There is substantial literature covering both problems and solutions related to the operation of Long Term Evolution (LTE) networks in unlicensed spectrum (LTE-U) while in coexistence with other technologies, such as Wi-Fi. However, a seldom explored scenario is the coexistence between multiple LTE-U networks. Within this scenario, a big issue is establishing optimal configurations that take into account fairness among different operators coexisting in the same unlicensed spectrum coverage

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... Solutions to this problem could, for instance, react to changes in the environment by "tuning" different system configurations. We propose a game theoretical reinforcement learning algorithm, called GTDM-CSAT, aiming to maximize the LTE-U aggregated throughput while keeping channel access fairness among different access points. GTDM-CSAT uses the relative data rate offered by the system to adapt the LTE-U ON-OFF time. For this, we formulate the problem as a Markovian game, where the LTE-U operators coexist on a two-zero-sum game. The solution for the best ON-OFF time ratio is defined by applying a modified Minimax Q-learning algorithm for finding the game equilibrium. We perform simulations following 3GPP specifications using the ns-3 simulator for evaluating GTDM-CSAT under different traffic load scenarios. Results indicate gains in the system aggregated throughput by improving the individual data rate of each operator.