Existing approaches to network management and self-organization are inadequate to cope with the growth of autonomous network elements, and with partial and uncertain network knowledge. Our research efforts aim at redesigning wireless network management mechanisms in order to eliminate, or at least mitigate the deficiencies of existing solutions. Moreover, infrastructural issues have to be detected before they have impact on the quality-of-service in the network.
A thorough understanding of the complex interconnections in current and future communication networks is tantamount to enabling smart optimization decisions, to enable future networks to better utilize scarce wireless resources, and to improve the quality-of-service (QoS) experienced by users. Modern communication networks can be seen as big, evolving distributed databases full of context and information available from handheld devices (e.g., mobility information), the network itself (e.g., a base station's current load), and the environment (e.g., predicted user trajectories). We develop and incorporate novel knowledge extraction mechanisms and predictive analytics means over extremely large volumes of data to enhance decision-making processes throughout the network. This knowledge has a plethora of applications, including real-time system identification and network-wide self-optimization of wireless networked entities. In particular, the extracted information can be used for demand modelling, anticipatory buffering, anticipatory handover, anticipatory traffic offloading, inter-cell scheduling, and many more.