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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. Microeconomic concepts are included in the proposed approach so that user profile differentiation can be considered when making a JRRM decision. On the other hand, interoperator JRRM enables subscribers to obtain service through other operators, if the home operator network is blocked. Simulation results in a number of different scenarios show that interoperator agreements established in a cooperative scenario benefit both the operators and users, which enables efficient load management and increased operator revenue.