Optimization of user behavior based handover using fuzzy Q-learning for LTE networks

In LTE networks, handover optimization is necessary to enhance the users’ satisfaction. Specifically, users using real time traffic need to experience continuous connectivity. Hence, radio link failures (RLFs) severely affect their quality of experience. Decreasing the RLFs for non-real time users is not as urgent as the case of real time users. On the other hand, a total network collapse can happen in case of too much unnecessary handovers (ping-pongs). In this work, fuzzy Q-learning is used to optimize the two contradictory handover problems, which are RLFs and ping-pongs. The former needs to decrease Handover Margin (HOM) to reduce the too late handover, and the latter needs to increase the HOM to reduce the unnecessary signaling. In the developed algorithm, the users in the network are divided into four categories, according to their speed and the data traffic used. This increases the satisfaction of some users, while keeping the overall handover problems within acceptable limits. For each category of users, fuzzy Q-learning is applied with a different initial candidate fuzzy actions. The proposed technique shows the best performance for each category of users in terms of the most preferred metric, either decreasing RLF or decreasing ping-pongs, for this category of users in comparison with two other literature techniques, or without using any optimization technique. Moreover, the algorithm is robust against changes in the number of users in the system, as it maintains the best solution when the number of users is halved or even doubled.