Concurrent transmission for energy efficiency of user equipment in 5G wireless communication networks



In this paper, we investigate concurrent transmission among multiple radio access technologies (RATs) for energy efficiency (EE) of multi-mode user equipment (MUE) in 5G wireless communication networks. Considering both the static circuit power consumption of the MUE and channel state information of different RATs, we propose an EE maximization concurrent transmission (EXACT) strategy by fully utilizing the multi-RAT combining gain of concurrent transmission. In particular, we formulate such EE maximization concurrent transmission problem as a mixed binary integer programming (MIP), and under some given static circuit power conditions, the optimal RATs selection and transmission rates for establishing concurrent transmission among multiple RATs are derived. Furthermore, in order to deal with the challenging MIP, an approximate expression is derived to simplify the integer constraints, thus the original MIP is transformed into a nonlinear continuous optimization problem. Consequently, a low complexity heuristic algorithm for general static circuit power conditions, which can achieve the near-optimal solution, is presented. Simulation results confirm the effectiveness of the EXACT strategy and show that the EE performance of the MUE can be significantly improved by reasonable and effective utilization of multiple RATs to execute concurrent transmission.



This is a preview of subscription content, access via your institution.


  1. 1

    Onireti O, Heliot F, Imran M A. On the energy efficiency-spectral efficiency trade-off in the uplink of comp system. IEEE Trans Wirel Commun, 2012, 11: 556–561

    Article  Google Scholar 

  2. 2

    Carroll A, Heiser G. An analysis of power consumption in a smartphone. In: Proceedings of USENIX Annual Technical Conference. Boston: USENIX Association, 2010. 8–21

    Google Scholar 

  3. 3

    Zhong X, Xu C Z. Energy-efficient wireless packet scheduling with quality of service control. IEEE Trans Mob Comput, 2007, 6: 1158–1170

    Article  Google Scholar 

  4. 4

    Ibrahim M, Khawam K, Tohmé S. Network-centric joint radio resource policy in heterogeneous WiMAX-UMTS networks for streaming and elastic traffic. In: Proceedings of IEEE Wireless Communications and Networking Conference, Budapest, 2009. 1–6

    Google Scholar 

  5. 5

    Andrews J G, Buzzi S, Choi W, et al. What will 5G be? IEEE J Sel Areas Commun, 2014, 32: 1065–1082

    Article  Google Scholar 

  6. 6

    Lee W Y, Akyldiz I F. A spectrum decision framework for cognitive radio networks. IEEE Trans Mob Comput, 2011, 10: 161–174

    Article  Google Scholar 

  7. 7

    Ferrus R, Sallent O, Agusti R. Interworking in heterogeneous wireless networks: comprehensive framework and future trends. IEEE Wirel Commun, 2010, 17: 22–31

    Article  Google Scholar 

  8. 8

    Dimou K, Aguero R, Bortnik M, et al. Generic link layer: a solution for multi-radio transmission diversity in communication networks beyond 3G. In: Proceedings of 62nd IEEE Vehicular Technology Conference, Dallas, 2005. 1672–1676

    Google Scholar 

  9. 9

    Bazzi A, Pasolini G, Andrisano O. Multiradio resource management: parallel transmission for higher throughput? EURASIP J Adv Signal Process, 2008, 2008: 763264

    Article  Google Scholar 

  10. 10

    Chen Y, Zhang S, Xu S, et al. Fundamental trade-offs on green wireless networks. IEEE Commun Mag, 2011, 49: 30–37

    Article  Google Scholar 

  11. 11

    Saker L, Elayoubi S E, Chahed T. Minimizing energy consumption via sleep mode in green base station. In: Proceedings of IEEE Wireless Communications and Networking Conference, Sydney, 2010. 1–6

    Google Scholar 

  12. 12

    Ismail M, Zhuang W. Network cooperation for energy saving in green radio communications. IEEE Wirel Commun, 2011, 18: 76–81

    Article  Google Scholar 

  13. 13

    Miao G, Himayat N, Li G. Energy-efficient link adaptation in frequency-selective channels. IEEE Trans Commun, 2010, 58: 545–554

    Article  Google Scholar 

  14. 14

    Wang Y, Xu W, Yang K, et al. Optimal energy-efficient power allocation for OFDM-based cognitive radio networks. IEEE Commun Lett, 2012, 16: 1420–1423

    Article  Google Scholar 

  15. 15

    Xie R, Yu F R, Ji H. Energy-efficient spectrum sharing and power allocation in cognitive radio femtocell networks. In: Proceedings of IEEE INFOCOM, Orlando, 2012. 1665–1673

    Google Scholar 

  16. 16

    Miao G, Himayat N, Li G Y, et al. Low-complexity energy-efficient scheduling for uplink OFDMA. IEEE Trans Commun, 2012, 60: 112–120

    Article  Google Scholar 

  17. 17

    Khakurel S, Musavian L, Le-Ngoc T. Energy-efficient resource and power allocation for uplink multi-user OFDM systems. In: Proceedings of IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, 2012. 357–361

    Google Scholar 

  18. 18

    Ma X, Sheng M, Zhang Y. Green communications with network cooperation: a concurrent transmission approach. IEEE Commun Lett, 2012, 16: 1952–1955

    Article  Google Scholar 

  19. 19

    Radio Access Network; Generic Access Network; Stage 2 (Release 7). TS 43.318, v.8.3.0, Std., 2008

  20. 20

    Akbari A, Imran M, Tafazolli R, et al. Energy efficiency contours for single-carrier downlink channels. IEEE Commun Lett, 2011, 15: 1307–1309

    Article  Google Scholar 

  21. 21

    Boyd S, Vandenberghe L. Convex Optimization. Cambridge: Cambridge University Press, 2004

    Google Scholar 

  22. 22

    Sesia S, Toufik I, Baker M. LTE: the UMTS Long Term Evolution. Wiley Online Library, 2009

    Google Scholar 

  23. 23

    Hoekstra G J, van der Mei R D, Bhulai S. Optimal job splitting in parallel processor sharing queues. Stoch Models, 2012, 28: 144–166

    MathSciNet  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Min Sheng.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ma, X., Sheng, M., Li, J. et al. Concurrent transmission for energy efficiency of user equipment in 5G wireless communication networks. Sci. China Inf. Sci. 59, 1–15 (2016).

Download citation


  • energy efficiency
  • green communication
  • concurrent transmission
  • 5G wireless communication networks
  • data splitting


  • 022306


  • 能效
  • 绿色通信
  • 并发传输
  • 5G无线通信网络
  • 数据分流