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As the scarce spectrum resource is becoming overcrowded, cognitive wireless mesh networks (CogMesh) indicate great flexibility to improve the spectrum utilization by opportunistically accessing the authorized frequency bands. In this paper, the authors consider non-cooperative green power assignment in CogMesh with the consideration of energy efficiency. The problem is modeled as a stochastic learning process. They extend the single-agent Q-learning to a multi-user context, and propose a conjecture based multi-agent Q-learning scheme to obtain the optimal strategies with private and incomplete information. A learning secondary user performs Q-function updates based on the conjecture about other secondary users' behaviors.
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