Using multiple-Input Multiple-Output (MIMO) configuration is not new in the field of wireless communication to increase the capacity of the system. This configuration is still valid to use nowadays with the modern wireless configuration such as the Fifth generation (5G). Massive MIMO is the key resource of the 5G systems due to its huge ability to increase the capacity of the network and on the other hand its ability to enhance both spectral and transmit-energy efficiency. The need for using Massive MIMO comes from the increase in using smartphones, tablets, and the rise of the Internet of Things. This increasing demand for the use of wireless applications requires networking and Internet infrastructures to meet the needs of current and future multimedia applications which massive MIMO satisfies. The key limitation of using massive MIMO is the cost of installation of these antennas and how to multiplex between them. In addition to this, the Radio Frequency (RF) links are also increased where this increase leads to high system complexity and hardware energy consumption. Because of this, reducing the required number of RF chains is essential to use by performing antenna selection which this paper aims to evaluate without significant performance loss which can be performed by employing low-resolution Analog-to-Digital Converter (ADC) to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. In this paper, Quantization-Aware Greedy Antenna Selection (QAGAS) algorithm has been proposed and compared with other antenna selection algorithms especially simple algorithms like random selection and Fast Antenna Selection (FAS) algorithm. The achieved capacity is compared with that of a very simple scheme that selects the antennas with the highest received power. The system capacity obtained from QAGAS is evaluated related to the transmit power of the Base Station (BS) and the quantization bits used in the low-resolution ADC. The simulation is also performed for different numbers of users served by the BS and with the number of antennas at the BS. The simulation results show that the proposed algorithm indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF links and performing antenna selection using simple algorithms.
1. Hu, A., "Beam grouping based user scheduling in multi-cell millimeter-wave MIMO systems," IEEE Access, Vol. 6, 55004-55012, 2018. doi:10.1109/ACCESS.2018.2872516
2. Pandey, R., A. K. Shankhwar, and A. Singh, "An improved conversion efficiency of 1.975 to 4.744 GHz rectenna for wireless sensor applications," Progress In Electromagnetics Research C, Vol. 109, 217-225, 2021. doi:10.2528/PIERC20121102
3. Di, B., L. Song, and Y. Li, "Sub-channel assignment, power allocation, and user scheduling for non-orthogonal multiple access networks," IEEE Trans. Wirel. Commun., Vol. 15, No. 11, 7686-7698, 2016. doi:10.1109/TWC.2016.2606100
4. Al-Heety, A. T., M. T. Islam, A. H. Rashid, H. N. A. Ali, A. M. Fadil, and F. Arabian, "Performance evaluation of wireless data traffic in mm wave massive MIMO communication," Indones. J. Electr. Eng. Comput. Sci., Vol. 20, No. 3, 2020.
5. Jubair, M. A., S. A. Mostafa, A. Mustapha, M. A. Salamat, and H. Hassan, "Digging deeper into quality assessment of software requirement specifications," J. Crit. Rev., Vol. 7, No. 12, 3869-3875, 2020.
6. French, A. M. and J. P. Shim, "The digital revolution: Internet of things, 5G, and beyond," Commun. Assoc. Inf. Syst., Vol. 38, 840-850, 2016.
7. Al-Heety, A. T., M. Singh, J. Singh, M. T. Islam, and A. H. Ahmed, "MM-wave backhauling for 5G small cells," International Journal of Engineering & Technology, Vol. 7, No. 4, 6233-6237, 2018.
8. Khan, M. F., K.-L. A. Yau, R. M. D. Noor, and M. A. Imran, "Survey and taxonomy of clustering algorithms in 5G," J. Netw. Comput. Appl., Vol. 154, 102539, 2020. doi:10.1016/j.jnca.2020.102539
9. Hamdi, M. M., S. A. Rashid, M. Ismail, M. A. Altahrawi, M. F. Mansor, and M. K. Abufoul, "Performance evaluation of active queue management algorithms in large network," ISTT 2018 — 2018 IEEE 4th Int. Symp. Telecommun. Technol., 1-6, May 2018.
10. Rashid, S. A., L. Audah, M. M. Hamdi, and S. Alani, "Prediction based efficient multi-hop clustering approach with adaptive relay node selection for VANET," J. Commun., Vol. 15, No. 4, 332-344, 2020. doi:10.12720/jcm.15.4.332-344
11. Jubair, M. A., et al., "Bat optimized link state routing protocol for energy-aware mobile ad-hoc networks," Symmetry (Basel)., Vol. 11, No. 11, 2019.
12. Hamid, N. A., A. T. Al-heety, A. Alaa, and H. Alsabbagh, "Filtered-multicarrier modulation techniques for vehicle-to-vehicle communication," Solid State Technology, Vol. 63, No. 6, November 2020.
13. Guo, J., N. Li, Z. Jiang, S. Liu, and P. Chen, "System-level evaluation on practical massive MIMO deployment scenarios for 5G," 2019 IEEE 5th Int. Conf. Comput. Commun. ICCC 2019, 993-998, 2019.
14. Saad, M. A., S. T. Mustafa, M. H. Ali, M. M. Hashim, M. Bin Ismail, and A. H. Ali, "Spectrum sensing and energy detection in cognitive networks," Indones. J. Electr. Eng. Comput. Sci., Vol. 17, No. 1, 465-472, 2019.
15. Ahmed, A. H., A. T. Al-Heety, and B. Al-Khateeb, "Energy efficiency in 5G massive MIMO for mobile wireless network," 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2020.
16. Chandrasekaran, G., N. Wang, M. Hassanpour, M. Xu, and R. Tafazolli, "Mobility as a Service (MaaS): A D2D-based information centric network architecture for edge-controlled content distribution," IEEE Access, Vol. 6, 2110-2129, 2018. doi:10.1109/ACCESS.2017.2781736
17. Rubia, J. J., B. R. Lincy, and B. R. Lawrence, "Enhanced modified booth recoding technique for signal processing application," International Journal of Trends in Computational Engineering and Technology (IJTCET), January 2017.
18. Felipe, C. F. D., A. P. de Figueiredo, E. R. de Lima, and G. Fraidenraich, "Capacity bounds for dense massive MIMO in a line-of-sight propagation environment," Sensors, Vol. 20, 1-24, 2020. doi:10.1109/JSEN.2019.2959158
19. Choi, J. and B. L. Evans, "User scheduling for millimeter wave MIMO communications with low-resolution ADCs," IEEE Int. Conf. Commun., May 2018.
20. Ademaj, F., S. Schwarz, T. Berisha, and M. Rupp, "A spatial consistency model for geometry-based stochastic channels," IEEE Access, Vol. 7, 183414-183427, 2019. doi:10.1109/ACCESS.2019.2958154
21. Jency Rubia, J., B. Lincy, and A. T. Al-Heety, "Moving vehicle detection from video sequences for traffic surveillance system," Journal of Engineering and Technology for Industrial Applications, 41-48, 2021.