HetNet için küçük hücre kullanıcı yoğunluğunun küçük hücre uyutma tekniği performansı üzerindeki etkisi

Öz

Bu çalışmada, küçük hücre kullanıcı yoğunluğunun güç tüketimi ve makro ve küçük hücreler tarafından servis verilen kullanıcı sayısı üzerindeki etkisi analiz edilmektedir. Kullanıcıların tüm kapsama alanında düzgün dağılımlı olarak konumlandığı ve küçük hücre kullanıcı yoğunluğunun makro hücre kullanıcı yoğunluğu ile doğrusal olarak ilişkili olduğu varsayılmıştır. Benzetimlerde, üç farklı ortalama değer seçilmiş ve bu değerlere göre güç tüketimi ve makro hücre ve küçük hücreler tarafından servis verilen kullanıcı sayısı incelenmiştir. Benzetim sonuçları, düşük ortalama değere sahip küçük hücrelerin yüksek tasarruf gücü sağladığını göstermektedir. Makro hücrenin kullanıcı yoğunluğu  ve küçük hücre kullanıcı yoğunluğunun ortalama değeri 15 olduğunda, küçük hücre uyutma tekniği kullanılarak elde edilen kazanç 695 Watt’tır.  olduğunda ise 372 Watt’a düşmektedir. Benzer olarak küçük hücre kullanıcı yoğunluğunun ortalama değeri arttıkça algoritma ile elde edilen güç kazancı azalmaktadır.

Anahtar Kelimeler

• Heterojen ağlar,Kullanıcı yoğunluğu,Küçük Hücre,Makro Hücre,Trafik aktarma

Impact of small cell user density on performance of small cells sleeping technique for HetNet

Abstract

In this paper, we analyze the impact of the small cell user density on the power consumption and the number of served users by the macro cell and small cells. It is assumed that users are distributed uniformly in all coverage area with different density and small cell user density is related linearly with macro cell user density. In the simulations, three different mean values are chosen, where the power consumption and the number of served users by the macro cell and small cells are investigated according to these values. The simulation results show that small cells with low mean value enable to save more power. When user density of macro cell is and mean value of small cell user density equals to 15, saved power by using the small cells sleeping technique is 695 Watts. When this saved power decreases to 372 Watts. Similarly, as the mean value of the small cell user density increases, the power gain obtained by the algorithm decreases.

Keywords

• Heterogeneous network,Macro cell,Small cell,Traffic offloading,User density

References

1. Auer G, Blume O, Giannini V, Godor I, Imran MA, Jading Y, Katranaras E, Olsson M, Sabella D, Skillermark P, Wajda W. “Earth project d2.3-energy efficiency analysis of the reference systems, areas of improvements and target breakdown”. Energy Aware Radio Network Technology (EARTH), 7, 1-68, 2012.
2. Webb M. "SMART 2020: Enabling the low carbon economy in the information age". The Climate Group. London, England, 2008.
3. Saker L, Elayoubi SE, Chahed T, Gati A. "Energy efficiency and capacity of heterogeneous network deployment in LTE-Advanced". European Wireless 2012; 18th European Wireless Conference 2012, Poznan, Poland, 18-20 April 2012.
4. Zhang H, Chu X, Guo W and Wang S. "Coexistence of Wi-Fi and heterogeneous SC networks sharing unlicensed spectrum". IEEE Communications Magazine, 53(3), 158-164, 2015.
5. Hwang I, Song B, Soliman SS. “A holistic view on hyper-dense heterogeneous and SC networks”. IEEE Communication Magazine, 51(6), 20-27, 2013.
6. Ge X, Tu S, Mao G, Wang CX, Han T. “5G ultra-dense cellular networks”. IEEE Wireless Communications, 23(1), 72-79, 2016.
7. Bhushan N, Li J, Malladi D, Gilmore R, Brenner D, Damnjanovic A, Sukhavasi R, Patel C, Geinhofer S. “Network densification: The dominant theme for wireless evolution into 5G”. IEEE Communications Magazine, 52(2), 82-89, 2014.
8. Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong ACK, Zhang JC, “What Will 5G Be?”. IEEE Journal on Selected Areas in Communications, 32(6), 1065-1082, 2014.

9. 3GPP. “ETSI Summit on Future Mobile and Standards for 5G”. http://www.3gpp.org/news-events/conferences/ 1515-etsi-summit-on-future-mobile-and (11.04.2018).
10. Shakir MZ, Qarage KA, Tabassum H, Aloini MA, Serpedin E, Imran MA. "Green heterogeneous small-cell networks: Toward reducing the CO2 emissions of mobile communications industry using uplink power adaptation". IEEE Communications Magazine, 51(6), 52-61, 2013.
11. Luo S, Zhang R, Lim TJ. "Optimal power and range adaptation for green broadcasting". IEEE Transactions on Wireless Communications, 12(9), 4592-4603, 2013.
12. Vereecken W, Deruyck M, Colle D, Joseph W. Pickavet M., Martens L., Demeester P. "Evaluation of the potential for energy saving in macrocell and femtocell networks using a heuristic introducing sleep modes in base stations". EURASIP Journal on Wireless Communications and Networking, 170, 1-14, 2012.
13. Cai S, Che Y, Duan L, Wang J, Zhou S, Zhang R. "Green 5G heterogeneous networks through dynamic small-cell operation". IEEE Journal on Selected Areas in Communications, 34(5), 1103-1115, 2016.
14. Details are hidden under the double-sided privacy policy.
15. Li L, Peng M, Yang, C, Wu Y. “Optimization of base-station density for high energy-efficient cellular networks with sleeping strategies”. IEEE Transactions on Vehicular Technology, 65(9), 7501-7514, 2016.
16. Namdar M, Ilhan H, Durak-Ata L. “Dispersed chirp-z transform based spectrum sensing and utilization in cognitive radio networks”. IET Signal Processing, 8(4), 320-329, 2014.
17. Che YL, Duan L, Zhang R. “Spatial throughput maximization of wireless powered communication networks”. IEEE Journal on Selected Areas in Communication, 33(8), 1534-1548, 2015.
18. Samarakoon S, Bennis M, Saad W, Debbah M, Latva-aho M. "Ultra dense small cell networks: turning density into energy efficiency". IEEE Journal on Selected Areas in Communications, 34(5), 1267-1280, 2016.
19. “Small cell enhancements for E-UTRA and E-UTRAN - Physical layer aspects”. 3rd Generation Partnership Project (3GPP), TR 36.872, 2013.