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Energy-Efficiency Maximization with Non-linear Fractional Programming for Intelligent Device-to-Device Communications

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Abstract

With the exponential growth of wireless users and their traffic demands, it is greatly increasing for the demand of the scarce spectrum resources in the communication networks. In order to enhance the performance of the wireless networks such as end-to-end delay, energy efficiency and throughput, the device-to-device (D2D) communication has been attracted more attention because the two devices in close proximity can communicate directly without traversing the central base station. However, most of users are very sensitive to the battery. Therefore, we aim to maximize the energy efficiency of wireless communication system in the context of underlaying device-to-device communication in this paper, We focus on the formulated power control and resource allocation problem which is non-convex in the fractional form. We reduce it from the power allocation of all users to the joint power and subchannel allocation of D2D users. Then, we tackle it by an iterative approximation algorithm leveraging to the properties of fractional programming. There are two studied cases for the subchannel allocation. One can be solved by the penalty function approach, and the other can be solved by the dual decomposition as well as sub-gradient method. Accordingly, we propose a dual-based algorithm in general. Numerical simulations demonstrate that the proposed algorithms outperform the conventional algorithm in terms of the energy efficiency.

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References

  1. Sheng Z, Mahapatra C, Zhu C, Leung VCM (2015) Recent advances in industrial wireless sensor networks toward efficient management in IoT. IEEE Access 3:622–637

    Article  Google Scholar 

  2. Afzal A, Zaidi SAR, Shakir MZ, Imran MA, Ghogho M, Vasilakos AV, McLernon DC, Qaraqe K (2015) The cognitive internet of things: a unified perspective. Mob Netw Appl 20(1):72–85

    Article  Google Scholar 

  3. Zhu C, Leung VCM, Shu L, Ngai ECH (2015) Green internet of things for smart world. IEEE Access 3:2151–2162

    Article  Google Scholar 

  4. Li W, Zhu C, Leung VCM, Yang LT, Ma Y (2015) Performance comparison of cognitive radio sensor networks for industrial IOT with different deployment patterns. IEEE Syst J 11(3):1–11

    Google Scholar 

  5. Gupta A, Jha R (2015) A survey of 5G network: Architecture and emerging technologies. IEEE Access 3:1206–1232

    Article  Google Scholar 

  6. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutorials 17(4):2347–2376

    Article  Google Scholar 

  7. Whitmore A, Agarwal A, Daxu L (2015) The internet of things–a survey of topics and trends. Inf Syst Front 17(2):261–274

    Article  Google Scholar 

  8. Fu Z, Sun X, Ji S, Xie G (2016) Towards efficient content-aware search over encrypted outsourced data in cloud. In: The 35th annual IEEE international conference on computer communications, pp 1–9

  9. Chen Y, Hao C, Wu W, Wu E (2016) Robust dense reconstruction by range merging based on confidence estimation. Sci China Inform Sci 59(9):1–11

    Google Scholar 

  10. Kong Y, Zhang M, Ye D (2017) A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl-Based Syst 115:123–132

    Article  Google Scholar 

  11. Li J, Han G, Zhu C, Sun G (2016) An indoor ultrasonic positioning system based on toa for internet of things. Mob Inf Syst 4502867:1–10

    Google Scholar 

  12. Guan X, Zhai X, Yuan J, Liu H (2017) Energy-efficient power control and resource allocation for D2D communications in underlaying cellular networks. In: International conference on cloud compting and security

  13. Sambo YA, Shakir MZ, Qaraqe KA, Serpedin E (2014) Energy efficiency improvements in HetNets by exploiting device-to-device communications. IEEE Signal Process Conf 151–155

  14. Xu Y (2015) Energy-efficient power control scheme for device-to-device communications. Wirel Pers Commun 94(3):481–495

    Article  Google Scholar 

  15. Bhadauria SS, Vishwakarma S (2016) Energy efficient D2D application for increasing battery usage of smartphones. Int J Hybrid Inf Technol 9(2):311–328

    Article  Google Scholar 

  16. Xu Y (2016) A mode selection scheme for D2D communication in heterogeneous cellular networks. IEEE Glob Commun Conf 1–6

  17. Gao C, Sheng X, Tang J, Zhang W, Zou S, Guizani M (2014) Joint mode selection, channel allocation and power assignment for green device-to-device communications. In: IEEE international conference on communications, pp 178–183

  18. Yu CH, Tirkkonen O, Doppler K, Ribeiro C (2009) Power optimization of Device-to-Device communication underlaying cellular communication. In: IEEE international conference on communications, pp 1–5

  19. Zhou Z, Dong M, Ota K, Wu J, Sato T (2014) Distributed interference-aware energy-efficient resource allocation for device-to-device communications underlaying cellular networks. IEEE Glob Commun Conf 4454–4459

  20. Jiang Y, Liu Q, Zheng F, Gao X, You X (2016) Energy-efficient joint resource allocation and power control for D2D communications. IEEE Trans Veh Technol 65(8):6119–6127

    Article  Google Scholar 

  21. Hoang TD, Le LB, Le-Ngoc T (2016) Energy-efficient resource allocation for D2D communications in cellular networks. IEEE Trans Veh Technol 65(9):6972–6986

    Article  Google Scholar 

  22. Zhang Y, Sun X, Wang B (2016) Efficient algorithm for K-barrier coverage based on integer linear programming. China Communications 13(7):16–23

    Article  Google Scholar 

  23. Xue Y, Jiang J, Zhao B, Ma T (2017) A self-adaptive articial bee colony algorithm based on global best for global optimization. Soft Comput 8:1–18

    Google Scholar 

  24. Kong Y, Zhang M, Ye D (2017) A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl-Based Syst 115:123–132

    Article  Google Scholar 

  25. Dinkelbach W (1967) On nonlinear fractional programming. Manag Sci 13(7):492–498

    Article  MathSciNet  MATH  Google Scholar 

  26. Zhai X, Zheng L, Tan CW (2014) Energy-infeasibility tradeoff in cognitive radio networks: Price-driven spectrum access algorithms. IEEE J Sel Areas Commun 32(3):528–538

    Article  Google Scholar 

  27. Ng DWK, Lo ES, Schober R (2012) Energy-efficient resource allocation in SDMA systems with large numbers of base station antennas. In: IEEE international conference on communications, pp 4027–4032

  28. Xiao X, Tao X, Lu J (2013) QoS-aware energy-efficient radio resource scheduling in multi-user SDMA systems. IEEE Commun Lett 17(1):75–78

    Article  Google Scholar 

  29. Abrardo A, Belleschi M, Detti P, Moretti M (2012) Message passing resource allocation for the uplink of multi-carrier multi-format systems. IEEE Trans Wirel Commun 11:130–141

    Article  Google Scholar 

  30. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

Download references

Acknowledgements

The work in this paper was partially supported by the Special Foundation for State Major Basic Research Program of China under Grant No. 2017YFB0802303, in part by the National Natural Science Foundation of China under Grant No. 61701231, in part by FCT - Fundação para a Cie~ncia e a Tecnologia funding Project UID/EEA/500008/2013, in part by the Government of Russian Federation under Grant 074-U01, and in part by Finep, with resources from Funttel under Grant 01.14.0231.00, under the Centro de Referencia em Radiocomunicações-CRR project of the Instituto Nacional de Telecomunicações (Inatel), Brazil. The material in this paper was presented in part at the 3rd IEEE International Conference on Cloud Computing and Security, Nanjing, P. R. China, 2017.

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Correspondence to Xiangping Zhai.

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Zhai, X., Guan, X., Yuan, J. et al. Energy-Efficiency Maximization with Non-linear Fractional Programming for Intelligent Device-to-Device Communications. Mobile Netw Appl 23, 308–317 (2018). https://doi.org/10.1007/s11036-017-0951-5

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