Policy-based self-healing for radio access networks

Various centralized, distributed or cooperative management systems have been proposed to address the demands of wireless telecommunication networks. However, considering the size, complexity and heterogeneity that those networks will have in the future, current solutions either do not scale properly, or have no support for automation, or lack of the flexibility and simple control that operators will need for managing future networks in a cost-effective way. To address this problem, we designed Omega, a distributed and policy-based network management system that uses rich knowledge-modeling techniques to develop self-configuration capabilities. Omega also implements a novel conflict-resolution method that uses high-level goals and machine learning techniques to optimize its policy-based decisions. Using simulations, in this paper we show how omega reduces the impact of a node crash on the overall availability of a radio access network by optimizing the lists of neighboring cells of the nodes in the vicinity.