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Joint Radio Resource Management (JRRM) is the envisaged process aimed at optimizing the radio resource usage of wireless systems to satisfy the requirements of both the network operators and the users in the context of future generation wireless networks. In particular, this paper proposes a two-layered JRRM framework to improve the efficiency of multiradio and multioperator cellular scenarios. On the one hand, the intraoperator JRRM relies on fuzzy neural mechanisms with economic-driven reinforcement learning techniques to exploit radio resources within a single-operator domain.
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