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Abstract
Cognitive Autonomous Networks (CAN) promise to advance Self Organizing Networks (SON) by applying artificial intelligence to significantly raise the degree of automation in mobile networks. In CAN, Cognitive Functions (CFs) learn the optimal configuration parameter values to optimize specific network metrics, with the execution coordinated via a controller. In open, multi-vendor systems however, the CF’s learning ability may raise a new risk: a manipulative CF (MCF) may learn not only its objective, but also to manipulate the coordination system in pursuit of that objective. In this paper we propose and evaluate our proposed functionality, called CoDeRa, that neutralizes manipulative CF behavior. However, although CoDeRa is effective against MCFs, it is inadequate to resolving error propagation in CF coordination, caused by corrupted network data, for which we have proposed an alternate simpler and cost efficient network management architecture. Our evaluation shows that the proposed design is robust against the observed concerns, and in context of ongoing worldwide standardization efforts, we summarize the relevance and implications of our proposed architecture.
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