Third generationUmts network has come with significant high-quality services that considerably increase the complexity of its management. Autonomic management has been introduced to alleviate these complex lengthy tasks. In this paper, we propose an autonomic management of macro-diversity algorithm inUmts networks. The new approach allows to dynamically adapt macro-diversity parameters to varying network situations. The online adaptation of these parameters is made by an intelligent controller calledfuzzy-Q-Learning. The combination of Fuzzy Inference System (Fis) and Q-learning algorithm allows to determine the best on-line parameterization of base stations and to deal with large number of continuous states and actions. The proposed scheme improves the system capacity up to 30% compared to a classical network with fixed parameters, balances the load between base stations and minimizes human interventions in the network management. However, the reactivity of the controller should be chosen with a special care since it impacts the frequency of active set updates and hence signalling messages in the radio interface as well as in the core network.
['Ridha Nasri', 'Zwi Altman', 'Hervé Dubreil']
Fuzzy-Q-learning-based autonomic management of macro-diversity algorithm in UMTS networks