The rapid advancement of smartphones has instigated tremendous data applications for cell phones. Supporting simultaneous voice and data services in a cellular network is not only desirable but also becoming indispensable. However, if the voice and data are serviced through the same antenna (like the 3G UMTS network), a voice call with data sessions requires better radio connection than a voice-only call. In this paper, we systematically study the coordination between the voice and data transmissions in UMTS networks. From analyzing a large carrier's UMTS network recording data, we first identify the most relevant network measurements/features indicating a potential call drop, then propose a drop-call predictor based on AdaBoost. Moreover, we develop an intelligent call management strategy to voluntarily block data sessions when the voice is predicted to be dropped. Our analysis utilizing real service provider's data sets shows that our proposed scheme can not only predict drop calls with a very high accuracy but also achieve the highest user satisfaction compared to the other existing call management strategies.

We study the problem of scalable monitoring of operational 3G wireless networks. Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. A fine-grained threshold setting (e.g., perlocation hourly) incurs prohibitively high management complexity, while a single static threshold fails to capture the network dynamics, thus resulting in unacceptably poor alarm quality (up to 70% false/miss alarm rates). In this paper, we propose a scalable monitoring solution, called threshold-compression that can characterize the location- and time-specific threshold trend of each individual network element (NE) with minimal threshold setting. The main insight is to identify groups of NEs with similar threshold behaviors across location and time dimensions, forming spatial-temporal clusters to reduce the number of thresholds while maintaining acceptable alarm accuracy in a large-scale 3G network. Our evaluations based on the operational experience on a commercial 3G network have demonstrated the effectiveness of the proposed solution. We are able to reduce the threshold setting up to 90% with less than 10% false/miss alarms.

Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. There exists a fundamental tradeoff between the size of threshold settings and the alarm quality. In this paper, we propose a scalable monitoring solution, called threshold-compression that characterizes the tradeoff via intelligent threshold aggregation. The main insight behind our solution is to identify groups of network elements with similar threshold behaviors across location and time dimensions, thus forming spatial-temporal clusters and generating the associated compressed thresholds within the optimization framework. Our evaluations on a commercial 3G network have demonstrated the effectiveness of our threshold-compression solution, e.g., threshold setting reduction up to 90% within 10% false/miss alarms.