Reinforcement Learning Enhanced Iterative Power Allocation in Stochastic Cognitive Wireless Mesh Networks

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As the scarce spectrum resource is becoming overcrowded, cognitive wireless mesh networks have great flexibility to improve the spectrum utilization by opportunistically accessing the licensed frequency bands. One of the critical challenges for realizing such network is how to adaptively allocate transmit powers and frequency resources among Secondary Users (SUs) of the licensed frequency bands while maintaining the Quality-of-Service (QoS) requirement of the Primary Users (PUs). In this paper, the authors consider the power control problem in the context of cognitive wireless mesh networks formed by a number of clusters under the total transmit power constraint by each SU as well as the Mean-Squared Error (MSE) constraint by PUs. The problem is modeled as a non-cooperative game.