Optimized Q-learning for WiFi Offloading in Dense Cellular Networks

What is it about?

WiFi traffic offloading is becoming especially appealing because of the upcoming ultra-dense cellular networks. However, WiFi offloading decision as well as WiFi-Access Points selection should be carefully studied.

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Why is it important?

A distributed Q-learning algorithm is proposed in which each cellular user learns about his local environment and selects the best base station after reaching convergence. With Q-learning scheme, each user decides to join the WiFi offloading or not, depending on the received reward from his environment and from his previous learning.