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

Abstract : 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.
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Submitted on : Monday, May 13, 2013 - 3:30:46 PM
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Xu Chen, François Mériaux, Stefan Valentin. Predicting a User's Next Cell With Supervised Learning Based on Channel States. IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2013), Jun 2013, Darmstadt, Germany. pp.1-5, ⟨10.1109/spawc.2013.6612007 ⟩. ⟨hal-00821207⟩

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