Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks

This paper presents call admission control and bandwidth reservation schemes in wireless cellular networks that have been developed based on assumptions more realistic than existing proposals. In order to guarantee the handoff dropping probability, we propose to statistically predict user mobility based on the mobility history of users. Our mobility prediction scheme is motivated by computational learning theory, which has shown that prediction is synonymous with data compression. We derive our mobility prediction scheme from data compression techniques that are both theoretically optimal and good in practice. In order to utilize resource more efficiently, we predict not only the cell to which the mobile will handoff but also when the handoff will occur. Based on the mobility prediction, bandwidth is reserved to guarantee some target handoff dropping probability. We also adaptively control the admission threshold to achieve a better balance between guaranteeing handoff dropping probability and maximizing resource utilization. Simulation results show that the proposed schemes meet our design goals and outperforms the static-reservation scheme and cell-reservation scheme