Optimization of Handover Parameters for Traffic Sharing in GERAN
Wireless Personal Communications: An International Journal
Challenges in mobile network operation: Towards self-optimizing networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Fuzzy Q-Learning Admission Control for WCDMA/WLAN Heterogeneous Networks with Multimedia Traffic
IEEE Transactions on Mobile Computing
LTE, the radio technology path towards 4G
Computer Communications
Expert Systems with Applications: An International Journal
Optimal Traffic Sharing in GERAN
Wireless Personal Communications: An International Journal
A dynamic channel assignment policy through Q-learning
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
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.