Dynamic traffic steering based on fuzzy Q-Learning approach in a multi-RAT multi-layer wireless network

The infrastructure of current cellular networks must evolve to cope with the increasing demand for mobile-broadband services. Heterogeneous networks are an attractive solution for operators to expand network capacity, based on deploying different Radio Access Technologies, cell sizes and carrier frequencies in the same environment. As a result, operators gain flexibility to distribute traffic across the different networks (or layers) in order to make a more efficient use of resources and enhance network performance. In this work, a dynamic traffic steering technique in multi-RAT multi-layer wireless networks is proposed. In particular, a fuzzy rule-based reinforcement learning algorithm modifies handover parameters according to a specific policy set by the operator, which typically searches for a trade-off between key performance indicators. Results show that the proposed optimization algorithm provides good flexibility to support different policies by simply adjusting some weighting factors. In addition, the Q-Learning algorithm is shown as an effective solution to adapt the network to context variations, such as those produced in the user spatial distribution.