Volume 11, Issue 8 (August 2024), Pages: 66-79
----------------------------------------------
Original Research Paper
Enhancing spectral efficiency in uplink/downlink channels of multi-cell massive MIMO for 5G networks
Author(s):
Rao Muhammad Asif 1, Ateeq Ur Rehman 2, *, Sghaier Guizani 3, Habib Hamam 4, 5, 6, 7
Affiliation(s):
1Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
2School of Computing, Gachon University, Seongnam 13120, South Korea
3Electrical Engineering Department, Alfaisal University, Riyadh, Saudi Arabia
4Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
5Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada
6Faculty of Graduate Studies and Research, Hodmas University College, Taleh Area, Mogadishu, Somalia
7Sector of Research and Innovation, Bridges for Academic Excellence, Tunis, Centre-Ville 1002, Tunisia
Full text
Full Text - PDF
* Corresponding Author.
Corresponding author's ORCID profile: https://orcid.org/0000-0001-5203-0621
Digital Object Identifier (DOI)
https://doi.org/10.21833/ijaas.2024.08.008
Abstract
Massive multiple-input multiple-output (MIMO) systems are at the forefront of 5G technology, significantly improving energy efficiency compared to earlier wireless communication systems. This study develops an optimal model for energy-efficient massive MIMO systems, aiming to increase spectral efficiency (SE) within a multi-cell framework. Base stations (BSs) use various techniques for channel estimations during uplink (UL) transmission, including minimum mean-squared error (MMSE), Least Squares, and Element-wise MMSE (EW-MMSE) estimators. The research evaluates the SE achievable through MMSE channel estimation by applying different receive combining schemes. Additionally, it explores downlink (DL) transmission using various precoding schemes, utilizing vectors similar to those in combining schemes. Simulations show a significant improvement in SE by advancing UL and DL transmission models. The study highlights that optimized MMSE channel estimation, along with an increased number of BS antennas and the ability to serve multiple user equipment (UEs) per cell, can enhance the average SE per cell. The findings indicate that optimizing channel estimation is crucial for the development of massive MIMO systems, especially for improving SE in both UL and DL transmissions.
© 2024 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
Massive MIMO systems, Energy efficiency, Spectral efficiency, Channel estimation, Uplink transmission, Downlink transmission
Article history
Received 29 March 2024, Received in revised form 14 July 2024, Accepted 27 July 2024
Acknowledgment
No Acknowledgment.
Compliance with ethical standards
Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Citation:
Asif RM, Rehman AU, Guizani S, and Hamam H (2024). Enhancing spectral efficiency in uplink/downlink channels of multi-cell massive MIMO for 5G networks. International Journal of Advanced and Applied Sciences, 11(8): 66-79
Permanent Link to this page
Figures
Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7
Tables
Table 1 Table 2 Table 3 Table 4
----------------------------------------------
References (32)
- Ahmed A, Ahmed QZ, Almogren A, Haider SK, and Rehman AU (2021). Hybrid precoding aided fast frequency-hopping for millimeter-wave communication. IEEE Access, 9: 149596-149608. https://doi.org/10.1109/ACCESS.2021.3124923 [Google Scholar]
- Alam MS, Siddiqui ST, Qidwai KA, Aftab A, Kamal MS, and Shahi FI (2023). Evolution of wireless communication networks from 5G to 6G: Future perspective. Radioelectronics and Communications Systems, 66: 213-222. https://doi.org/10.3103/S0735272723050047 [Google Scholar]
- Ali SK, Khan AA, Rehman AU, and Ouahada K (2023). Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems. PLOS ONE, 18(9): e0289868. https://doi.org/10.1371/journal.pone.0289868 [Google Scholar] PMid:37682816 PMCid:PMC10490977
