Wireless Internet. 6th International ICST Conference, WICON 2011, Xi’an, China, October 19-21, 2011, Revised Selected Papers

Research Article

Distributed Fast Convergent Power Allocation Algorithm in Underlay Cognitive Radio Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-30493-4_34,
        author={Yanan Mei and Yanhui Lu and Xiaomin Mu and Xin Liu},
        title={Distributed Fast Convergent Power Allocation Algorithm in Underlay Cognitive Radio Networks},
        proceedings={Wireless Internet. 6th International ICST Conference, WICON 2011, Xi’an, China, October 19-21, 2011, Revised Selected Papers},
        proceedings_a={WICON},
        year={2012},
        month={10},
        keywords={Cognitive Radio power allocation competitive equilibrium Nash equilibrium distributed algorithm},
        doi={10.1007/978-3-642-30493-4_34}
    }
    
  • Yanan Mei
    Yanhui Lu
    Xiaomin Mu
    Xin Liu
    Year: 2012
    Distributed Fast Convergent Power Allocation Algorithm in Underlay Cognitive Radio Networks
    WICON
    Springer
    DOI: 10.1007/978-3-642-30493-4_34
Yanan Mei1, Yanhui Lu, Xiaomin Mu1, Xin Liu2
  • 1: Zhengzhou University
  • 2: University of California

Abstract

In underlay cognitive radio networks, secondary users can share the same frequency band with primary users under the condition of meeting interference temperature constraint. In order to improve their spectrum efficiency, we consider a market competitive equilibrium (CE) model to formulate the multi-channel power allocation problem. In this paper, we prove the existence and uniqueness of CE. We simplify the CE to Nash equilibrium (NE) first, which exists and is unique under weak-interference conditions, for the fixed price; we then prove that the prices converge to the equilibrium price and present the sufficient condition of unique CE solution. Furthermore, we propose a distributed fast convergent power allocation algorithm (FCPAA) with round robin rules. The simulation results show that FCPAA can satisfy the interference temperature constraint perfectly and converge faster than the one in literature [9].