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Spectrum Efficiency and BER Analysis of Massive MIMO Systems with QR-RLS Channel Estimation Technique

Tamanna Rajput1 , Munna Lal Jatav2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-10 , Page no. 64-68, Oct-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i10.6468

Online published on Oct 31, 2019

Copyright © Tamanna Rajput, Munna Lal Jatav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Tamanna Rajput, Munna Lal Jatav, “Spectrum Efficiency and BER Analysis of Massive MIMO Systems with QR-RLS Channel Estimation Technique,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.64-68, 2019.

MLA Style Citation: Tamanna Rajput, Munna Lal Jatav "Spectrum Efficiency and BER Analysis of Massive MIMO Systems with QR-RLS Channel Estimation Technique." International Journal of Computer Sciences and Engineering 7.10 (2019): 64-68.

APA Style Citation: Tamanna Rajput, Munna Lal Jatav, (2019). Spectrum Efficiency and BER Analysis of Massive MIMO Systems with QR-RLS Channel Estimation Technique. International Journal of Computer Sciences and Engineering, 7(10), 64-68.

BibTex Style Citation:
@article{Rajput_2019,
author = {Tamanna Rajput, Munna Lal Jatav},
title = {Spectrum Efficiency and BER Analysis of Massive MIMO Systems with QR-RLS Channel Estimation Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2019},
volume = {7},
Issue = {10},
month = {10},
year = {2019},
issn = {2347-2693},
pages = {64-68},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4895},
doi = {https://doi.org/10.26438/ijcse/v7i10.6468}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.6468}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4895
TI - Spectrum Efficiency and BER Analysis of Massive MIMO Systems with QR-RLS Channel Estimation Technique
T2 - International Journal of Computer Sciences and Engineering
AU - Tamanna Rajput, Munna Lal Jatav
PY - 2019
DA - 2019/10/31
PB - IJCSE, Indore, INDIA
SP - 64-68
IS - 10
VL - 7
SN - 2347-2693
ER -

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Abstract

The fifth generation of mobile communication systems (5G) promises unprecedented levels of connectivity and quality of service (QoS) to satisfy the incessant growth in the number of mobile smart devices and the huge increase in data demand. One of the primary ways 5G network technology will be accomplished is through network densification, namely increasing the number of antennas per site and deploying smaller and smaller cells. Massive MIMO, where MIMO stands for multiple-input multiple-output, is widely expected to be a key enabler of 5G. This technology leverages an aggressive spatial multiplexing, from using a large number of transmitting/receiving antennas, to multiply the capacity of a wireless channel. Cell-free massive MIMO refers to a massive MIMO system where the BS antennas, herein referred to as access points (APs), are geographically spread out. The APs are connected, through a fronthaul network, to a central processing unit (CPU) which is responsible for coordinating the coherent joint transmission. Such a distributed architecture provides additional macro-diversity, and the co-processing at multiple APs entirely suppresses the inter-cell interference. In order to overcome the above effects, the work focuses on the QR-RLS based channel estimation method for cell free Massive MIMO systems.

Key-Words / Index Term

Massive MIMO, Channel State Information, Square Root-Recursive Least Square (QR-RLS)

