A self-adaptive handoff decision algorithm for densely deployed closed-group femtocell networks

Due to the high traffic demand in cellular networks, femtocells are considered as one promising solution for providing cellular traffic offloading and better indoor coverage. However, coexistence of femtocells with macrocell networks introduces special challenges to mobility management. In particular, since indoor and unplanned deployment of femtocells usually suffers abrupt signal drop due to mutipath propagation, wall penetration loss, and shadowing, unnecessary handoffs and ping-pong effects may happen frequently, which severely degrades the quality of connections and user experience. On the other hand, offloading in femtocells requires a high cell utilization. Therefore, handoff decision algorithms should be carefully designed to trigger proper handoffs and fulfill the different requirements of macro-to-femto and femto-to-macro handoffs. In this paper, we propose a location history based adaptive handoff decision algorithm to address the special challenges of indoor and unplanned deployment of femtocells. Our proposed algorithm uses the neighboring cell list in dense femtocell networks to obtain the location of users. Based on the user location history, a new concept, handoff frequency of occurrence, is introduced to assist intelligent handoff decision-making. The hysteresis margin in our proposed handoff decision criteria can be adaptively adjusted to meet various handoff requirements. Simulation results show that our proposed location history based adaptive handoff decision algorithm can significantly improve the femtocell utilization and handoff failure rate. To the best of our knowledge, this is the first adaptive handoff decision algorithm that considers specific challenges of indoor deployment of femtocells.