New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory

  • De-gan Zhang
  • Chen Chen
  • Yu-ya CuiEmail author
  • Ting Zhang


Since there is a competition between subcarriers because FBMC (Filter Bank Multicarrier) modulation technology does not need subcarriers to be orthogonal to each other, we consider the evolutionary game method to optimize subcarrier allocation. Because the adjacent subcarriers do not need to be orthogonal to each other in FBMC, there is conflict and competition, thus the evolutionary game theory is used to optimize the subcarrier allocation problem. We innovatively introduced the channel state matrix to show the quality of subcarriers. Considering the height of secondary user and base station’s antenna, the total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier, a nonlinear fractional programming problem is established where maximum energy efficiency is the objective function, total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier are constraint conditions. The utility function for each secondary user is established when the evolutionary game operator is designed. When the utility function becomes optimal, the evolutionary game reaches Nash equilibrium, and the strategy combination is considered to be the energy efficient resource allocation scheme. Through experimental simulation, EESA-EG proposed in this paper gives the most reasonable subcarrier allocation scheme, allocates more subcarriers for the subcarriers with better channel state and the energy efficiency in EESA-EG is optimal.


Cognitive radio network FBMC Resource allocation Evolution game Energy efficient optimization 



This research work is supported by National Natural Science Foundation of China (Grant No. 61571328), Tianjin Key Natural Science Foundation (No.13JCZDJC34600), CSC Foundation (No. 201308120010), Major projects of science and technology in Tianjin (No.15ZXDSGX 00050), Training plan of Tianjin University Innovation Team (No.TD12-5016, No.TD13-5025), Major projects of science and technology for their services in Tianjin (No.16ZXFWGX00010, No.17YFZC GX00360), the Key Subject Foundation of Tianjin(15JCYBJC 46500), Training plan of Tianjin 131 Innovation Talent Team (No.TD2015-23).


