  Knowing a user's next cell allows more efficient resource allocation and
enables new location-aware services. To anticipate the cell a user will
hand-over to, we introduce a new machine learning based prediction system.
Therein, we formulate the prediction as a classification problem based on
information that is readily available in cellular networks. Using only Channel
State Information (CSI) and handover history, we perform classification by
embedding Support Vector Machines (SVMs) into an efficient pre-processing
structure. Simulation results from a Manhattan Grid scenario and from a
realistic radio map of downtown Frankfurt show that our system provides timely
prediction at high accuracy.