References

[1] Supraja Eduru and Nakkeeran Rangaswamy, “BER Analysis of Massive MIMO Systems under Correlated Rayleigh Fading Channel”, 9th ICCCNT IEEE 2018, IISC, Bengaluru, India.
[2] H. Q. Ngo A. Ashikhmin H. Yang E. G. Larsson T. L. Marzetta "Cell-free massive MIMO versus small cells" IEEE Trans. Wireless Commun. vol. 16 no. 3 pp. 1834-1850 Mar. 2017.
[3] Huang A. Burr "Compute-and-forward in cell-free massive MIMO: Great performance with low backhaul load" Proc. IEEE Int. Conf. Commun. (ICC) pp. 601-606 May 2017.
[4] H. Al-Hraishawi, G. Amarasuriya, and R. F. Schaefer, “Secure communication in underlay cognitive massive MIMO systems with pilot contamination,” in In Proc. IEEE Global Commun. Conf. (Globecom), pp. 1–7, Dec. 2017.
[5] V. D. Nguyen et al., “Enhancing PHY security of cooperative cognitive radio multicast communications,” IEEE Trans. Cognitive Communication And Networking, vol. 3, no. 4, pp. 599–613, Dec. 2017.
[6] R. Zhao, Y. Yuan, L. Fan, and Y. C. He, “Secrecy performance analysis of cognitive decode-and-forward relay networks in Nakagami-m fading channels,” IEEE Trans. Communication, vol. 65, no. 2, pp. 549–563, Feb. 2017.
[7] W. Zhu, J. and. Xu and N. Wang, “Secure massive MIMO systems with limited RF chains,” IEEE Trans. Veh. Technol., vol. 66, no. 6, pp. 5455–5460, Jun. 2017.
[8] R. Zhang, X. Cheng, and L. Yang, “Cooperation via spectrum sharing for physical layer security in device-to-device communications under laying cellular networks,” IEEE Trans. Wireless Communication, vol. 15, no. 8, pp. 5651–5663, Aug. 2016.
[9] K. Tourki and M. O. Hasna, “A collaboration incentive exploiting the primary-secondary systems cross interference for PHY security enhancement,” IEEE J. Sel. Topics Signal Process., vol. 10, no. 8, pp. 1346–1358, Dec 2016.
[10] T. Zhang et al., “Secure transmission in cognitive MIMO relaying networks with outdated channel state information,” IEEE Access, vol. 4, pp. 8212–8224, Sep. 2016.
[11] Y. Huang et al., “Secure transmission in spectrum sharing MIMO channels with generalized antenna selection over Nakagami-m channels,” IEEE Access, vol. 4, pp. 4058–4065, Jul. 2016.
[12] Y. Deng et al., “Artificial-noise aided secure transmission in large scale spectrum sharing networks,” IEEE Trans. Communication, vol. 64, no. 5, pp. 2116–2129, May 2016.
[13] J. Zhu, R. Schober, and V. K. Bhargava, “Linear precoding of data and artificial noise in secure massive MIMO systems,” IEEE Trans. Wireless Communication, vol. 15, no. 3, pp. 2245–2261, Mar. 2016.
[14] K. Guo, Y. Guo, and G. Ascheid, “Security-constrained power allocation in MU-massive-MIMO with distributed antennas,” IEEE Trans. Wireless Commun., vol. 15, no. 12, pp. 8139–8153, Dec. 2016.
[15] Danda B. Rawat, “Evaluation Performance of Cognitive Radio Users in MIMO-OFDM based Wireless Networks”, IEEE Transaction of Wireless System, Vol. 32, Issue 07, IEEE 2016.
[16] H. Q. Ngo A. Ashikhmin H. Yang E. G. Larsson T. L. Marzetta "Cell-free massive MIMO: Uniformly great service for everyone" Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC) pp. 201-205 Jun. 2015
[17] E. Nayebi A. Ashikhmin T. L. Marzetta H. Yang "Cell-free massive MIMO systems" Proc. 49th Asilomar Conf. Signals Syst. Comput. pp. 695-699 Nov. 2015.
[18] Shan Jin and Xi Zhang, “Compressive Spectrum Sensing for MIMO-OFDM Based Cognitive Radio Networks”, 2015 IEEE Wireless Communications and Networking Conference (WCNC), Applications, and Business, Vol. 27, No. 2, pp. 567-572, 2015.
[19] H. Q. Ngo A. Ashikhmin H. Yang E. G. larsson T. L. Marzetta "Cell-Free Massive MIMO: Uniformly great service for everyone" Proc. 16th Int Workshop Signal Process. Ad If Wireless Commun. (SPAWC) pp. 201-205 Jun. 2015.