A Fuzzy Neural JRRM in a Heterogeneous Scenario Supported by Prediction Strategies for Horizontal and Vertical Handovers

In this paper it will be shown how the fuzzy neural methodology can be used to develop an innovative mechanism to perform joint radio resource management (JRRM) in the context of heterogeneous radio access networks (RANs). In particular, an algorithm able to ensure certain quality of service (QoS) constraints in a multi-cell scenario deployment with three different radio access technologies (RATs), namely WLAN (wireless local area network), UMTS (universal mobile telecommunications system) and GERAN (GSM EDGE radio access network), is discussed. In addition, particular attention is paid to the design of an approach capable of managing handoff calls in a heterogeneous network. It is based on predicting future JRRM decisions. A RLS (recursive least square) predictor has been selected to provide reliable and accurate estimations of handoff calls and handoff call droppings. Performance improvements in terms of new connection blocking and handoff call dropping probabilities are presented.