2nd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Price-Based Spectrum Management in Cognitive Radio Networks

  • @INPROCEEDINGS{10.1109/CROWNCOM.2007.4549775,
        author={Fan Wang and Marwan Krunz and Shuguang Cui},
        title={Price-Based Spectrum Management in Cognitive Radio Networks},
        proceedings={2nd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        keywords={Ad hoc networks  Chromium  Cognitive radio  Interference channels  Iterative algorithms  Nash equilibrium  Pricing  Radio spectrum management  Sliding mode control  System performance},
  • Fan Wang
    Marwan Krunz
    Shuguang Cui
    Year: 2008
    Price-Based Spectrum Management in Cognitive Radio Networks
    DOI: 10.1109/CROWNCOM.2007.4549775
Fan Wang1,*, Marwan Krunz1,*, Shuguang Cui1,*
  • 1: Department of Electrical & Computer Engineering University of Arizona Tucson, AZ 85721
*Contact email: wangfan@ece.arizona.edu, krunz@ece.arizona.edu, cui@ece.arizona.edu


A key challenge in operating cognitive radios (CRs) in a self-organizing (ad hoc) network is how to adaptively and efficiently allocate transmission powers and spectrum among CRs according to the surrounding environment. Most previous works address this issue via heuristic approaches or using centralized solutions. In this paper, we present a novel joint power/channel allocation scheme that uses a distributed pricing strategy to improve the network’s performance. In this scheme, the spectrum allocation problem is modelled as a non-cooperative game. A price-based iterative water-filling (PIWF) algorithm is proposed, which allows users to converge to the Nash Equilibrium (NE). This PIWF algorithm can be implemented distributively with CRs repeatedly negotiating their best transmission powers and spectrum. Simulation results show that the social optimality of the NE solution is dramatically improved with our price-based strategy. Based on the orders by which CRs take actions, we study sequential and parallel versions of the algorithm. We show that the parallel version converges faster than the sequential version.