- Almelah HB and Hamdi KA (2016). Spectral efficiency of distributed large-scale MIMO systems with ZF receivers. IEEE Transactions on Vehicular Technology, 66(6): 4834-4844. https://doi.org/10.1109/TVT.2016.2616173 [Google Scholar]
- Al-Mogren AS (2008). Energy adaptive approach in a multi-channel dissemination-based network. In the New Technologies, Mobility and Security, IEEE, Tangier, Morocco: 1-6. https://doi.org/10.1109/NTMS.2008.ECP.44 [Google Scholar]
- Arshad J, Rehman A, Rehman AU, Ullah R, and Hwang SO (2020). Spectral efficiency augmentation in uplink massive MIMO systems by increasing transmit power and uniform linear array gain. Sensors, 20(17): 4982. https://doi.org/10.3390/s20174982 [Google Scholar] PMid:32887453 PMCid:PMC7506917
- Asif M, Khan WU, Afzal HR, Nebhen J, Ullah I, Rehman AU, and Kaabar MK (2021). Reduced‐complexity LDPC decoding for next‐generation IoT networks. Wireless Communications and Mobile Computing, 2021(1): 2029560. https://doi.org/10.1155/2021/2029560 [Google Scholar]
- Asif RM, Arshad J, Shakir M, Noman SM, and Rehman AU (2020). Energy efficiency augmentation in massive MIMO systems through linear precoding schemes and power consumption modeling. Wireless Communications and Mobile Computing, 2020(1): 8839088. https://doi.org/10.1155/2020/8839088 [Google Scholar]
- Asif RM, Shakir M, Nebhen J, Rehman AU, Shafiq M, and Choi JG (2022). Energy efficiency trade-off with spectral efficiency in MIMO systems. Computers, Materials and Continua, 70(3): 5889–5905. https://doi.org/10.32604/cmc.2022.020777 [Google Scholar]
- Basir S, Qureshi UU, Subhan F, Khan MA, Mohsan SA, Ghadi YY, Ouahada K, Hamam H, and Noor F (2023). A novel monopole ultra-wide-band multiple-input multiple-output antenna with triple-notched characteristics for enhanced wireless communication and portable systems. Sensors, 23(15): 6985. https://doi.org/10.3390/s23156985 [Google Scholar] PMid:37571769 PMCid:PMC10422626
- Björnson E, Hoydis J, and Sanguinetti L (2017). Massive MIMO has unlimited capacity. IEEE Transactions on Wireless Communications, 17(1): 574-590. https://doi.org/10.1109/TWC.2017.2768423 [Google Scholar]
- Chataut R and Akl R (2020). Massive MIMO systems for 5G and beyond networks—Overview, recent trends, challenges, and future research direction. Sensors, 20(10): 2753. https://doi.org/10.3390/s20102753 [Google Scholar] PMid:32408531 PMCid:PMC7284607
- Chataut R, Akl R, and Dey UK (2019). Least square regressor selection based detection for uplink 5G massive MIMO systems. In the IEEE 20th Wireless and Microwave Technology Conference, IEEE, Cocoa Beach, USA. https://doi.org/10.1109/WAMICON.2019.8765469 [Google Scholar]
- Chen X, Wang X, and Chen X (2013). Energy-efficient optimization for wireless information and power transfer in large-scale MIMO systems employing energy beamforming. IEEE Wireless Communications Letters, 2(6): 667-670. https://doi.org/10.1109/WCL.2013.092813.130514 [Google Scholar]
- Hassan S, Tariq N, Naqvi RA, Rehman AU, and Kaabar MK (2022). Performance evaluation of machine learning‐based channel equalization techniques: New trends and challenges. Journal of Sensors, 2022(1): 2053086. https://doi.org/10.1155/2022/2053086 [Google Scholar]
- Huo Y, Lin X, Di B, Zhang H, Hernando FJ, Tan AS, Mumtaz S, Demir ÖT, and Chen-Hu K (2023). Technology trends for massive MIMO towards 6G. Sensors, 23(13): 6062. https://doi.org/10.3390/s23136062 [Google Scholar] PMid:37447911 PMCid:PMC10347082
- Kang B, Yoon JH, and Park J (2017). Low‐complexity massive MIMO detectors based on Richardson method. ETRI Journal, 39(3): 326-335. https://doi.org/10.4218/etrij.17.0116.0732 [Google Scholar]
- Khan R, Yang Q, Ullah I, Rehman AU, Tufail AB, Noor A, Rehman A, and Cengiz K (2022). 3D convolutional neural networks based automatic modulation classification in the presence of channel noise. IET Communications, 16(5): 497-509. https://doi.org/10.1049/cmu2.12269 [Google Scholar]
