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Based on interference temperature model, the problem of secondary spectrum sharing can be formulated as a power optimization problem at physical layer. In this paper, we consider decentralized cognitive radio networks, and we especially focus on the spectrum sharing scenario where multiple measurement points are located in the licensed system. Game theory is used to investigate the distributed power control for providing the maximum throughput in cognitive radio networks. There are two aspects that should be considered in the design of the payoff function for each player, one is counteracting negative externalities in cognitive radio networks, and the other is satisfying all the interference temperature limits from measurement points. A tax-based power control game algorithm is introduced to implement power allocation optimization in a distributed mode, and guarantees the convergence to globally optimal power allocations.