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In this article, we address the problem of auto-tuning of soft handover (SHO) parameters in WCDMA networks. The auto-tuning process uses a fuzzy Q-learning controller to adapt SHO parameters to varying network situations such as traffic fluctuation. The fuzzy Q-learning controller combines both fuzzy logic theory and reinforcement learning method. The cooperation of these two mechanisms simplifies the task of the online optimization of fuzzy logic rules and consequently leads to a better online SHO parameterization of each base station in the network. The proposed scheme improves the system capacity compared to a classical network with fixed parameters, balances the load between base stations and minimizes human intervention in network management and optimization tasks.

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