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International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

Performance Analysis of Various Massive MIMO Detection Algorithms in 5G Wireless Technologies

Author(s): Shihab Jimaa* and Jawahir Al-Ali

Volume 10, Issue 6, 2020

Page: [1012 - 1022] Pages: 11

DOI: 10.2174/2210327910666191223123059

Price: $65

Abstract

Background: The 5G will lead to a great transformation in the mobile telecommunications sector.

Objective: The huge challenges being faced by wireless communications such as the increased number of users have given a chance for 5G systems to be developed and considered as an alternative solution. The 5G technology will provide a higher data rate, reduced latency, more efficient power than the previous generations, higher system capacity, and more connected devices.

Method: It will offer new different technologies and enhanced versions of the existing ones, as well as new features. 5G systems are going to use massive MIMO (mMIMO), which is a promising technology in the development of these systems. Furthermore, mMIMO will increase the wireless spectrum efficiency and improve the network coverage.

Result: In this paper we present a brief survey on 5G and its technologies, discuss the mMIMO technology with its features and advantages, review the mMIMO capacity and energy efficiency and also presents the recent beamforming techniques.

Conclusion: Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the Alternating Direction Method Of Multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance.

Keywords: 5G, massive MIMO, detection algorithms, wireless technologies, ADMM, AD-MIN.

