Optimal Energy Efficiency Through Dpsn Based 5g Network

International Journal of Electronics and Communication Engineering
© 2020 by SSRG - IJECE Journal
Volume 7 Issue 3
Year of Publication : 2020
Authors : G. V. Ramanaiah , L. Krishna kavya, P. V. Rajya Lakshmi, V. Sai Kumar, Sk. Shahed Ali
pdf
How to Cite?

G. V. Ramanaiah , L. Krishna kavya, P. V. Rajya Lakshmi, V. Sai Kumar, Sk. Shahed Ali, "Optimal Energy Efficiency Through Dpsn Based 5g Network," SSRG International Journal of Electronics and Communication Engineering, vol. 7,  no. 3, pp. 29-34, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I3P105

Abstract:

With the increase of new operator(s) for mobile equipment's utilizing 4G services, these 4G network services extended rapidly. Presently focus is moved to 5G advances specifically meeting high data rates. To meet the necessary data rates, network densification is a clear way. Integrating Small cells in ultra-dense with mm-Wave backhauled massive MIMO enabled base stations is a successful method for accomplishing network densification, yet it to be designed in an energy effective way. This paper will propose an optimal Energy Efficient(EE) tractable model for designed Small cell DPSN (Digitally Phase Shifter Network) architecture integrating Massive MIMO based mm-wave backhauled network. This paper examines the practicality of DPSN based mmWave Massive MIMO-based backhaul for 5G Ultra-Dense Small cells. Afterward, tractable uplink general Energy efficiency(EE) optimized framework is determined for a proposed network concerning the Small cell Base Station(BSs) density, the transceiver hardware impairments, and the pilot reuse factor. One of the proposed network's key highlights is simultaneously supporting numerous Small cell Base Stations(SBS) in an energy-efficient way utilizing general Energy Efficiency maximizing framework.

Keywords:

5G, Network densification, mm-Wave, DPSN, Massive MIMO, Optimal Energy Efficiency

References:

[1] Gao, Z., Dai, L., Mi, D., Wang, Z., Imran, M. A. and Shakir, M. Z. (2015) "MmWavemassive‐ MIMO‐based wireless backhaul for the 5G ultra‐dense network". IEEE Wireless Communications, 22(5), 13–21.
[2] Singh, S., Kulkarni, M. N., Ghosh, A. and Andrews, J. G. (2015) "Tractable model for rate in self‐ backhauled millimeter-wave cellular networks." IEEE Journal on Selected Areas in Communication, 31 (10), 2196–2211.
[3] J. Hoydis, M. Kobayashi, and M. Debbah, "Green small-cell networks," IEEEVeh. Technol. Mag., vol. 6, no. 1, pp. 37–43, 2011.
[4] S. Mohammed, "Impact of transceiver power consumption on the energy efficiency of the zero-forcing detector in massive MIMO systems," IEEE Trans. Commun., vol. 62, no. 11, pp. 3874–3890, 2014.
[5] E. Bjornson, L. Sanguinetti, and M. Kountouris, "Designing wireless broadband access for energy efficiency: Are small cells the only answer? " in Proc IEEE International Conference on Communications (ICC), 2015.
[6] E. Bjornson, L. Sanguinetti, and M. Kountouris, "Energy-efficient future wireless networks: A marriage between massive MIMO and small cells," in Proc. of IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), June 2015, pp. 211– 215
[7] G. Miao, "Energy-efficient uplink multi-user MIMO," IEEE Trans. Wireless Commun., vol. 12, no. 5, pp. 2302–2313, 2013.
[8] GreenTouch Green Meter Research Study, "Reducing the net energy consumption in communications networks by up to 90% by 2020," Tech. Rep., Jun. 2013.
[9] E. Bjornson, L. Sanguinetti, J. Hoydis, and M. Debbah, "Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer?," IEEE Trans. Wireless Commun., vol. 14, no. 6, pp. 3059– 3075, 2015.
[10] P. Senthil, Sr. Dr. Stanly, M.Suganya, S.S.Inakshi "New sushi sen methods IEEE802.1 Analysis " International Journal of Recent Engineering Science 7.1(2020):17-20.
[11] Khaled Elbehiery, Hussam Elbehiery, "Millennial National Security's Cornerstones 5G, Cloud Technology, and Artificial Intelligence" SSRG International Journal of Electronics and Communication Engineering 6.8 (2019): 44-54.