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Knowing a user's next cell allows more efficient resource allocation andenables new location-aware services. To anticipate the cell a user willhand-over to, we introduce a new machine learning based prediction system.Therein, we formulate the prediction as a classification problem based oninformation that is readily available in cellular networks. Using only ChannelState Information (CSI) and handover history, we perform classification byembedding Support Vector Machines (SVMs) into an efficient pre-processingstructure. Simulation results from a Manhattan Grid scenario and from arealistic radio map of downtown Frankfurt show that our system provides timelyprediction at high accuracy.

Predicting a User's Next Cell With Supervised Learning Based on Channel States