Graphical Abstract

[1]
Soldani D, Manzalini A. Horizon 2020 and beyond: On the 5G operating system for a true digital society. IEEE Veh Technol Mag 2015; 1: 32.
[http://dx.doi.org/10.1109/MVT.2014.2380581]
[2]
Tullberg H, Popovski P, Li Z, et al. The METIS 5G System concept: meeting the 5G requirements. IEEE Commun Mag 2016; 12: 132.
[http://dx.doi.org/10.1109/MCOM.2016.1500799CM]
[3]
Morgado A, Huq KM, Mumtaz S, Rodrigues J. A survey of 5G technologies: regulatory standardization and industrial perspectives. Digital Commun Netw 2018; 4(2): 87-97.
[http://dx.doi.org/10.1016/j.dcan.2017.09.010]
[4]
Shi S, Yang W, Zhang J, Chang Z. Review of key technologies of 5G wireless communication system. MATEC Web of Conferences EDP Sciences 2015; 22: 01005.
[5]
Yadav A, Tsiropoulos GI, Dobre OA. Full-duplex communications: performance in ultradense mm-Wave small-cell wireless networks. IEEE Veh Technol Mag 2018; 13(2): 40-7.
[http://dx.doi.org/10.1109/MVT.2018.2811644]
[6]
Ren H, Liu N, Pan C, et al. Low-Latency C-RAN: an next-generation wireless approach. IEEE Veh Technol Mag 2018; 13(2): 48-56.
[http://dx.doi.org/10.1109/MVT.2018.2811244]
[7]
Ye Q, Li J, Qu K, Zhuang W, Shen XS, Li X. End-to-end quality of service in 5G networks: examining the effectiveness of a network slicing framework. IEEE Veh Technol Mag 2018; 13(2): 65-74.
[http://dx.doi.org/10.1109/MVT.2018.2809473]
[8]
Ortega V, Bouchmal F, Monserrat JF. Trusted 5G vehicular networks: blockchains and content-centric networking. IEEE Veh Technol Mag 2018; 13(2): 121-7.
[http://dx.doi.org/10.1109/MVT.2018.2813422]
[9]
Hussain SS, Yaseen SM, Barman KA. An overview of massive MIMO system in 5G. IJCTA 2016; pp. 4957-68.
[10]
Mundy J, Thomas K. .What is massive MIMO technology?. 5g. co.uk. 2019:1 Reference Available from:. www.5g.co5g.co.uk/guides/what-is-massive-mimo-technology/
[11]
Agrawal D, Thoi ST, Arcade N, Arif W, Sen D. Analysis of ergodic throughput under opportunistic space division multiple access with beam selection (OSDMA-S) for massive MIMO-BC channel in dynamic system. Intl Conf Wirel Commu Signal Proce Netw (WiSPNet) 2016; pp.1909-1911..
[12]
Sun D. Spectral efficiency and energy efficiency in massive MIMO systemsMSA thesis, University of New South Wales: Australia 2017.
[13]
Hassan N, Fernando X. Massive MIMO wireless networks: An overview. Electron Mag 2017; 6(3): 63.
[http://dx.doi.org/10.3390/electronics6030063]
[14]
AlShabili A, Taha B, Al-Ogaili F, et al. Sparse NLMS adaptive algorithms for multipath wireless channel estimation 2015 IEEE 11th Intl Conf Wirel Mobile Comput Netw Commu(WiMob) 2015; pp839-844.
[15]
Jimaa SA, Al-Simiri A, Shubair RM, Shimamura T. Convergence evaluation of variable step-size NLMS algorithm in adaptive channel equalization. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2009. 145-50.
[16]
Takekawa H, Shimamura T, Jimaa SA. An efficient and effective variable step-size NLMS algorithm. 42nd Asilomar Conference on Signals, Systems and Computers 2008. 1640-3.
[17]
Jimaa SA, Jadah ME, Sharif BS. Least-mean-mixed-norm adaptive filtering for impulsive DS-CDMA channels. Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology 2004 2004. 9-12.
[18]
Hassan MA, Sharif BS, Woo WL, Jimaa S. Semiblind estimation of time varying STFBC-OFDM channels using Kalman filter and CMA. CMA Proceedings ISCC 2004 Ninth International Symposium on Computers And Communications (IEEE Cat No 04TH8769) 2004; 2: 594-599.
[19]
Jimaa S, Cowan C, Holt M. Adaptive channel equalization using least-mean-switched error algorithm. Proceedings of the 16th IEEE Saraga Colloquium on Digital and Analogue Filters and Filtering System London, UK.
[http://dx.doi.org/10.1049/ic:19961269]
[20]
Al-Ogaili F, Elayan H, Alhalabi L, et al. Leveraging the ℓ1-LS criterion for OFDM sparse wireless channel estimation 2015 IEEE 11th Intl Conf Wirel Mobile Comput Netw Commu(wiMob) 2015; 845-849.
[21]
De Figueiredo FAP, Cardoso FACM, Moerman I, Fraidenraich G. Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading IEEE Access 2017; 5: 17733-41
[http://dx.doi.org/10.1109/ACCESS.2017.2749602]
[22]
Ali E, Ismail M, Nordin R, Abdulah N. Beamforming techniques for massive MIMO systems in 5G: overview classification and trends for future research. Front Info Technol Electronic Engr 2017; 18(6): 753-72.
[http://dx.doi.org/10.1631/FITEE.1601817]
[23]
Minango J, Flores AC. Low-complexity MMSE detector based on refinement Jacobi method for massive MIMO uplink. Phys Commun 2017; 26: 128-33.
[http://dx.doi.org/10.1016/j.phycom.2017.12.005]
[24]
Kang B, Yoon J, Park J. Low complexity massive MIMO detection architecture based on neumann method. Intel SoC Design Conf (ISOCC), Gyungju, 2015; pp293-4.
[http://dx.doi.org/10.1109/ISOCC.2015.7401703]
[25]
Jiang F, Li C, Gong Z. Block gauss-seidel method based detection in vehicle-to-infrastructure massive MIMO uplink GLOBECOM. IEEE Global Commu Conf Singapore 2017. 1-6.
[26]
Yin B, Wu M, Cavallaro JR, Studer C. Conjugate Gradient-based Soft-Output Detection and Precoding in Massive MIMO Systems. 2014 IEEE Global Commu Conf 2014; pp3696-3701.
[http://dx.doi.org/10.1109/GLOCOM.2014.7037382]
[27]
Shahabuddin S, Juntti M, Studer C. ADMM-based infinity norm detection for large MU-MIMO: algorithm and VLSI architecture IEEE Intel Symp Circuits Sys. ISCAS Baltimore 2007; pp. 1-4.
[28]
Wu M, Dick C, Cavallaro JR, Studer C. High-throughput data detection for massive MU-MIMO-OFDM using coordinate descent. IEEE Trans Circuits Syst I Regul Pap 2016; 63(12): 2357-67.
[http://dx.doi.org/10.1109/TCSI.2016.2611645]

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