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Energy-efficient power allocation for selfish cooperative communication networks using bargaining game

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Abstract

In commercial networks, user nodes operating on batteries are assumed to be selfish to consume their energy solely to maximize their own benefits, e.g., data rates. In this paper, we propose a bargaining game to perform the power allocation for the selfish cooperative communication networks. In our system, two partner nodes can act as a source as well as a relay for each other, and each node is with an energy constraint to transmit one frame. Consider a selfish node is willing to seek cooperative transmission only if the data rate achieved through cooperation will not lower than that achieved through noncooperation by using the same amount of energy. The energy-efficient power allocation problem can be modeled as a cooperative game. We proved that there exists a unique Nash bargaining solution (NBS) for the game by verifying that the game is indeed a bargaining problem. Thus, the two objectives, i.e., system efficiency and user fairness specified in the selfish networks can be achieved. Simulation results show that the NBS scheme is efficient in that the performance loss of the NBS scheme to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair. The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.

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References

  1. Sendonaris A, Erkip E, Aazhang B. User cooperation diversity-part I: System description. IEEE Trans Commun, 2003, 51: 1927–1938

    Article  Google Scholar 

  2. Laneman J N, Tse D N C, Wornell G W. Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Trans Inf Theory, 2004: 3062–3080

  3. Beres E, Adve R. Selection cooperation in multi-source cooperative networks. IEEE Trans Wireless Commun, 2008, 7: 118–127

    Article  Google Scholar 

  4. Zhao B, Valenti M C. Practical relay networks: A generalization of hybrid-ARQ. IEEE J Sel Areas Commun, 2005, 23: 7–18

    Article  Google Scholar 

  5. Luo J, Blum R S, Greenstein L J, et al. New approaches for cooperative use of multiple antennas in ad hoc wireless networks. In: Proceedings of IEEE 60th Vehicular Technology Conference, Los Angeles, 2004. 2769–2773

  6. Himsoon T, Siriwongpairat W P, Han Z, et al. Lifetime maximization framework by cooperative nodes and relay deployment in wireless networks. IEEE J Sel Areas Commun, 2007, 25: 306–317

    Article  Google Scholar 

  7. Cui S G, Goldsmith A J, Bahai A. Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE J Sel Areas Commun, 2004, 22: 1089–1098

    Article  Google Scholar 

  8. Wang B B, Han Z, Liu K J R. Distributed relay selection and power control for multiuser cooperative communication networks using Stackelberg game. IEEE Trans Mobile Comput, 2009, 8: 975–990

    Article  Google Scholar 

  9. Huang J W, Han Z, Liu K J R, et al. Auction-based resource allocation for cooperative communications. IEEE J Sel Areas Commun, 2008, 26: 1226–1237

    Article  Google Scholar 

  10. Zhang G P, Zhang H L, Zhao L Q, et al. Fair resource sharing for cooperative relay networks using Nash bargaining solutions. IEEE Commun Lett, 2009, 13: 381–383

    Article  Google Scholar 

  11. Zhang Z Y, Shi J, Chen H H, et al. A cooperation strategy based on Nash bargaining solution in cooperative relay networks. IEEE Trans Vehicular Technol, 2008, 57: 2570–2577

    Article  Google Scholar 

  12. Zhang G P, Yang K, Ding E J. Power allocation scheme for selfish cooperative communications based on game theory and particle swarm optimizer. Sci China Inf Sci, 2010, 53: 1908–1912

    Article  MathSciNet  Google Scholar 

  13. Zhang G P, Cong L, Zhao L Q, et al. Competitive resource sharing based on game theory in cooperative relay networks. ETRI J, 2009, 31: 89–91

    Article  Google Scholar 

  14. Han Z, Himsoon T, Siriwongpairat W P, et al. Resource allocation for multiuser cooperative OFDM networks: Who helps whom and how to cooperate. IEEE Trans Vehicular Technol, 2009, 58: 2378–2391

    Article  Google Scholar 

  15. Ibrahim A S, Sadek A K, Su W F, et al. Cooperative communications with relay-selection: when to cooperate and whom to cooperate with?. IEEE Trans Wireless Commun, 2008, 7: 2814–2827

    Article  Google Scholar 

  16. V. Krishna. Auction theory. London: Academic Press, 2002

    Google Scholar 

  17. Fudenberg D, Tirole J. Game theory. Cambridge: MIT Press, 1991

    Google Scholar 

  18. Yaiche H, Mazumdar R R, Rosenberg C. A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM Trans Networking, 2000, 8: 667–678

    Article  Google Scholar 

  19. Wang J, Chen J. Performance of wideband CDMA systems with complex spreading and imperfect channel estimation. IEEE J Sel Areas Commun, 2001, 19: 152–163

    Article  Google Scholar 

  20. Wang J Z, Milstein L B. CDMA overlay situations for microcellular mobile communications. IEEE Trans Commun, 1995, 43: 603–614

    Article  Google Scholar 

  21. Cao X R. Preference functions and bargaining solutions. In: Proceedings of IEEE Conference on Decision and Control, Orlando, 1982. 164–171

  22. Han Z, Ji Z, Liu K J R. Fair multiuser channel allocation for OFDMA networks using Nash bargaining solutions and coalitions. IEEE Trans Commun, 2005, 53: 1366–1376

    Article  Google Scholar 

  23. Lee K D, Leung V C M. Fair allocation of subcarrier and power in an OFDMA wireless mesh network. IEEE J Sel Areas Commun, 2006, 24: 2051–2063

    Article  Google Scholar 

  24. Shi Y, Eberhart R. A modified particle swarm optimizer. In: Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, 1998. 69–73

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Authors and Affiliations

  1. CUMT-IoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou, 221116, China

    EnJie Ding, GuoPeng Zhang & Peng Liu

  2. School of Computer Science and Electronic Engineering, University of Essex Wivenhoe Park, Colchester Essex, CO4 3SQ, UK

    Kun Yang

Authors
  1. EnJie Ding
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  2. GuoPeng Zhang
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  3. Peng Liu
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  4. Kun Yang
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Correspondence to GuoPeng Zhang.

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Ding, E., Zhang, G., Liu, P. et al. Energy-efficient power allocation for selfish cooperative communication networks using bargaining game. Sci. China Inf. Sci. 55, 795–804 (2012). https://doi.org/10.1007/s11432-011-4355-z

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  • DOI: https://doi.org/10.1007/s11432-011-4355-z

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