar 

  18. Xiao, W., Peng, W., Chen, D., Jiang, T.: Joint channel parameter estimation in multi-cell massive MIMO system. IEEE Trans. Commun. 67(5), 3251–3264 (2018)

    Google Scholar 

  19. Cheng, Q., Fu, X., Sidiropoulos, N.D.: Algebraic channel estimation algorithms for FDD massive MIMO systems. IEEE J. Selected Topics Signal Process. 13(5), 961–973 (2019)

    Article  Google Scholar 

  20. Evangelos, V., Alexandropoulos, G.C., Thompson, J.: Wideband MIMO channel estimation for hybrid beamforming millimeter wave systems via random spatial sampling. IEEE J. Select. Topics Signal Process. 13(5), 1136–1150 (2019)

    Article  Google Scholar 

  21. Zhen, G., Hu, C., Dai, L., Wang, Z.: Channel Estimation for millimeter-wave massive MIMO with hybrid precoding over frequency-selective fading channels. IEEE Commun. Lett. 20(6), 1259–1262 (2016)

    Article  Google Scholar 

  22. Marzetta, T. L., Larsson, E. G., Yang, H.: Fundamentals of Massive MIMO. Cambridge University Press (2016)

    Google Scholar 

  23. Hong, Y., Marzetta, T.L.: Massive MIMO with max-min power control in line-of-sight propagation environment. IEEE Trans. Commun. 65(11), 4685–4693 (2017)

    Article  Google Scholar 

  24. David, T., Viswanath, P.: Fundamentals of Wireless Communications. Cambridge University Press (2005)

    Google Scholar 

  25. Qi, Z., Jin, S., Wong, K.-K., Zhu, H., Matthaiou, M.: Power scaling of uplink massive MIMO systems with arbitrary-rank channel means. IEEE J. Select. Topics Signal Process. 8(5), 966–981 (2014)

    Article  Google Scholar 

  26. Chuil, K., Zhong, C., Matthaiou, M., Zhang, Z.: Performance of downlink massive MIMO in Ricean fading channels with ZF precoder. In: IEEE International Conference on Communications (ICC), pp. 1776–1782. IEEE (2015)

    Google Scholar 

  27. Yeqing, H., Hong, Y., Evans, J.: Angle-of-arrival-dependent interference modeling in Rician massive MIMO. IEEE Trans. Veh. Technol. 66(7), 6171–6183 (2017)

    Article  Google Scholar 

  28. Lou, Z., Yang, T., Geraci, G., Yuan, J.: Downlink multiuser massive MIMO in Rician channels under pilot contamination. In: IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)

    Google Scholar 

  29. Liang, W., Zhang, Z., Dang, J., Wang, J., Liu, H., Wu, F.: Channel estimation for multicell multiuser massive MIMO uplink over Rician fading channels. IEEE Trans. Veh. Technol. 66(10), 8872–8882 (2017)

    Article  Google Scholar 

  30. Luca, S., Kammoun, A., Debbah, M.: Asymptotic analysis of multicell massive MIMO over Rician fading channels. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3539–3543. IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India

    Ashu Taneja

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Ankita Rana & Nitin Saluja

Authors
  1. Ashu Taneja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ankita Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nitin Saluja
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

  2. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  3. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  4. School of Electrical and Electronic Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Rabindra Nath Shaw

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Taneja, A., Rana, A., Saluja, N. (2022). Achieving Maximum Sum Spectral Efficiency with Channel Estimation. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_49

Download citation

Publish with us

Policies and ethics

Search

Navigation