Optimization of load balancing using fuzzy Q-Learning for next generation wireless networks

Load balancing is considered by the 3GPP as an important issue in Self-Organizing Networks due to its effectiveness to increase network capacity. In next generation wireless networks, load balancing can be easily implemented by tuning handover (HO) margins, achieving a decrease in call blocking. However, call dropping can be increased as a negative effect of the HO-based load balancing, because users usually are handed over to cells where the radio conditions are worse. In this work, a Fuzzy Logic Controller (FLC) optimized by the fuzzy Q-Learning algorithm is proposed for the load balancing problem, with the aim of decreasing call blocking in congested cells, while at the same time restricting call dropping in neighboring cells according to the network policy. In particular, two different approaches for the FLC optimization are evaluated in this work, highlighting that one of the proposed methods allows to accurately preserve the call quality constraint during the load balancing, while the other can adapt to network variations. Results show that the optimized FLC provides a notable reduction in call blocking while preserving call dropping under the operator constraint.

► We model a new approach for load balancing which allows to preserve call quality.
► With pure exploration, the optimized controller accurately preserves call quality.
► Combining both exploration and exploitation enhances the speed of convergence.
► The Q-Learning algorithm effectively adapts the controller to context variations.
► The proposed algorithm can highly decrease call blocking in large urban scenarios.