Cluster-Based Resource Allocation for Spectrum-Sharing Femtocell Networks

Abstract

Femtocells in two-tier femto-macro networks can enhance indoor coverage and improve overall network performance. Macro networks may share spectrum with overlaid femtocells so as to improve spectral efficiency. However, the deployment of femtocells also brings co-tier and cross-tier interferences, which will significantly degrade system performance. In order to solve this problem efficiently, we propose a distributed scheme to manage wireless resources in this heterogeneous networks. The feasible solution can be obtained by dividing the problem into two sub-problems. First, we propose a femtocells clustering scheme, which uses a mathematical modeling idea based on LINGO, an optimization software that can solve the joint clustering problem for the femtocell access points (FAPs). The proposed branch-and-bound algorithm and the simplex algorithm are used jointly to find the optimal solution by LINGO. The optimality of the proposed clustering algorithm is verified both theoretically and through simulations where the comparison with other algorithms is made. Second, a novel algorithm is proposed to allocate sub-channels to the femtocell users (FUEs). Compared with other related schemes, the proposed channel-allocation algorithm can reduce the interference more effectively and achieve higher data-rate fairness among FUEs. Specifically, according to the situation that the FUEs move in the room, the FUE mobility model is proposed to predict the change tendency of path loss values of the FUEs, which can guarantee the mobile service quality and improve system capacity effectively. Finally, the power of the FAPs is adjusted dynamically through setting the interference threshold to further improve the performance of the system.