Recent evolutions in mobile networks have led to increased resource demands of applications such as voip, web browsing and video conferences. Data rates of such applications can’t be supported by traditional cellular network. Latest technologies such as LTE have an important role to obtain high data rate and fourth generation users’ demands. In the meantime, femtocell networks are one of the solutions that support high data rates as well as better indoor coverage needless of any improvements in the network architecture. However, femtocell networks have some interference challenge mainly due to dense and random deployment of femto base stations. It should be noted that in some previous algorithms of resource management of macrocell networks, admission control techniques are used to ensure user quality of service and determine the cell resource requirements. Doing so, attempts have been made to ensure the call dropping probability on an acceptable level while reaching a higher call blocking probability and also a higher spectral efficiency. Moreover, in some cases, mobility characteristics of mobile users are predicted and resource reservation has been made with more accurate information to improve the mentioned parameters. However, in the previous resource management algorithms of femtocell networks, there is no attention on mobility information of users and its impact on efficient resource management. In this thesis, we have tried to design an algorithm to accurately determine resource requirement of femto base stations. This goal is achieved by utilizing mobility prediction. Then, resources are allocated more efficiently to the femto base stations using a resource management algorithm which is based on the graph method. Also, by prioritizing hand-off calls to new calls we have ensured the call dropping probability on an acceptable level. The proposed method has been shown to reach a higher call dropping probability and spectral efficiency compared to the benchmark algorithms. Keywords: femtocell networks, resource management, mobility prediction