- Li X, Bjornson E, Larsson E G, Zhou S, and Wang J (2015). A multi-cell MMSE detector for massive MIMO systems and new large system analysis. In the IEEE global communications conference, IEEE, San Diego, CA, USA: 1-6. https://doi.org/10.1109/GLOCOM.2015.7417112 [Google Scholar]
- Liu Y, Wang CX, Huang J, Sun J, and Zhang W (2018). Novel 3-D nonstationary mmWave massive MIMO channel models for 5G high-speed train wireless communications. IEEE Transactions on Vehicular Technology, 68(3): 2077-2086. https://doi.org/10.1109/TVT.2018.2866414 [Google Scholar]
- Mazhar T, Malik MA, Haq I, Rozeela I, Ullah I, Khan MA, Adhikari D, Ben Othman MT, and Hamam H (2022). The role of ML, AI and 5G technology in smart energy and smart building management. Electronics, 11(23): 3960. https://doi.org/10.3390/electronics11233960 [Google Scholar]
- Ngo HQ, Larsson EG, and Marzetta TL (2013). Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Transactions on Communications, 61(4): 1436-1449. https://doi.org/10.1109/TCOMM.2013.020413.110848 [Google Scholar]
- Nguyen HV, Nguyen VD, Dobre OA, Sharma SK, Chatzinotas S, Ottersten B, and Shin OS (2020). On the spectral and energy efficiencies of full-duplex cell-free massive MIMO. IEEE Journal on Selected Areas in Communications, 38(8): 1698-1718. https://doi.org/10.1109/JSAC.2020.3000810 [Google Scholar]
- Ozdogan O, Bjornson E, and Larsson EG (2018). Uplink spectral efficiency of massive MIMO with spatially correlated Rician fading. In the IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, IEEE, Kalamata, Greece: 1-5. https://doi.org/10.1109/SPAWC.2018.8445853 [Google Scholar]
- Palanisamy S, Rubini SS, Khalaf OI, and Hamam H (2024). Multi-objective hybrid split-ring resonator and electromagnetic bandgap structure-based fractal antennas using hybrid metaheuristic framework for wireless applications. Scientific Reports, 14(1): 1-27. https://doi.org/10.1038/s41598-024-53443-z [Google Scholar] PMid:38332219 PMCid:PMC11269741
- Shafiei A, Jamshidi M, Khani F, Talla J, Peroutka Z, Gantassi R, Baz M, Cheikhrouhou O, Hamam H (2021). A hybrid technique based on a genetic algorithm for fuzzy multiobjective problems in 5G, internet of things, and mobile edge computing. Mathematical Problems in Engineering, 2021: 9194578. https://doi.org/10.1155/2021/9194578 [Google Scholar]
- Tan W, Huang W, Yang X, Shi Z, Liu W, and Fan L (2018). Multiuser precoding scheme and achievable rate analysis for massive MIMO system. EURASIP Journal on Wireless Communications and Networking, 2018: 1-12. https://doi.org/10.1186/s13638-018-1223-1 [Google Scholar]
- Thakur A, and Mishra RC (2019). Performance analysis of energy-efficient multi-cell massive MIMO system. In the 10th International Conference on Computing, Communication and Networking Technologies, IEEE, Kanpur, India: 1-7. https://doi.org/10.1109/ICCCNT45670.2019.8944389 [Google Scholar] PMCid:PMC6544167
- Van Chien T, Mollén C, and Björnson E (2018). Large-scale-fading decoding in cellular massive MIMO systems with spatially correlated channels. IEEE Transactions on Communications, 67(4): 2746-2762. https://doi.org/10.1109/TCOMM.2018.2889090 [Google Scholar]
- Xin Y, Wang D, Li J, Zhu H, Wang J, and You X (2015). Area spectral efficiency and area energy efficiency of massive MIMO cellular systems. IEEE Transactions on Vehicular Technology, 65(5): 3243-3254. https://doi.org/10.1109/TVT.2015.2436896 [Google Scholar]
- Yang H and Marzetta TL (2013). Performance of conjugate and zero-forcing beamforming in large-scale antenna systems. IEEE Journal on Selected Areas in Communications, 31(2): 172-179. https://doi.org/10.1109/JSAC.2013.130206 [Google Scholar]
- Zahoor S, Ahmad I, Othman MTB, Mamoon A, Rehman AU, Shafiq M, and Hamam H (2022). Comprehensive analysis of network slicing for the developing commercial needs and networking challenges. Sensors, 22(17): 6623. https://doi.org/10.3390/s22176623 [Google Scholar] PMid:36081079 PMCid:PMC9459685
|