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WiGig networks and 60 GHz frequency communications have a lot of potentialfor commercial and personal use. They can offer extremely high transmissionrates but at the cost of low range and penetration. Due to these issues, WiGigsystems are unstable and need to rely on frequent handovers to maintainhigh-quality connections. However, this solution is problematic as it forcesusers into bad connections and downtime before they are switched to a betteraccess point. In this work, we use Machine Learning to identify patterns inuser behaviors and predict user actions. This prediction is used to doproactive handovers, switching users to access points with better futuretransmission rates and a more stable environment based on the future state ofthe user. Results show that not only the proposal is effective at predictingchannel data, but the use of such predictions improves system performance andavoids unnecessary handovers.

Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems