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