  1. 1.
    Zhao Q, Sadler B (2007) A survey of dynamic spectrum access. IEEE Signal Process Mag 24(3):79–89CrossRefGoogle Scholar
  2. 2.
    Weiss T, Jondral F (2004) Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Commun Mag 42(3):S8–S14CrossRefGoogle Scholar
  3. 3.
    Boroujeny BF (2011) OFDM versus filter bank multicarrier. IEEE Signal Process Mag 28(3):92–112CrossRefGoogle Scholar
  4. 4.
    Medjahdi Y, Terre M, Le Ruyet D (2011) Performance analysis in the downlink of asynchronous OFDM /FBMC based multi-cellular networks. IEEE Trans Wirel Commun 10(8):2630–2639CrossRefGoogle Scholar
  5. 5.
    Li GY, Xu Z, Xiong C (2011) Energy-efficient wireless communications: tutorial, survey, and open issues. IEEE Trans Wirel Commun 18(6):29–35Google Scholar
  6. 6.
    Liu S, Zhang T (2017) Novel unequal clustering routing protocol considering energy balancing based on Network Partition & Distance for Mobile education. J Netw Comput Appl 88(15):1–9Google Scholar
  7. 7.
    D. G. Zhang, H. Ge, T. Zhang (2018) New Multi-hop Clustering Algorithm for Vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems 7. doi:
  8. 8.
    Jiang C, Chen Y, Gao Y (2013) Joint spectrum sensing and access evolutionary game in cognitive radio networks. IEEE Trans Wirel Commun 12(5):2470–2483CrossRefGoogle Scholar
  9. 9.
    Jiang C, Chen Y, Liu RKJ (2013) Renewal-theoretical dynamic spectrum access in cognitive radio networks with unknown primary behavior. IEEE J Select Areas Commun 31(3):406–416CrossRefGoogle Scholar
  10. 10.
    Ngo DT, Ngoc TL (2011) Distributed resource allocation for cognitive radio networks with spectrum-sharing constraints. IEEE Trans Veh Technol 60(7):3436–3449CrossRefGoogle Scholar
  11. 11.
    Choi KW, Hossain E, Kim DI (2011) Downlink subchannel and power allocation in multi-cell OFDMA cognitive radio networks. IEEE Trans Wirel Commun 10(7):2259–2271CrossRefGoogle Scholar
  12. 12.
    Ma Y, Kim DI, Wu Z (2010) Optimization of OFDMA- based cellular cognitive radio networks. IEEE Trans Commun 58(8):2265–2276CrossRefGoogle Scholar
  13. 13.
    Li W, Lei J, Wang T (2016) Dynamic optimization for resource allocation in relay-aided OFDMA systems under multiservice. IEEE Trans Veh Technol 65(3):1303–1313CrossRefGoogle Scholar
  14. 14.
    Zarakovitis CC, Ni Q (2016) Maximizing energy efficiency in multiuser multicarrier broadband wireless systems: convex relaxation and global optimization techniques. IEEE Trans Veh Technol 65(7):5275–5286CrossRefGoogle Scholar
  15. 15.
    Singh K, Ku ML, Lin JC (2014) Power control for achieving energy efficient multiuser two-way balancing relay networks. Proc IEEE ICASSP:2749–2753Google Scholar
  16. 16.
    Gur G, Alagoz F (2011) Green wireless communications via cognitive dimension: an overview. IEEE Netw 25(2):50–56CrossRefGoogle Scholar
  17. 17.
    Zhang DG, Li G (2014) An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Trans Industrial Inform 10(1):766–773CrossRefGoogle Scholar
  18. 18.
    Buzzi S, Colavolpe G, Saturnino D (2012) Potential games for energy-efficient power control and subcarrier allocation in uplink multicell OFDMA systems. IEEE J Select Topics Sign Proc 6(2):89–103CrossRefGoogle Scholar
  19. 19.
    Denis J, Pischella M, Le Ruyet D (2016) Optimal energy-efficient power allocation for asynchronous cognitive radio networks using FBMC/OFDM. IEEE wireless conference and networking conference (WCNC 2016) track 1: PHY and fundamentalsGoogle Scholar
  20. 20.
    Huang JW, Krishnamurthy V (2011) Cognitive base stations in LTE/3GPP femtocells: a correlated equilibrium game-theoretic approach. IEEE Trans Wirel Commun 59(12):3485–3493CrossRefGoogle Scholar
  21. 21.
    Zhang DG, Wang X (2014) A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans Serv Comput 7(4):741–748CrossRefGoogle Scholar
  22. 22.
    Yang CG, Li JD, Tian Z (2010) Optimal power control for cognitive radio networks under coupled interference constraints: a cooperative game- theoretic perspective. IEEE Trans Veh Technol 59(4):1696–1706CrossRefGoogle Scholar
  23. 23.
    Yaacoub E (2009) Z. Dawy. A game theoretical formulation for proportional fairness in LTE uplink scheduling. IEEE Wireless Commun Network Conf 2009:1–5Google Scholar
  24. 24.
    Vatsikas S, Armour S, Vos MD (2011) A Fast and Fair Algorithm for Distributed Subcarrier Allocation Using Coalitions and the Nash Bargaining Solution. 2011 IEEE Vehicular Technology Conference (VTC Fall) 1–5Google Scholar
  25. 25.
    Zheng K, Zhao DX (2016) Novel quick start (QS) method for optimization of TCP. Wirel Netw 22(1):211–222CrossRefGoogle Scholar
  26. 26.
    Huang SL, Tan JJ, Xu J (2015) Nash Bargaining Game Based Subcarrier Allocation for Physical Layer Security in Orthogonal Frequency Division Multiplexing System. 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 1094–1100Google Scholar
  27. 27.
    Song QY, Zhuang JH, Zhang LC (2011) Evolution Game Based Spectrum Allocation in Cognitive Radio Networks. 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing 1–4Google Scholar
  28. 28.
    Medjahdi Y, Terre M, Le Ruyet D et al. (2009) Inter-cell interference analysis for OFDM/FBMC systems. IEEE 10th Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 598–602Google Scholar
  29. 29.
    Yu W, Lui R (2006) Dual methods for nonconvex spectrum optimization of multicarrier systems. IEEE Trans Commun 54(7):1310–1322CrossRefGoogle Scholar
  30. 30.
    Zhou S, Tang Y-m (2018) A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mob Network Appl 23(4):828–839CrossRefGoogle Scholar
  31. 31.
    Zhang T, Dong Y (2018) Novel optimized link state routing protocol based on quantum genetic strategy for Mobile learning. J Netw Comput Appl 2018(122):37–49. CrossRefGoogle Scholar
  32. 32.
    Zhang DG (2012) A new approach and system for attentive mobile learning based on seamless migration. Appl Intell 36(1):75–89CrossRefGoogle Scholar
  33. 33.
    Zhang XD (2012) Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterprise Informa Syst 6(4):473–489CrossRefGoogle Scholar
  34. 34.
    Zhao CP (2012) A new medium access control protocol based on perceived data reliability and spatial correlation in wireless sensor network. Comput Electr Eng 38(3):694–702CrossRefGoogle Scholar
  35. 35.
    Zhang T, Zhang J (2018) A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP J Wirel Commun Netw 2018(159):1–15. CrossRefGoogle Scholar
  36. 36.
    Chen JQ, Mao GQ (2018) Capacity of cooperative vehicular networks with infrastructure support: multi-user case. IEEE Trans Veh Technol 67(2):1546–1560CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • De-gan Zhang
    • 1
    • 2
    • 3
  • Chen Chen
    • 1
    • 2
  • Yu-ya Cui
    • 1
    • 2
    Email author
  • Ting Zhang
    • 1
    • 2
  1. 1.Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of EducationBeijingChina
  2. 2.Tianjin Key Lab of Intelligent Computing & Novel software TechnologyTianjin University of TechnologyXiqingChina
  3. 3.School of Electronic and Information EngineeringUniversity of SydneySydneyAustralia

Personalised recommendations