Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
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Base stations experiencing hardware or software failures have negative impact on network performance and customer satisfaction. The timely detection of such so-called outage or sleeping cells can be a difficult and costly task, depending on the type of the error. As a first step towards self-healing capabilities of mobile communication networks, operators have formulated a need for an automated cell outage detection. This paper presents and evaluates a novel cell outage detection algorithm, which is based on the neighbor cell list reporting of mobile terminals. Using statistical classification techniques as well as a manually designed heuristic, the algorithm is able to detect most of the outage situations in our simulations.